# papers

title: Hiding Images in Diffusion Models by Editing Learned Score Functions
authors: Haoyu Chen, Yunqiao Yang, Nan Zhong, Kede Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18663-18673
raw_affiliation: [{"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "Hong Kong"}]
affiliations: ["City University of Hong Kong"]
cities: ["Hong Kong"]
countries: ["Hong Kong"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Hiding_Images_in_Diffusion_Models_by_Editing_Learned_Score_Functions_CVPR_2025_paper.html

abstract:
Hiding data using neural networks (i.e., neural steganography) has achieved remarkable success across both discriminative classifiers and generative adversarial networks. However, the potential of data hiding in diffusion models remains relatively unexplored. Current methods exhibit limitations in achieving high extraction accuracy, model fidelity, and hiding efficiency due primarily to the entanglement of the hiding and extraction processes with multiple denoising diffusion steps. To address these, we describe a simple yet effective approach that embeds images at specific timesteps in the reverse diffusion process by editing the learned score functions. Additionally, we introduce a parameter-efficient fine-tuning method that combines gradient-based parameter selection with low-rank adaptation to enhance model fidelity and hiding efficiency. Comprehensive experiments demonstrate that our method extracts high-quality images at human-indistinguishable levels, replicates the original model behaviors at both sample and population levels, and embeds images orders of magnitude faster than prior methods. Besides, our method naturally supports multi-recipient scenarios through independent extraction channels.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Hiding_Images_in_Diffusion_Models_by_Editing_Learned_Score_Functions_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Hiding_Images_in_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Haoyu and Yang, Yunqiao and Zhong, Nan and Ma, Kede},
    title     = {Hiding Images in Diffusion Models by Editing Learned Score Functions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18663-18673}
}


title: Disco4D: Disentangled 4D Human Generation and Animation from a Single Image
authors: Hui En Pang, Shuai Liu, Zhongang Cai, Lei Yang, Tianwei Zhang, Ziwei Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26331-26344
affiliations: ["S-Lab, Nanyang Technological University", "SenseTime Research", "Shanghai AI Laboratory"]
cities: ["Singapore", "Shanghai", "Shanghai"]
countries: ["Singapore", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pang_Disco4D_Disentangled_4D_Human_Generation_and_Animation_from_a_Single_CVPR_2025_paper.html

abstract:
We present Disco4D, a novel Gaussian Splatting framework for 4D human generation and animation from a single image. Different from existing methods, Disco4D distinctively disentangles clothings (with Gaussian models) from the human body (with SMPL-X model), significantly enhancing the generation details and flexibility. It has the following technical innovations. (1) Disco4D learns to efficiently fit the clothing Gaussians over the SMPL-X Gaussians. (2) It adopts diffusion models to enhance the 3D generation process, e.g. modeling occluded parts not visible in the input image. (3) It learns an identity encoding for each clothing Gaussian to facilitate the separation and extraction of clothing assets. Furthermore, Disco4D naturally supports 4D human animation with vivid dynamics. Extensive experiments demonstrate the superiority of Disco4D on 4D human generation and animation tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pang_Disco4D_Disentangled_4D_Human_Generation_and_Animation_from_a_Single_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pang_Disco4D_Disentangled_4D_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Pang_2025_CVPR,
    author    = {Pang, Hui En and Liu, Shuai and Cai, Zhongang and Yang, Lei and Zhang, Tianwei and Liu, Ziwei},
    title     = {Disco4D: Disentangled 4D Human Generation and Animation from a Single Image},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26331-26344}
}

raw_affiliation:
[{"affiliation": "S-Lab, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "SenseTime Research", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}]


title: DoraCycle: Domain-Oriented Adaptation of Unified Generative Model in Multimodal Cycles
authors: Rui Zhao, Weijia Mao, Mike Zheng Shou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2835-2846
affiliations: ["Show Lab, National University of Singapore"]
cities: ["Singapore"]
countries: ["Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_DoraCycle_Domain-Oriented_Adaptation_of_Unified_Generative_Model_in_Multimodal_Cycles_CVPR_2025_paper.html

abstract:
Adapting generative models to specific domains presents an effective solution for satisfying specialized requirements. However, adapting to some complex domains remains challenging, especially when these domains require substantial paired data to capture the targeted distributions. Since unpaired data from a single modality, such as vision or language, is more readily available, we utilize the bidirectional mappings between vision and language learned by the unified generative model to enable training on unpaired data for domain adaptation. Specifically, we propose DoraCycle, which integrates two multimodal cycles: text-to-image-to-text and image-to-text-to-image. The model is optimized through cross-entropy loss computed at the cycle endpoints, where both endpoints share the same modality. This facilitates self-evolution of the model without reliance on annotated text-image pairs. Experimental results demonstrate that for tasks independent of paired knowledge, such as stylization, DoraCycle can effectively adapt the unified model using only unpaired data. For tasks involving new paired knowledge, such as specific identities, a combination of a small set of paired image-text examples and larger-scale unpaired data is sufficient for effective domain-oriented adaptation. The code will be released at https://github.com/showlab/DoraCycle.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_DoraCycle_Domain-Oriented_Adaptation_of_Unified_Generative_Model_in_Multimodal_Cycles_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Rui and Mao, Weijia and Shou, Mike Zheng},
    title     = {DoraCycle: Domain-Oriented Adaptation of Unified Generative Model in Multimodal Cycles},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2835-2846}
}

raw_affiliation:
[{"affiliation": "Show Lab, National University of Singapore", "city": "Singapore", "country": "Singapore"}]


title: WeatherGen: A Unified Diverse Weather Generator for LiDAR Point Clouds via Spider Mamba Diffusion
authors: Yang Wu, Yun Zhu, Kaihua Zhang, Jianjun Qian, Jin Xie, Jian Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17019-17028
cities: ["Nanjing", "Nanjing", "Nanjing", "Suzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_WeatherGen_A_Unified_Diverse_Weather_Generator_for_LiDAR_Point_Clouds_CVPR_2025_paper.html

abstract:
3D scene perception demands a large amount of adverse-weather LiDAR data, yet the cost of LiDAR data collection presents a significant scaling-up challenge. To this end, a series of LiDAR simulators have been proposed. Yet, they can only simulate a single adverse weather with a single physical model, and the fidelity is quite limited. This paper presents **WeatherGen**, the first unified diverse-weather LiDAR data diffusion generation framework, significantly improving fidelity. Specifically, we first design a map-based data producer, which is capable of providing a vast amount of high-quality diverse-weather data for training purposes. Then, we utilize the diffusion-denoising paradigm to construct a diffusion model.  Among them, we propose a spider mamba generator with the spider mamba scan to restore the disturbed diverse weather data gradually. The spider mamba models the feature interactions by scanning the LiDAR beam circle and central ray, excellently maintaining the physical structure of the LiDAR point cloud. Subsequently, we design a latent domain aligner following the generator to transfer real-world knowledge. Afterward, we devise a contrastive learning-based controller, which equips weather control signals with compact semantic knowledge through language supervision from CLIP, guiding the diffusion model in generating more discriminative data. Finally, we fine-tune WeatherGen with small-scale real-world data to further enhance its performance. Extensive evaluations on KITTI-360 and Seeing Through Fog demonstrate the high generation quality of WeatherGen. Through WeatherGen, we construct the mini-weather dataset, promoting the performance of the downstream task under adverse weather conditions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_WeatherGen_A_Unified_Diverse_Weather_Generator_for_LiDAR_Point_Clouds_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_WeatherGen_A_Unified_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Yang and Zhu, Yun and Zhang, Kaihua and Qian, Jianjun and Xie, Jin and Yang, Jian},
    title     = {WeatherGen: A Unified Diverse Weather Generator for LiDAR Point Clouds via Spider Mamba Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17019-17028}
}

raw_affiliation:
[{"affiliation": "PCA Lab, Nanjing University of Science and Technology", "city": "Nanjing", "country": "China"}, {"affiliation": "School of Automation, Southeast University", "city": "Nanjing", "country": "China"}, {"affiliation": "State Key Laboratory for Novel Software Technology, Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "School of Intelligence Science and Technology, Nanjing University", "city": "Suzhou", "country": "China"}]

affiliations:
["PCA Lab, Nanjing University of Science and Technology", "School of Automation, Southeast University", "State Key Laboratory for Novel Software Technology, Nanjing University", "School of Intelligence Science and Technology, Nanjing University"]


title: MUST: The First Dataset and Unified Framework for Multispectral UAV Single Object Tracking
authors: Haolin Qin, Tingfa Xu, Tianhao Li, Zhenxiang Chen, Tao Feng, Jianan Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16882-16891
raw_affiliation: [{"affiliation": "Beijing Institute of Technology", "city": "Beijing", "country": "China"}]
affiliations: ["Beijing Institute of Technology"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qin_MUST_The_First_Dataset_and_Unified_Framework_for_Multispectral_UAV_CVPR_2025_paper.html

abstract:
UAV tracking faces significant challenges in real-world scenarios, such as small-size targets and occlusions, which limit the performance of RGB-based trackers. Multispectral images (MSI), which capture additional spectral information, offer a promising solution to these challenges. However, progress in this field has been hindered by the lack of relevant datasets. To address this gap, we introduce the first large-scale Multispectral UAV Single Object Tracking dataset (MUST), which includes 250 video sequences spanning diverse environments and challenges, providing a comprehensive data foundation for multispectral UAV tracking. We also propose a novel tracking framework, UNTrack, which encodes unified spectral, spatial, and temporal features from spectrum prompts, initial templates, and sequential searches. UNTrack employs an asymmetric transformer with a spectral background eliminate mechanism for optimal relationship modeling and an encoder that continuously updates the spectrum prompt to refine tracking, improving both accuracy and efficiency. Extensive experiments show that our proposed UNTrack outperforms state-of-the-art UAV trackers. We believe our dataset and framework will drive future research in this area. The dataset is available on https://github.com/q2479036243/MUST-Multispectral-UAV-Single-Object-Tracking.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qin_MUST_The_First_Dataset_and_Unified_Framework_for_Multispectral_UAV_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qin_MUST_The_First_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qin_2025_CVPR,
    author    = {Qin, Haolin and Xu, Tingfa and Li, Tianhao and Chen, Zhenxiang and Feng, Tao and Li, Jianan},
    title     = {MUST: The First Dataset and Unified Framework for Multispectral UAV Single Object Tracking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16882-16891}
}


title: IDOL: Instant Photorealistic 3D Human Creation from a Single Image
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26308-26319
cities: ["Nanjing", "Shenzhen", "Beijing", "Shenzhen", "Shenzhen"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhuang_IDOL_Instant_Photorealistic_3D_Human_Creation_from_a_Single_Image_CVPR_2025_paper.html

authors:
Yiyu Zhuang, Jiaxi Lv, Hao Wen, Qing Shuai, Ailing Zeng, Hao Zhu, Shifeng Chen, Yujiu Yang, Xun Cao, Wei Liu

abstract:
Creating a high-fidelity, animatable 3D full-body avatar from a single image is a challenging task due to the diverse appearance and poses of humans and the limited availability of high-quality training data. To achieve fast and high-quality human reconstruction, this work rethinks the task from the perspectives of dataset, model, and representation. First, we introduce a large-scale HUman GEnerated training dataset, HuGe100K, consisting of 100K diverse, photorealistic human images with corresponding 24-view in a static pose or dynamic pose frames generated via a pose-controllable image-to-video model. Next, leveraging the diversity in views, poses, and appearances within HuGe100K, we develop a scalable feed-forward transformer model to predict a 3D human Gaussian representation in a uniform space of a given human image. This model is trained to disentangle human pose, shape, clothing geometry, and texture. Accordingly, the estimated Gaussians can be animated robustly without post-processing. We conduct comprehensive experiments to validate the effectiveness of the proposed dataset and method. Our model demonstrates the generalizable ability to efficiently reconstruct photorealistic humans in under 1 second using a single GPU. Additionally, it seamlessly supports various applications, including animation, shape, and texture editing tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhuang_IDOL_Instant_Photorealistic_3D_Human_Creation_from_a_Single_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhuang_IDOL_Instant_Photorealistic_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhuang_2025_CVPR,
    author    = {Zhuang, Yiyu and Lv, Jiaxi and Wen, Hao and Shuai, Qing and Zeng, Ailing and Zhu, Hao and Chen, Shifeng and Yang, Yujiu and Cao, Xun and Liu, Wei},
    title     = {IDOL: Instant Photorealistic 3D Human Creation from a Single Image},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26308-26319}
}

raw_affiliation:
[{"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Tencent", "city": "Shenzhen", "country": "China"}, {"affiliation": "Shenzhen University of Advanced Technology", "city": "Shenzhen", "country": "China"}]

affiliations:
["Nanjing University", "Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences", "Tsinghua University", "Tencent", "Shenzhen University of Advanced Technology"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20695-20705
cities: ["Nanjing", "Amherst", "Shenzhen"]
countries: ["China", "United States", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xiao_Tightening_Robustness_Verification_of_MaxPool-based_Neural_Networks_via_Minimizing_the_CVPR_2025_paper.html

title:
Tightening Robustness Verification of MaxPool-based Neural Networks via Minimizing the Over-Approximation Zone

authors:
Yuan Xiao, Yuchen Chen, Shiqing Ma, Chunrong Fang, Tongtong Bai, Mingzheng Gu, Yuxin Cheng, Yanwei Chen, Zhenyu Chen

abstract:
The robustness of neural network classifiers is important in the safety-critical domain and can be quantified by robustness verification. At present, efficient and scalable verification techniques are always sound but incomplete, and thus, the improvement of verified robustness results is the key criterion to evaluate the performance of incomplete verification approaches. The multi-variate function MaxPool is widely adopted yet challenging to verify.    In this paper, we present Ti-Lin, a robustness verifier for MaxPool-based CNNs with Tight Linear Approximation. Following the sequel of minimizing the over-approximation zone of the non-linear function of CNNs, we are the first to propose the provably neuron-wise tightest linear bounds for the MaxPool function. By our proposed linear bounds, we can certify larger robustness results for CNNs.     We evaluate the effectiveness of Ti-Lin on different verification frameworks with open-sourced benchmarks, including LeNet, PointNet, and networks trained on the MNIST, CIFAR-10, Tiny ImageNet and ModelNet40 datasets. Experimental results show that Ti-Lin significantly outperforms the state-of-the-art methods across all networks with up to 78.6% improvement in terms of the certified accuracy with almost the same time consumption as the fastest tool.     Our code is available at https://anonymous.4open.science/r/Ti-Lin-cvpr-72EE.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiao_Tightening_Robustness_Verification_of_MaxPool-based_Neural_Networks_via_Minimizing_the_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiao_Tightening_Robustness_Verification_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xiao_2025_CVPR,
    author    = {Xiao, Yuan and Chen, Yuchen and Ma, Shiqing and Fang, Chunrong and Bai, Tongtong and Gu, Mingzheng and Cheng, Yuxin and Chen, Yanwei and Chen, Zhenyu},
    title     = {Tightening Robustness Verification of MaxPool-based Neural Networks via Minimizing the Over-Approximation Zone},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20695-20705}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory for Novel Software Technology, Nanjing University, China", "city": "Nanjing", "country": "China"}, {"affiliation": "University of Massachusetts Amherst, United States", "city": "Amherst", "country": "United States"}, {"affiliation": "Shenzhen Research Institute, Nanjing University, China", "city": "Shenzhen", "country": "China"}]

affiliations:
["State Key Laboratory for Novel Software Technology, Nanjing University, China", "University of Massachusetts Amherst, United States", "Shenzhen Research Institute, Nanjing University, China"]


title: SketchVideo: Sketch-based Video Generation and Editing
authors: Feng-Lin Liu, Hongbo Fu, Xintao Wang, Weicai Ye, Pengfei Wan, Di Zhang, Lin Gao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23379-23390
cities: ["Beijing", "Beijing", "Hong Kong", "Unknown"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_SketchVideo_Sketch-based_Video_Generation_and_Editing_CVPR_2025_paper.html

abstract:
Video generation and editing conditioned on text prompts or images have undergone significant advancements. However, challenges remain in accurately controlling global layout and geometry details solely by texts, and supporting motion control and local modification through images.  In this paper, we aim to achieve sketch-based spatial and motion control for video generation and support fine-grained editing of real or synthetic videos. Based on the DiT video generation model, we propose a memory-efficient control structure with sketch control blocks that predict residual features of skipped DiT blocks.  Sketches are drawn on one or two keyframes (at arbitrary time points) for easy interaction. To propagate such temporally sparse sketch conditions across all frames, we propose an inter-frame attention mechanism to analyze the relationship between the keyframes and each video frame. For sketch-based video editing, we design an additional video insertion module that maintains consistency between the newly edited content and the original video's spatial feature and dynamic motion. During inference, we use latent fusion for the accurate preservation of unedited regions. Extensive experiments demonstrate that our SketchVideo achieves superior performance in controllable video generation and editing.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_SketchVideo_Sketch-based_Video_Generation_and_Editing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_SketchVideo_Sketch-based_Video_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Feng-Lin and Fu, Hongbo and Wang, Xintao and Ye, Weicai and Wan, Pengfei and Zhang, Di and Gao, Lin},
    title     = {SketchVideo: Sketch-based Video Generation and Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23379-23390}
}

raw_affiliation:
[{"affiliation": "Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "Kuaishou Technology", "city": "Unknown", "country": "China"}]

affiliations:
["Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "Hong Kong University of Science and Technology", "Kuaishou Technology"]


title: PhysicsGen: Can Generative Models Learn from Images to Predict Complex Physical Relations?
authors: Martin Spitznagel, Jan Vaillant, Janis Keuper
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11125-11134
affiliations: ["IMLA, Offenburg University", "Herrenknecht AG", "Mannheim University"]
cities: ["Offenburg", "Schwanau", "Mannheim"]
countries: ["Germany", "Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Spitznagel_PhysicsGen_Can_Generative_Models_Learn_from_Images_to_Predict_Complex_CVPR_2025_paper.html

abstract:
The image-to-image translation abilities of generative learning models have recently made significant progress in the estimation of complex (steered) mappings between image distributions. While appearance based tasks like image in-painting or style transfer have been studied at length, we propose to investigate the potential of generative models in the context of physical simulations. Providing a dataset of 300k image-pairs and baseline evaluations for three different physical simulation tasks, we propose a benchmark to investigate the following research questions: i) are generative models able to learn complex physical relations from input-output image pairs? ii) what speedups can be achieved by replacing differential equation based simulations? While baseline evaluations of different current models show the potential for high speedups (ii), these results also show strong limitations toward the physical correctness (i). This underlines the need for new methods to enforce physical correctness.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Spitznagel_PhysicsGen_Can_Generative_Models_Learn_from_Images_to_Predict_Complex_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Spitznagel_PhysicsGen_Can_Generative_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Spitznagel_2025_CVPR,
    author    = {Spitznagel, Martin and Vaillant, Jan and Keuper, Janis},
    title     = {PhysicsGen: Can Generative Models Learn from Images to Predict Complex Physical Relations?},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11125-11134}
}

raw_affiliation:
[{"affiliation": "IMLA, Offenburg University", "city": "Offenburg", "country": "Germany"}, {"affiliation": "Herrenknecht AG", "city": "Schwanau", "country": "Germany"}, {"affiliation": "Mannheim University", "city": "Mannheim", "country": "Germany"}]


title: Taste More, Taste Better: Diverse Data and Strong Model Boost Semi-Supervised Crowd Counting
authors: Maochen Yang, Zekun Li, Jian Zhang, Lei Qi, Yinghuan Shi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24440-24451
affiliations: ["Nanjing University", "Suzhou Laboratory", "Southeast University"]
cities: ["Nanjing", "Suzhou", "Nanjing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Taste_More_Taste_Better_Diverse_Data_and_Strong_Model_Boost_CVPR_2025_paper.html

abstract:
Semi-supervised crowd counting is crucial for addressing the high annotation costs of densely populated scenes. Although several methods based on pseudo-labeling have been proposed, it remains challenging to effectively and accurately utilize unlabeled data. In this paper, we propose a novel framework called Taste More Taste Better (TMTB), which emphasizes both data and model aspects. Firstly, we explore a data augmentation technique well-suited for the crowd counting task. By inpainting the background regions, this technique can effectively enhance data diversity while preserving the fidelity of the entire scenes. Secondly, we introduce the Visual State Space Model as backbone to capture the global context information from crowd scenes, which is crucial for extremely crowded, low-light, and adverse weather scenarios. In addition to the traditional regression head for exact prediction, we employ an Anti-Noise classification head to provide less exact but more accurate supervision, since the regression head is sensitive to noise in manual annotations. We conduct extensive experiments on four benchmark datasets and show that our method outperforms state-of-the-art methods by a large margin. Code is publicly available on https://github.com/syhien/taste_more_taste_better.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Taste_More_Taste_Better_Diverse_Data_and_Strong_Model_Boost_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Taste_More_Taste_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Maochen and Li, Zekun and Zhang, Jian and Qi, Lei and Shi, Yinghuan},
    title     = {Taste More, Taste Better: Diverse Data and Strong Model Boost Semi-Supervised Crowd Counting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24440-24451}
}

raw_affiliation:
[{"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "Suzhou Laboratory", "city": "Suzhou", "country": "China"}, {"affiliation": "Southeast University", "city": "Nanjing", "country": "China"}]


title: Gaussian Splashing: Unified Particles for Versatile Motion Synthesis and Rendering
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 518-529
affiliations: ["State Key Laboratory of CAD&CG, Zhejiang University", "University of Utah", "UCLA"]
cities: ["Hangzhou", "Salt Lake City", "Los Angeles"]
countries: ["China", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Feng_Gaussian_Splashing_Unified_Particles_for_Versatile_Motion_Synthesis_and_Rendering_CVPR_2025_paper.html

authors:
Yutao Feng, Xiang Feng, Yintong Shang, Ying Jiang, Chang Yu, Zeshun Zong, Tianjia Shao, Hongzhi Wu, Kun Zhou, Chenfanfu Jiang, Yin Yang

abstract:
We demonstrate the feasibility of integrating physics-based animations of solids and fluids with 3D Gaussian Splatting (3DGS) to create novel effects in virtual scenes reconstructed using 3DGS. Leveraging the coherence of the Gaussian Splatting and Position-Based Dynamics (PBD) in the underlying representation, we manage rendering, view synthesis, and the dynamics of solids and fluids in a cohesive manner. Similar to GaussianShader, we enhance each Gaussian kernel with an added normal, aligning the kernel's orientation with the surface normal to refine the PBD simulation. This approach effectively eliminates spiky noises that arise from rotational deformation in solids. It also allows us to integrate physically based rendering to augment the dynamic surface reflections on fluids. Consequently, our framework is capable of realistically reproducing surface highlights on dynamic fluids and facilitating interactions between scene objects and fluids from new views.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Feng_Gaussian_Splashing_Unified_Particles_for_Versatile_Motion_Synthesis_and_Rendering_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Feng_Gaussian_Splashing_Unified_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Feng_2025_CVPR,
    author    = {Feng, Yutao and Feng, Xiang and Shang, Yintong and Jiang, Ying and Yu, Chang and Zong, Zeshun and Shao, Tianjia and Wu, Hongzhi and Zhou, Kun and Jiang, Chenfanfu and Yang, Yin},
    title     = {Gaussian Splashing: Unified Particles for Versatile Motion Synthesis and Rendering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {518-529}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of CAD&CG, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "University of Utah", "city": "Salt Lake City", "country": "USA"}, {"affiliation": "UCLA", "city": "Los Angeles", "country": "USA"}]


title: Improve Representation for Imbalanced Regression through Geometric Constraints
authors: Zijian Dong, Yilei Wu, Chongyao Chen, Yingtian Zou, Yichi Zhang, Juan Helen Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5082-5091
affiliations: ["National University of Singapore", "Duke University"]
cities: ["Singapore", "Durham"]
countries: ["Singapore", "US"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dong_Improve_Representation_for_Imbalanced_Regression_through_Geometric_Constraints_CVPR_2025_paper.html

abstract:
In representation learning, uniformity refers to the uniform feature distribution in the latent space (i.e., unit hypersphere). Previous work has shown that improving uniformity contributes to the learning of under-represented classes. However, most of the previous work focused on classification; the representation space of imbalanced regression remains unexplored. Classification-based methods are not suitable for regression tasks because they cluster features into distinct groups without considering the continuous and ordered nature essential for regression. In a geometric aspect, we uniquely focus on ensuring uniformity in the latent space for imbalanced regression through two key losses: enveloping and homogeneity. The enveloping loss encourages the induced trace to uniformly occupy the surface of a hypersphere, while the homogeneity loss ensures smoothness, with representations evenly spaced at consistent intervals. Our method integrates these geometric principles into the data representations via a Surrogate-driven Representation Learning (SRL) framework. Experiments with real-world regression and operator learning tasks highlight the importance of uniformity in imbalanced regression and validate the efficacy of our geometry-based loss functions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dong_Improve_Representation_for_Imbalanced_Regression_through_Geometric_Constraints_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dong_Improve_Representation_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dong_2025_CVPR,
    author    = {Dong, Zijian and Wu, Yilei and Chen, Chongyao and Zou, Yingtian and Zhang, Yichi and Zhou, Juan Helen},
    title     = {Improve Representation for Imbalanced Regression through Geometric Constraints},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5082-5091}
}

raw_affiliation:
[{"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Duke University", "city": "Durham", "country": "US"}]


title: AnyDressing: Customizable Multi-Garment Virtual Dressing via Latent Diffusion Models
authors: Xinghui Li, Qichao Sun, Pengze Zhang, Fulong Ye, Zhichao Liao, Wanquan Feng, Songtao Zhao, Qian He
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23723-23733
affiliations: ["Bytedance Intelligent Creation", "Tsinghua University"]
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_AnyDressing_Customizable_Multi-Garment_Virtual_Dressing_via_Latent_Diffusion_Models_CVPR_2025_paper.html

abstract:
Recent advances in garment-centric image generation from text and image prompts based on diffusion models are impressive. However, existing methods lack support for various combinations of attire, and struggle to preserve the garment details while maintaining faithfulness to the text prompts, limiting their performance across diverse scenarios. In this paper, we focus on a new task, i.e., Multi-Garment Virtual Dressing, and we propose a novel AnyDressing method for customizing characters conditioned on any combination of garments and any personalized text prompts. AnyDressing primarily comprises two primary networks named GarmentsNet and DressingNet, which are respectively dedicated to extracting detailed clothing features and generating customized images. Specifically, we propose an efficient and scalable module called Garment-Specific Feature Extractor in GarmentsNet to individually encode garment textures in parallel. This design prevents garment confusion while ensuring network efficiency. Meanwhile, we design an adaptive Dressing-Attention mechanism and a novel Instance-Level Garment Localization Learning strategy in DressingNet to accurately inject multi-garment features into their corresponding regions. This approach efficiently integrates multi-garment texture cues into generated images and further enhances text-image consistency. Additionally, we introduce a Garment-Enhanced Texture Learning strategy to improve the fine-grained texture details of garments. Thanks to our well-craft design, AnyDressing can serve as a plug-in module to easily integrate with any community control extensions for diffusion models, improving the diversity and controllability of synthesized images. Extensive experiments show that AnyDressing achieves state-of-the-art results.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_AnyDressing_Customizable_Multi-Garment_Virtual_Dressing_via_Latent_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_AnyDressing_Customizable_Multi-Garment_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Xinghui and Sun, Qichao and Zhang, Pengze and Ye, Fulong and Liao, Zhichao and Feng, Wanquan and Zhao, Songtao and He, Qian},
    title     = {AnyDressing: Customizable Multi-Garment Virtual Dressing via Latent Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23723-23733}
}

raw_affiliation:
[{"affiliation": "Bytedance Intelligent Creation", "city": "Beijing", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]


title: Spectral Informed Mamba for Robust Point Cloud Processing
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11799-11809
affiliations: ["LIVIA, \u00b4ETS Montr\u00b4eal, Canada", "International Laboratory on Learning Systems (ILLS)"]
cities: ["Montreal", "Unknown"]
countries: ["Canada", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bahri_Spectral_Informed_Mamba_for_Robust_Point_Cloud_Processing_CVPR_2025_paper.html

authors:
Ali Bahri, Moslem Yazdanpanah, Mehrdad Noori, Sahar Dastani, Milad Cheraghalikhani, Gustavo Adolfo Vargas Hakim, David Osowiechi, Farzad Beizaee, Ismail Ben Ayed, Christian Desrosiers

abstract:
State Space Models (SSMs) have shown significant promise in Natural Language Processing (NLP) and, more recently, computer vision. This paper introduces a new methodology leveraging Mamba and Masked Autoencoder (MAE) networks for point cloud data in both supervised and self-supervised learning. We propose three key contributions to enhance Mamba's capability in processing complex point cloud structures. First, we exploit the spectrum of a graph Laplacian to capture patch connectivity, defining an isometry-invariant traversal order that is robust to viewpoints and better captures shape manifolds than traditional 3D grid-based traversals. Second, we adapt segmentation via a recursive patch partitioning strategy informed by Laplacian spectral components, allowing finer integration and segment analysis. Third, we address token placement in MAE for Mamba by restoring tokens to their original positions, which preserves essential order and improves learning. Extensive experiments demonstrate our approach's improvements in classification, segmentation, and few-shot tasks over state-of-the-art (SOTA) baselines.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bahri_Spectral_Informed_Mamba_for_Robust_Point_Cloud_Processing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bahri_Spectral_Informed_Mamba_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bahri_2025_CVPR,
    author    = {Bahri, Ali and Yazdanpanah, Moslem and Noori, Mehrdad and Dastani, Sahar and Cheraghalikhani, Milad and Hakim, Gustavo Adolfo Vargas and Osowiechi, David and Beizaee, Farzad and Ben Ayed, Ismail and Desrosiers, Christian},
    title     = {Spectral Informed Mamba for Robust Point Cloud Processing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11799-11809}
}

raw_affiliation:
[{"affiliation": "LIVIA, \u00b4ETS Montr\u00b4eal, Canada", "city": "Montreal", "country": "Canada"}, {"affiliation": "International Laboratory on Learning Systems (ILLS)", "city": "Unknown", "country": "Unknown"}]


html_link: https://openaccess.thecvf.com/content/CVPR2025/html/Souza_Latent_Space_Imaging_CVPR_2025_paper.html
title: Latent Space Imaging
authors: Matheus Souza, Yidan Zheng, Kaizhang Kang, Yogeshwar Nath Mishra, Qiang Fu, Wolfgang Heidrich
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28295-28305
raw_affiliation: [{"affiliation": "KAUST", "city": "Thuwal", "country": "Saudi Arabia"}]
affiliations: ["KAUST"]
cities: ["Thuwal"]
countries: ["Saudi Arabia"]
abstract:
Digital imaging systems have traditionally relied on brute-force measurement and processing of pixels arranged on regular grids. In contrast, the human visual system performs significant data reduction from the large number of photoreceptors to the optic nerve, effectively encoding visual information into a low-bandwidth latent space representation optimized for brain processing. Inspired by this, we propose a similar approach to advance artificial vision systems. Latent Space Imaging introduces a new paradigm that combines optics and software to encode image information directly into the semantically rich latent space of a generative model. This approach substantially reduces bandwidth and memory demands during image capture and enables a range of downstream tasks focused on the latent space.We validate this principle through an initial hardware prototype based on a single-pixel camera. By implementing an amplitude modulation scheme that encodes into the generative model's latent space, we achieve compression ratios ranging from 1:100 to 1:1000 during imaging, and up to 1:16384 for downstream applications. This approach leverages the model's intrinsic linear boundaries, demonstrating the potential of latent space imaging for highly efficient imaging hardware, adaptable future applications in high-speed imaging, and task-specific cameras with significantly reduced hardware complexity.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Souza_Latent_Space_Imaging_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Souza_Latent_Space_Imaging_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Souza_2025_CVPR,
    author    = {Souza, Matheus and Zheng, Yidan and Kang, Kaizhang and Mishra, Yogeshwar Nath and Fu, Qiang and Heidrich, Wolfgang},
    title     = {Latent Space Imaging},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28295-28305}
}


title: Balanced Direction from Multifarious Choices: Arithmetic Meta-Learning for Domain Generalization
authors: Xiran Wang, Jian Zhang, Lei Qi, Yinghuan Shi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30577-30587
affiliations: ["Nanjing University", "Southeast University", "Suzhou Laboratory"]
cities: ["Nanjing", "Nanjing", "Suzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Balanced_Direction_from_Multifarious_Choices_Arithmetic_Meta-Learning_for_Domain_Generalization_CVPR_2025_paper.html

abstract:
Domain generalization is proposed to address distribution shift, arising from statistical disparities between training source and unseen target domains. The widely used first-order meta-learning algorithms demonstrate strong performance for domain generalization by leveraging the gradient matching theory, which aims to establish balanced parameters across source domains to reduce overfitting to any particular domain. However, our analysis reveals that there are actually numerous directions to achieve gradient matching, with current methods representing just one possible path. These methods actually overlook another critical factor that the balanced parameters should be close to the centroid of optimal parameters of each source domain. To address this, we propose a simple yet effective arithmetic meta-learning with arithmetic-weighted gradients. This approach, while adhering to the principles of gradient matching, promotes a more precise balance by estimating the centroid between domain-specific optimal parameters. Experimental results validate the effectiveness of our strategy. Our code is available at https://github.com/zzwdx/ARITH.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Balanced_Direction_from_Multifarious_Choices_Arithmetic_Meta-Learning_for_Domain_Generalization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Balanced_Direction_from_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Xiran and Zhang, Jian and Qi, Lei and Shi, Yinghuan},
    title     = {Balanced Direction from Multifarious Choices: Arithmetic Meta-Learning for Domain Generalization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30577-30587}
}

raw_affiliation:
[{"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "Southeast University", "city": "Nanjing", "country": "China"}, {"affiliation": "Suzhou Laboratory", "city": "Suzhou", "country": "China"}]


authors: Yejee Shin, Yeeun Lee, Hanbyol Jang, Geonhui Son, Hyeongyu Kim, Dosik Hwang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30918-30927
affiliations: ["Yonsei University", "Korea Institute of Science and Technology"]
cities: ["Seoul", "Seoul"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shin_Anatomical_Consistency_and_Adaptive_Prior-informed_Transformation_for_Multi-contrast_MR_Image_CVPR_2025_paper.html

title:
Anatomical Consistency and Adaptive Prior-informed Transformation for Multi-contrast MR Image Synthesis via Diffusion Model

abstract:
Multi-contrast magnetic resonance (MR) images offer critical diagnostic information but are limited by long scan times and high cost. While diffusion models (DMs) excel in medical image synthesis, they often struggle to maintain anatomical consistency and utilize the diverse characteristics of multi-contrast MR images effectively. We propose APT, a unified diffusion model designed to generate accurate and anatomically consistent multi-contrast MR images. APT introduces a mutual information fusion module and an anatomical consistency loss to preserve critical anatomical structures across multiple contrast inputs. To enhance synthesis, APT incorporates a two-stage inference process: in the first stage, a prior codebook provides coarse anatomical structures by selecting appropriate guidance based on precomputed similarity mappings and Bezier curve transformations. The second stage applies iterative unrolling with weighted averaging to refine the initial output, enhancing fine anatomical details and ensuring structural consistency. This approach enables the preservation of both global structures and local details, resulting in realistic and diagnostically valuable synthesized images. Extensive experiments on public multi-contrast MR brain images demonstrate that our approach significantly outperforms state-of-the-art methods. The source codes are available at https://github.com/yejees/APT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shin_Anatomical_Consistency_and_Adaptive_Prior-informed_Transformation_for_Multi-contrast_MR_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shin_Anatomical_Consistency_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shin_2025_CVPR,
    author    = {Shin, Yejee and Lee, Yeeun and Jang, Hanbyol and Son, Geonhui and Kim, Hyeongyu and Hwang, Dosik},
    title     = {Anatomical Consistency and Adaptive Prior-informed Transformation for Multi-contrast MR Image Synthesis via Diffusion Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30918-30927}
}

raw_affiliation:
[{"affiliation": "Yonsei University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Korea Institute of Science and Technology", "city": "Seoul", "country": "South Korea"}]


title: BlobGEN-Vid: Compositional Text-to-Video Generation with Blob Video Representations
authors: Weixi Feng, Chao Liu, Sifei Liu, William Yang Wang, Arash Vahdat, Weili Nie
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12989-12998
affiliations: ["UC Santa Barbara", "NVIDIA"]
cities: ["Santa Barbara", "Santa Clara"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Feng_BlobGEN-Vid_Compositional_Text-to-Video_Generation_with_Blob_Video_Representations_CVPR_2025_paper.html

abstract:
Existing video generation models struggle to follow complex text prompts and synthesize multiple objects, raising the need for additional grounding input for improved controllability. In this work, we propose to decompose videos into visual primitives -- blob video representation, a general representation for controllable video generation. Based on blob conditions, we develop a blob-grounded video diffusion model named BlobGEN-Vid that allows users to control object motions and fine-grained object appearance. In particular, we introduce a masked 3D attention module that effectively improves regional consistency across frames. In addition, we introduce a learnable module to interpolate text embeddings so that users can control semantics in specific frames and obtain smooth object transitions. We show that our framework is model-agnostic and build BlobGEN-Vid based on both U-Net and DiT-based video diffusion models. Extensive experimental results show that BlobGEN-Vid achieves superior zero-shot video generation ability and state-of-the-art layout controllability on multiple benchmarks. When combined with a Large Language Model for layout planning, our framework even outperforms proprietary text-to-video generators regarding compositional accuracy.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Feng_BlobGEN-Vid_Compositional_Text-to-Video_Generation_with_Blob_Video_Representations_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Feng_BlobGEN-Vid_Compositional_Text-to-Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Feng_2025_CVPR,
    author    = {Feng, Weixi and Liu, Chao and Liu, Sifei and Wang, William Yang and Vahdat, Arash and Nie, Weili},
    title     = {BlobGEN-Vid: Compositional Text-to-Video Generation with Blob Video Representations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12989-12998}
}

raw_affiliation:
[{"affiliation": "UC Santa Barbara", "city": "Santa Barbara", "country": "USA"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19218-19229
cities: ["Shanghai", "Shanghai", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_D2SP_Dynamic_Dual-Stage_Purification_Framework_for_Dual_Noise_Mitigation_in_CVPR_2025_paper.html

title:
D2SP: Dynamic Dual-Stage Purification Framework for Dual Noise Mitigation in Vision-based Affective Recognition.

authors:
Haoran Wang, Xinji Mai, Zeng Tao, Xuan Tong, Junxiong Lin, Yan Wang, Jiawen Yu, Shaoqi Yan, Ziheng Zhou, Wenqiang Zhang

abstract:
The current advancements in Dynamic Facial Expression Recognition (DFER) methods mainly focus on better capturing the spatial and temporal features of facial expressions. However, DFER datasets contain a substantial amount of noisy samples, and few have addressed the issue of handling this noise. We identified two types of noise: one is caused by low-quality data resulting from factors such as occlusion, dim lighting, and blurriness; the other arises from mislabeled data due to annotation bias by annotators. Addressing the two types of noise, we have meticulously crafted a Dynamic Dual-Stage Purification (D2SP) Framework. This initiative aims to dynamically purify the DFER datasets of these two types of noise, ensuring that only high-quality and correctly labeled data is used in the training process. To mitigate low-quality samples, we introduce the Coarse-Grained Pruning (CGP) stage, which computes sample weights and prunes those low-weight samples. After CGP, the Fine-Grained Correction (FGC) stage evaluates prediction stability to correct mislabeled data. Moreover, D2SP is conceived as a general and plug-and-play framework, tailored to integrate seamlessly with prevailing DFER methods. Extensive experiments covering prevalent DFER datasets and deploying multiple benchmark methods have substantiated D2SP's ability to significantly enhance performance metrics.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_D2SP_Dynamic_Dual-Stage_Purification_Framework_for_Dual_Noise_Mitigation_in_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_D2SP_Dynamic_Dual-Stage_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Haoran and Mai, Xinji and Tao, Zeng and Tong, Xuan and Lin, Junxiong and Wang, Yan and Yu, Jiawen and Yan, Shaoqi and Zhou, Ziheng and Zhang, Wenqiang},
    title     = {D2SP: Dynamic Dual-Stage Purification Framework for Dual Noise Mitigation in Vision-based Affective Recognition.},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19218-19229}
}

raw_affiliation:
[{"affiliation": "Shanghai Engineering Research Center of AI & Robotics, Academy for Engineering & Technology, Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Engineering Research Center of AI & Robotics, Ministry of Education,Academy for Engineering & Technology, Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University", "city": "Shanghai", "country": "China"}]

affiliations:
["Shanghai Engineering Research Center of AI & Robotics, Academy for Engineering & Technology, Fudan University", "Engineering Research Center of AI & Robotics, Ministry of Education,Academy for Engineering & Technology, Fudan University", "Shanghai Key Lab of Intelligent Information Processing, School of Computer Science, Fudan University"]


title: PartRM: Modeling Part-Level Dynamics with Large Cross-State Reconstruction Model
authors: Mingju Gao, Yike Pan, Huan-ang Gao, Zongzheng Zhang, Wenyi Li, Hao Dong, Hao Tang, Li Yi, Hao Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7004-7014
affiliations: ["Tsinghua University", "University of Michigan", "Peking University", "BAAI"]
cities: ["Beijing", "Ann Arbor", "Beijing", "Beijing"]
countries: ["China", "USA", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_PartRM_Modeling_Part-Level_Dynamics_with_Large_Cross-State_Reconstruction_Model_CVPR_2025_paper.html

abstract:
As interest grows in world models that predict future states from current observations and actions, accurately modeling part-level dynamics has become increasingly relevant for various applications. Existing approaches, such as Puppet-Master, rely on fine-tuning large-scale pre-trained video diffusion models, which are impractical for real-world use due to the limitations of 2D video representation and slow processing times. To overcome these challenges, we present PartRM, a novel 4D reconstruction framework that simultaneously models appearance, geometry, and part-level motion from multi-view images of a static object. PartRM builds upon large 3D Gaussian reconstruction models, leveraging their extensive knowledge of appearance and geometry in static objects. To address data scarcity in 4D, we introduce the PartDrag-4D dataset, providing multi-view observations of part-level dynamics across over 20,000 states. We enhance the model's understanding of interaction conditions with a multi-scale drag embedding module that captures dynamics at varying granularities. To prevent catastrophic forgetting during fine-tuning, we implement a two-stage training process that focuses sequentially on motion and appearance learning. Experimental results show that PartRM establishes a new state-of-the-art in part-level motion learning and can be applied in manipulation tasks in robotics.  Our code, data, and models are publicly available to facilitate future research.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_PartRM_Modeling_Part-Level_Dynamics_with_Large_Cross-State_Reconstruction_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_PartRM_Modeling_Part-Level_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Gao_2025_CVPR,
    author    = {Gao, Mingju and Pan, Yike and Gao, Huan-ang and Zhang, Zongzheng and Li, Wenyi and Dong, Hao and Tang, Hao and Yi, Li and Zhao, Hao},
    title     = {PartRM: Modeling Part-Level Dynamics with Large Cross-State Reconstruction Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7004-7014}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "BAAI", "city": "Beijing", "country": "China"}]


title: LaVin-DiT: Large Vision Diffusion Transformer
authors: Zhaoqing Wang, Xiaobo Xia, Runnan Chen, Dongdong Yu, Changhu Wang, Mingming Gong, Tongliang Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20060-20070
cities: ["Sydney", "Singapore", "Melbourne", "Unknown"]
countries: ["Australia", "Singapore", "Australia", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_LaVin-DiT_Large_Vision_Diffusion_Transformer_CVPR_2025_paper.html

abstract:
This paper presents the Large Vision Diffusion Transformer (LaVin-DiT), a scalable and unified foundation model designed to tackle over 20 computer vision tasks in a generative framework. Unlike existing large vision models directly adapted from natural language processing architectures, which rely on less efficient autoregressive techniques and disrupt spatial relationships essential for vision data, LaVin-DiT introduces key innovations to optimize generative performance for vision tasks. First, to address the high dimensionality of visual data, we incorporate a spatial-temporal variational autoencoder that encodes data into a continuous latent space. Second, for generative modeling, we develop a joint diffusion transformer that progressively produces vision outputs. Third, for unified multi-task training, in-context learning is implemented. Input-target pairs serve as task context, which guides the diffusion transformer to align outputs with specific tasks within the latent space. During inference, a task-specific context set and test data as queries allow LaVin-DiT to generalize across tasks without fine-tuning. Trained on extensive vision datasets, the model is scaled from 0.1B to 3.4B parameters, demonstrating substantial scalability and state-of-the-art performance across diverse vision tasks. This work introduces a novel pathway for large vision foundation models, underscoring the promising potential of diffusion transformers.  The code and models are available at https://derrickwang005.github.io/LaVin-DiT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_LaVin-DiT_Large_Vision_Diffusion_Transformer_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_LaVin-DiT_Large_Vision_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zhaoqing and Xia, Xiaobo and Chen, Runnan and Yu, Dongdong and Wang, Changhu and Gong, Mingming and Liu, Tongliang},
    title     = {LaVin-DiT: Large Vision Diffusion Transformer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20060-20070}
}

raw_affiliation:
[{"affiliation": "The University of Sydney", "city": "Sydney", "country": "Australia"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "The University of Melbourne", "city": "Melbourne", "country": "Australia"}, {"affiliation": "AIsphere", "city": "Unknown", "country": "Unknown"}]

affiliations:
["The University of Sydney", "National University of Singapore", "The University of Melbourne", "AIsphere"]


title: DiffFNO: Diffusion Fourier Neural Operator
authors: Xiaoyi Liu, Hao Tang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 150-160
affiliations: ["Washington University in St. Louis", "Peking University"]
cities: ["St. Louis", "Beijing"]
countries: ["USA", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_DiffFNO_Diffusion_Fourier_Neural_Operator_CVPR_2025_paper.html

abstract:
We introduce DiffFNO, a novel diffusion framework for arbitrary-scale super-resolution strengthened by a Weighted Fourier Neural Operator (WFNO). Mode Rebalancing in WFNO effectively captures critical frequency components, significantly improving the reconstruction of high-frequency image details that are crucial for super-resolution tasks. Gated Fusion Mechanism (GFM) adaptively complements WFNO's spectral features with spatial features from an Attention-based Neural Operator (AttnNO). This enhances the network's capability to capture both global structures and local details. Adaptive Time-Step (ATS) ODE solver, a deterministic sampling strategy, accelerates inference without sacrificing output quality by dynamically adjusting integration step sizes ATS. Extensive experiments demonstrate that DiffFNO achieves state-of-the-art (SOTA) results, outperforming existing methods across various scaling factors by a margin of 2-4 dB in PSNR, including those beyond the training distribution. It also achieves this at lower inference time. Our approach sets a new standard in super-resolution, delivering both superior accuracy and computational efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_DiffFNO_Diffusion_Fourier_Neural_Operator_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_DiffFNO_Diffusion_Fourier_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Xiaoyi and Tang, Hao},
    title     = {DiffFNO: Diffusion Fourier Neural Operator},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {150-160}
}

raw_affiliation:
[{"affiliation": "Washington University in St. Louis", "city": "St. Louis", "country": "USA"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}]


authors: Jingshun Huang, Haitao Lin, Tianyu Wang, Yanwei Fu, Xiangyang Xue, Yi Zhu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11654-11664
affiliations: ["Fudan University", "Huawei, Noah\u2019s Ark Lab"]
cities: ["Shanghai", "Shenzhen"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_CAP-Net_A_Unified_Network_for_6D_Pose_and_Size_Estimation_CVPR_2025_paper.html

title:
CAP-Net: A Unified Network for 6D Pose and Size Estimation of Categorical Articulated Parts from a Single RGB-D Image

abstract:
This paper tackles category-level pose estimation of ar- ticulated objects in robotic manipulation tasks and intro- duces a new benchmark dataset. While recent methods es- timate part poses and sizes at the category level, they often rely on geometric cues and complex multi-stage pipelines that first segment parts from the point cloud, followed by Normalized Part Coordinate Space (NPCS) estimation for 6D poses. These approaches overlook dense semantic cues from RGB images, leading to suboptimal accuracy, partic- ularly for objects with small parts. To address these limita- tions, we propose a single-stage Network, CAP-Net, for es- timating the 6D poses and sizes of Categorical Articulated Parts. This method combines RGB-D features to generate instance segmentation and NPCS representations for each part in an end-to-end manner. CAP-Net uses a unified net- work to simultaneously predict point-wise class labels, cen- troid offsets, and NPCS maps. A clustering algorithm then groups points of the same predicted class based on their es- timated centroid distances to isolate each part. Finally, the NPCS region of each part is aligned with the point cloud to recover its final pose and size. To bridge the sim-to-real do- main gap, we introduce the RGBD-Art dataset, the largest RGB-D articulated dataset to date, featuring photorealistic RGB images and depth noise simulated from real sensors. Experimental evaluations on the RGBD-Art dataset demon- strate that our method significantly outperforms the state- of-the-art approach. Real-world deployments of our model in robotic tasks underscore its robustness and exceptional sim-to-real transfer capabilities, confirming its substantial practical utility.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_CAP-Net_A_Unified_Network_for_6D_Pose_and_Size_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_CAP-Net_A_Unified_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Jingshun and Lin, Haitao and Wang, Tianyu and Fu, Yanwei and Xue, Xiangyang and Zhu, Yi},
    title     = {CAP-Net: A Unified Network for 6D Pose and Size Estimation of Categorical Articulated Parts from a Single RGB-D Image},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11654-11664}
}

raw_affiliation:
[{"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Huawei, Noah\u2019s Ark Lab", "city": "Shenzhen", "country": "China"}]


authors: Shining Wang, Yunlong Wang, Ruiqi Wu, Bingliang Jiao, Wenxuan Wang, Peng Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22119-22128
cities: ["Xi'an", "Ningbo", "Shenzhen", "Xi'an"]
countries: ["China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_SeCap_Self-Calibrating_and_Adaptive_Prompts_for_Cross-view_Person_Re-Identification_in_CVPR_2025_paper.html

title:
SeCap: Self-Calibrating and Adaptive Prompts for Cross-view Person Re-Identification in Aerial-Ground Networks

abstract:
When discussing the Aerial-Ground Person Re-identification (AGPReID) task, we face the main challenge of the significant appearance variations caused by different viewpoints, making identity matching difficult. To address this issue, previous methods attempt to reduce the differences between viewpoints by critical attributes and decoupling the viewpoints. While these methods can mitigate viewpoint differences to some extent, they still face two main issues: (1) difficulty in handling viewpoint diversity and (2) neglect of the contribution of local features. To effectively address these challenges, we design and implement the Self-Calibrating and Adaptive Prompt (SeCap) method for the AGPReID task. The core of this framework relies on the Prompt Re-calibration Module (PRM), which adaptively re-calibrates prompts based on the input. Combined with the Local Feature Refinement Module (LFRM), SeCap can extract view-invariant features from local features for AGPReID. Meanwhile, given the current scarcity of datasets in the AGPReID field, we further contribute two real-world Large-scale Aerial-Ground Person Re-Identification datasets, LAGPeR and G2APS-ReID. The former is collected and annotated by us independently, covering 4,231 unique identities and containing 63,841 high-quality images; the latter is reconstructed from the person search dataset G2APS. Through extensive experiments on AGPReID datasets, we demonstrate that SeCap is a feasible and effective solution for the AGPReID task.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_SeCap_Self-Calibrating_and_Adaptive_Prompts_for_Cross-view_Person_Re-Identification_in_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_SeCap_Self-Calibrating_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Shining and Wang, Yunlong and Wu, Ruiqi and Jiao, Bingliang and Wang, Wenxuan and Wang, Peng},
    title     = {SeCap: Self-Calibrating and Adaptive Prompts for Cross-view Person Re-Identification in Aerial-Ground Networks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22119-22128}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "Ningbo Institute, Northwestern Polytechnical University", "city": "Ningbo", "country": "China"}, {"affiliation": "Shenzhen Research Institute, Northwestern Polytechnical University", "city": "Shenzhen", "country": "China"}, {"affiliation": "National Engineering Laboratory for integrated Aero-Space-Ground-Ocean", "city": "Xi'an", "country": "China"}]

affiliations:
["School of Computer Science, Northwestern Polytechnical University", "Ningbo Institute, Northwestern Polytechnical University", "Shenzhen Research Institute, Northwestern Polytechnical University", "National Engineering Laboratory for integrated Aero-Space-Ground-Ocean"]


title: Zero-Shot Styled Text Image Generation, but Make It Autoregressive
authors: Vittorio Pippi, Fabio Quattrini, Silvia Cascianelli, Alessio Tonioni, Rita Cucchiara
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7910-7919
affiliations: ["University of Modena and Reggio Emilia", "Google"]
cities: ["Modena", "Mountain View"]
countries: ["Italy", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pippi_Zero-Shot_Styled_Text_Image_Generation_but_Make_It_Autoregressive_CVPR_2025_paper.html

abstract:
Styled Handwritten Text Generation (HTG) has recently received attention from the computer vision and document analysis communities, which have developed several solutions, either GAN- or diffusion-based, that achieved promising results. Nonetheless, these strategies fail to generalize to novel styles and have technical constraints, particularly in terms of maximum output length and training efficiency. To overcome these limitations, in this work, we propose a novel framework for text-image generation, dubbed Emuru. Our approach leverages a powerful text-image representation model (a variational autoencoder) combined with an autoregressive Transformer. Our approach enables the generation of styled text images conditioned on textual content and style examples, such as specific fonts or handwriting styles. We train our model solely on a diverse, synthetic dataset of English text rendered in over 100,000 typewritten and calligraphy fonts, which gives it the capability to reproduce unseen styles (both fonts and users' handwriting) in zero-shot. To the best of our knowledge, Emuru is the first autoregressive model for HTG, and the first designed specifically for generalization to novel styles. Moreover, our model generates images without background artifacts, which are easier to use for downstream applications. Extensive evaluation on both typewritten and handwritten, any-length text image generation scenarios demonstrates the effectiveness of our approach.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pippi_Zero-Shot_Styled_Text_Image_Generation_but_Make_It_Autoregressive_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pippi_Zero-Shot_Styled_Text_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Pippi_2025_CVPR,
    author    = {Pippi, Vittorio and Quattrini, Fabio and Cascianelli, Silvia and Tonioni, Alessio and Cucchiara, Rita},
    title     = {Zero-Shot Styled Text Image Generation, but Make It Autoregressive},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7910-7919}
}

raw_affiliation:
[{"affiliation": "University of Modena and Reggio Emilia", "city": "Modena", "country": "Italy"}, {"affiliation": "Google", "city": "Mountain View", "country": "USA"}]


title: Don't Shake the Wheel: Momentum-Aware Planning in End-to-End Autonomous Driving
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22432-22441
cities: ["Beijing", "Beijing", "Unknown", "Macau", "Unknown", "Brisbane", "Hong Kong"]
countries: ["China", "China", "Unknown", "China", "Unknown", "Australia", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Song_Dont_Shake_the_Wheel_Momentum-Aware_Planning_in_End-to-End_Autonomous_Driving_CVPR_2025_paper.html

authors:
Ziying Song, Caiyan Jia, Lin Liu, Hongyu Pan, Yongchang Zhang, Junming Wang, Xingyu Zhang, Shaoqing Xu, Lei Yang, Yadan Luo

abstract:
End-to-end autonomous driving frameworks enable seamless integration of perception and planning but often rely on one-shot trajectory prediction, which may lead to unstable control and vulnerability to occlusions in single-frame perception. To address this, we propose the Momentum-Aware Driving (MomAD) framework, which introduces trajectory momentum and perception momentum to stabilize and refine trajectory predictions. MomAD comprises two core components: (1) Topological Trajectory Matching (TTM) employs Hausdorff Distance to select the optimal planning query that aligns with prior paths to ensure coherence; (2) Momentum Planning Interactor (MPI) cross-attends the selected planning query with historical queries to expand static and dynamic perception files. This enriched query, in turn, helps regenerate long-horizon trajectory and reduce collision risks. To mitigate noise arising from dynamic environments and detection errors, we introduce robust instance denoising during training, enabling the planning model to focus on critical signals and improve its robustness. We also propose a novel Trajectory Prediction Consistency (TPC) metric to quantitatively assess planning stability. Experiments on the nuScenes dataset demonstrate that MomAD achieves superior long-term consistency (>3s) compared to SOTA methods. Moreover, evaluations on the curated Turning-nuScenes shows that MomAD reduces the collision rate by 26% and improves TPC by 0.97m (33.45%) over a 6s prediction horizon, while closed-loop on Bench2Drive demonstrates an up to 16.3% improvement in success rate.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Song_Dont_Shake_the_Wheel_Momentum-Aware_Planning_in_End-to-End_Autonomous_Driving_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Song_Dont_Shake_the_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Song_2025_CVPR,
    author    = {Song, Ziying and Jia, Caiyan and Liu, Lin and Pan, Hongyu and Zhang, Yongchang and Wang, Junming and Zhang, Xingyu and Xu, Shaoqing and Yang, Lei and Luo, Yadan},
    title     = {Don't Shake the Wheel: Momentum-Aware Planning in End-to-End Autonomous Driving},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22432-22441}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology, Beijing Jiaotong University", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence", "city": "Beijing", "country": "China"}, {"affiliation": "Horizon Robotics", "city": "Unknown", "country": "Unknown"}, {"affiliation": "University of Macau", "city": "Macau", "country": "China"}, {"affiliation": "THU", "city": "Unknown", "country": "Unknown"}, {"affiliation": "The University of Queensland", "city": "Brisbane", "country": "Australia"}, {"affiliation": "HKU", "city": "Hong Kong", "country": "China"}]

affiliations:
["School of Computer Science and Technology, Beijing Jiaotong University", "Beijing Key Laboratory of Traffic Data Mining and Embodied Intelligence", "Horizon Robotics", "University of Macau", "THU", "The University of Queensland", "HKU"]


title: Leveraging Perturbation Robustness to Enhance Out-of-Distribution Detection
authors: Wenxi Chen, Raymond A. Yeh, Shaoshuai Mou, Yan Gu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4724-4733
cities: ["West Lafayette", "West Lafayette", "West Lafayette"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Leveraging_Perturbation_Robustness_to_Enhance_Out-of-Distribution_Detection_CVPR_2025_paper.html

abstract:
Out-of-distribution (OOD) detection is the task of identifying inputs that deviate from the training data distribution. This capability is essential for the safe deployment of deep computer vision models in open-world environments. In this work, we propose a post-hoc method, Perturbation-Rectified OOD detection (PRO), based on the insight that prediction confidence for OOD inputs is more susceptible to reduction under perturbation than IND inputs. From this observation, we proposed a meta-score function that searches for local minimum scores near original inputs by applying gradient descent. This procedure enhances the separability between in-distribution (IND) and OOD samples. Importantly, the approach improves OOD detection performance without complex modifications to the underlying model architectures or training protocol. To validate our approach, we conduct extensive experiments using the OpenOOD benchmark. Our approach further pushes the limit of softmax-based OOD detection and is the leading post-hoc method for small-scale models. On a CIFAR-10 model with adversarial training, PRO effectively detects near-OOD inputs, achieving a reduction of more than 10% on FPR@95 compared to state-of-the-art methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Leveraging_Perturbation_Robustness_to_Enhance_Out-of-Distribution_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Leveraging_Perturbation_Robustness_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Wenxi and Yeh, Raymond A. and Mou, Shaoshuai and Gu, Yan},
    title     = {Leveraging Perturbation Robustness to Enhance Out-of-Distribution Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4724-4733}
}

raw_affiliation:
[{"affiliation": "School of ME, Purdue University", "city": "West Lafayette", "country": "USA"}, {"affiliation": "Department of CS, Purdue University", "city": "West Lafayette", "country": "USA"}, {"affiliation": "School of AAE, Purdue University", "city": "West Lafayette", "country": "USA"}]

affiliations:
["School of ME, Purdue University", "Department of CS, Purdue University", "School of AAE, Purdue University"]


title: Neural Motion Simulator Pushing the Limit of World Models in Reinforcement Learning
authors: Chenjie Hao, Weyl Lu, Yifan Xu, Yubei Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27608-27617
affiliations: ["UC Davis", "Open Path AI Foundation"]
cities: ["Davis", "Unknown"]
countries: ["USA", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hao_Neural_Motion_Simulator_Pushing_the_Limit_of_World_Models_in_CVPR_2025_paper.html

abstract:
An embodied system must not only model the patterns of the external world but also understand its own motion dynamics. A motion dynamic model is essential for efficient skill acquisition and effective planning. In this work, we introduce the neural motion simulator (MoSim), a world model that predicts the future physical state of an embodied system based on current observations and actions. MoSim achieves state-of-the-art performance in physical state prediction and provides competitive performance across a range of downstream tasks. This works shows that when a world model is accurate enough and performs precise long-horizon predictions, it can facilitate efficient skill acquisition in imagined worlds and even enable zero-shot reinforcement learning. Furthermore, MoSim can transform any model-free reinforcement learning (RL) algorithm into a model-based approach, effectively decoupling physical environment modeling from RL algorithm development. This separation allows for independent advancements in RL algorithms and world modeling, significantly improving sample efficiency and enhancing generalization capabilities. Our findings highlight that world models for motion dynamics is a promising direction for developing more versatile and capable embodied systems.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hao_Neural_Motion_Simulator_Pushing_the_Limit_of_World_Models_in_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hao_Neural_Motion_Simulator_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hao_2025_CVPR,
    author    = {Hao, Chenjie and Lu, Weyl and Xu, Yifan and Chen, Yubei},
    title     = {Neural Motion Simulator Pushing the Limit of World Models in Reinforcement Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27608-27617}
}

raw_affiliation:
[{"affiliation": "UC Davis", "city": "Davis", "country": "USA"}, {"affiliation": "Open Path AI Foundation", "city": "Unknown", "country": "Unknown"}]


authors: Zhanhao Liang, Yuhui Yuan, Shuyang Gu, Bohan Chen, Tiankai Hang, Mingxi Cheng, Ji Li, Liang Zheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13199-13208
cities: ["Canberra", "Liverpool", "Nanjing", "Redmond", "Beijing"]
countries: ["Australia", "United Kingdom", "China", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liang_Aesthetic_Post-Training_Diffusion_Models_from_Generic_Preferences_with_Step-by-step_Preference_CVPR_2025_paper.html

title:
Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization

abstract:
Generating visually appealing images is fundamental to modern text-to-image generation models. A potential solution to better aesthetics is direct preference optimization (DPO), which has been applied to diffusion models to improve general image quality including prompt alignment and aesthetics. Popular DPO methods propagate preference labels from clean image pairs to all the intermediate steps along the two generation trajectories. However, preference labels provided in existing datasets are blended with layout and aesthetic opinions, which would disagree with aesthetic preference. Even if aesthetic labels were provided (at substantial cost), it would be hard for the two-trajectory methods to capture nuanced visual differences at different steps. To improve aesthetics economically, this paper uses existing generic preference data and introduces step-by-step preference optimization (SPO) that discards the propagation strategy and allows fine-grained image details to be assessed. Specifically, at each denoising step, we 1) sample a pool of candidates by denoising from a shared noise latent, 2) use a step-aware preference model to find a suitable win-lose pair to supervise the diffusion model, and 3) randomly select one from the pool to initialize the next denoising step. This strategy ensures that diffusion models focus on the subtle, fine-grained visual differences instead of layout aspect. We find that aesthetics can be significantly enhanced by accumulating these improved minor differences. When fine-tuning Stable Diffusion v1.5 and SDXL, SPO yields significant improvements in aesthetics compared with existing DPO methods while not sacrificing image-text alignment compared with vanilla models. Moreover, SPO converges much faster than DPO methods due to the use of more correct preference labels provided by the step-aware preference model. Code and models are available at https://github.com/RockeyCoss/SPO.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_Aesthetic_Post-Training_Diffusion_Models_from_Generic_Preferences_with_Step-by-step_Preference_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liang_Aesthetic_Post-Training_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liang_2025_CVPR,
    author    = {Liang, Zhanhao and Yuan, Yuhui and Gu, Shuyang and Chen, Bohan and Hang, Tiankai and Cheng, Mingxi and Li, Ji and Zheng, Liang},
    title     = {Aesthetic Post-Training Diffusion Models from Generic Preferences with Step-by-step Preference Optimization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13199-13208}
}

raw_affiliation:
[{"affiliation": "The Australian National University", "city": "Canberra", "country": "Australia"}, {"affiliation": "University of Liverpool", "city": "Liverpool", "country": "United Kingdom"}, {"affiliation": "Southeast University", "city": "Nanjing", "country": "China"}, {"affiliation": "Microsoft", "city": "Redmond", "country": "USA"}, {"affiliation": "Microsoft Research Asia", "city": "Beijing", "country": "China"}]

affiliations:
["The Australian National University", "University of Liverpool", "Southeast University", "Microsoft", "Microsoft Research Asia"]


title: Adversarial Diffusion Compression for Real-World Image Super-Resolution
authors: Bin Chen, Gehui Li, Rongyuan Wu, Xindong Zhang, Jie Chen, Jian Zhang, Lei Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28208-28220
cities: ["Beijing", "Shenzhen", "Hong Kong", "Guangzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Adversarial_Diffusion_Compression_for_Real-World_Image_Super-Resolution_CVPR_2025_paper.html

abstract:
Real-world image super-resolution (Real-ISR) aims to reconstruct high-resolution images from low-resolution inputs degraded by complex, unknown processes. While many Stable Diffusion (SD)-based Real-ISR methods have achieved remarkable success, their slow, multi-step inference hinders practical deployment. Recent SD-based one-step networks like OSEDiff and S3Diff alleviate this issue but still incur high computational costs due to their reliance on large pretrained SD models. This paper proposes a novel Real-ISR method, AdcSR, by distilling the one-step diffusion network OSEDiff into a streamlined diffusion-GAN model under our Adversarial Diffusion Compression (ADC) framework. We meticulously examine the modules of OSEDiff, categorizing them into two types: (1) Removable (VAE encoder, prompt extractor, text encoder, etc.) and (2) Prunable (denoising UNet and VAE decoder). Since direct removal and pruning can degrade the model's generation capability, we pretrain our pruned VAE decoder to restore its ability to decode images and employ adversarial distillation to compensate for performance loss. This ADC-based diffusion-GAN hybrid design effectively reduces complexity by 73% in inference time, 78% in computation, and 74% in parameters, while preserving the model's generation capability. Experiments manifest that our proposed AdcSR achieves competitive recovery quality on both synthetic and real-world datasets, offering up to 9.3x speedup over previous one-step diffusion-based methods. Code and models are available at https://github.com/Guaishou74851/AdcSR.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Adversarial_Diffusion_Compression_for_Real-World_Image_Super-Resolution_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Adversarial_Diffusion_Compression_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Bin and Li, Gehui and Wu, Rongyuan and Zhang, Xindong and Chen, Jie and Zhang, Jian and Zhang, Lei},
    title     = {Adversarial Diffusion Compression for Real-World Image Super-Resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28208-28220}
}

raw_affiliation:
[{"affiliation": "School of Electronic and Computer Engineering, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School, Peking University", "city": "Shenzhen", "country": "China"}, {"affiliation": "The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "China"}, {"affiliation": "OPPO Research Institute", "city": "Guangzhou", "country": "China"}]

affiliations:
["School of Electronic and Computer Engineering, Peking University", "Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School, Peking University", "The Hong Kong Polytechnic University", "OPPO Research Institute"]


title: DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery
authors: Utkarsh Mall, Cheng Perng Phoo, Mia Chiquier, Bharath Hariharan, Kavita Bala, Carl Vondrick
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29258-29267
affiliations: ["Columbia University", "Cornell University"]
cities: ["New York", "Ithaca"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mall_DiSciPLE_Learning_Interpretable_Programs_for_Scientific_Visual_Discovery_CVPR_2025_paper.html

abstract:
Visual data is used in numerous different scientific workflows ranging from remote sensing to ecology. As the amount of observation data increases, the challenge is not just to make accurate predictions but also to understand the underlying mechanisms for those predictions. Good interpretation is important in scientific workflows, as it allows for better decision-making by providing insights into the data. This paper introduces an automatic way of obtaining such interpretable-by-design models, by learning programs that interleave neural networks. We propose DiSciPLE (Discovering Scientific Programs using LLMs and Evolution) an evolutionary algorithm that leverages common sense and prior knowledge of large language models (LLMs) to create Python programs explaining visual data. Additionally, we propose two improvements: a program critic and a program simplifier to improve our method further to synthesize good programs. On three different real world problems, DiSciPLE learns state-of-the-art programs on novel tasks with no prior literature. For example, we can learn programs with 35% lower error than the closest non-interpretable baseline for population density estimation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mall_DiSciPLE_Learning_Interpretable_Programs_for_Scientific_Visual_Discovery_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mall_DiSciPLE_Learning_Interpretable_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mall_2025_CVPR,
    author    = {Mall, Utkarsh and Phoo, Cheng Perng and Chiquier, Mia and Hariharan, Bharath and Bala, Kavita and Vondrick, Carl},
    title     = {DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29258-29267}
}

raw_affiliation:
[{"affiliation": "Columbia University", "city": "New York", "country": "USA"}, {"affiliation": "Cornell University", "city": "Ithaca", "country": "USA"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26887-26898
affiliations: ["SenseTime Research", "S-Lab, Nanyang Technological University"]
cities: ["Beijing", "Singapore"]
countries: ["China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_SOLAMI_Social_Vision-Language-Action_Modeling_for_Immersive_Interaction_with_3D_Autonomous_CVPR_2025_paper.html

title:
SOLAMI: Social Vision-Language-Action Modeling for Immersive Interaction with 3D Autonomous Characters

authors:
Jianping Jiang, Weiye Xiao, Zhengyu Lin, Huaizhong Zhang, Tianxiang Ren, Yang Gao, Zhiqian Lin, Zhongang Cai, Lei Yang, Ziwei Liu

abstract:
Human beings are social animals. How to equip 3D autonomous characters with similar social intelligence that can perceive, understand and interact with humans remains an open yet foundamental problem. In this paper, we introduce SOLAMI, the first end-to-end Social vision-Language-Action (VLA) Modeling framework for Immersive interaction with 3D autonomous characters. Specifically, SOLAMI builds 3D autonomous characters from three aspects: 1) Social VLA Architecture: We propose a unified social VLA framework to generate multimodal response (speech and motion) based on the user's multimodal input to drive the character for social interaction. 2) Interactive Multimodal Data: We present SynMSI, a synthetic  multimodal social interaction dataset generated by an automatic pipeline using only existing motion datasets to address the issue of data scarcity. 3) Immersive VR Interface: We develop a VR interface that enables users to immersively interact with these characters driven by various architectures. Extensive quantitative experiments and user studies demonstrate that our framework leads to more precise and natural character responses (in both speech and motion) that align with user expectations with lower latency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_SOLAMI_Social_Vision-Language-Action_Modeling_for_Immersive_Interaction_with_3D_Autonomous_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_SOLAMI_Social_Vision-Language-Action_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Jianping and Xiao, Weiye and Lin, Zhengyu and Zhang, Huaizhong and Ren, Tianxiang and Gao, Yang and Lin, Zhiqian and Cai, Zhongang and Yang, Lei and Liu, Ziwei},
    title     = {SOLAMI: Social Vision-Language-Action Modeling for Immersive Interaction with 3D Autonomous Characters},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26887-26898}
}

raw_affiliation:
[{"affiliation": "SenseTime Research", "city": "Beijing", "country": "China"}, {"affiliation": "S-Lab, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]


authors: Ming Sun, Rui Wang, Zixuan Zhu, Lihua Jing, Yuanfang Guo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30692-30701
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_EntropyMark_Towards_More_Harmless_Backdoor_Watermark_via_Entropy-based_Constraint_for_CVPR_2025_paper.html

title:
EntropyMark: Towards More Harmless Backdoor Watermark via Entropy-based Constraint for Open-source Dataset Copyright Protection

abstract:
High-quality open-source datasets are essential for advancing deep neural networks. However, the unauthorized commercial use of these datasets has raised significant concerns about copyright protection. One promising approach is backdoor watermark-based dataset ownership verification (BW-DOV), in which dataset protectors implant specific backdoors into illicit models through dataset watermarking, enabling the tracing of these models through abnormal prediction behaviors. Unfortunately, the targeted nature of these BW-DOV methods can be maliciously exploited, potentially leading to harmful side effects. While existing harmless methods attempt to mitigate these risks, watermarked datasets can still negatively affect prediction results, partially compromising dataset functionality. In this paper, we propose a more harmless backdoor watermark, called EntropyMark, which improves prediction confidence without altering the final prediction results. For this purpose, an entropy-based constraint is introduced to regulate the probability distribution. Specifically, we design an iterative clean-label dataset watermarking framework. Our framework employs gradient matching and adaptive data selection to optimize backdoor injection. In parallel, we introduce a hypothesis test method grounded in entropy inconsistency to verify dataset ownership. Extensive experiments on benchmark datasets demonstrate the effectiveness, transferability, and defense resistance of our approach.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_EntropyMark_Towards_More_Harmless_Backdoor_Watermark_via_Entropy-based_Constraint_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_EntropyMark_Towards_More_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Ming and Wang, Rui and Zhu, Zixuan and Jing, Lihua and Guo, Yuanfang},
    title     = {EntropyMark: Towards More Harmless Backdoor Watermark via Entropy-based Constraint for Open-source Dataset Copyright Protection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30692-30701}
}

raw_affiliation:
[{"affiliation": "Institute of Information Engineering, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Cyber Security, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer Science and Engineering, Beihang University", "city": "Beijing", "country": "China"}]

affiliations:
["Institute of Information Engineering, Chinese Academy of Sciences", "School of Cyber Security, University of Chinese Academy of Sciences", "School of Computer Science and Engineering, Beihang University"]


title: Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29558-29568
cities: ["Hong Kong", "Shenzhen", "Hong Kong", "Shanghai"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xiao_Adaptive_Markup_Language_Generation_for_Contextually-Grounded_Visual_Document_Understanding_CVPR_2025_paper.html

authors:
Han Xiao, Yina Xie, Guanxin Tan, Yinghao Chen, Rui Hu, Ke Wang, Aojun Zhou, Hao Li, Hao Shao, Xudong Lu, Peng Gao, Yafei Wen, Xiaoxin Chen, Shuai Ren, Hongsheng Li

abstract:
Visual Document Understanding has become essential with the increase of text-rich visual content. This field poses significant challenges due to the need for effective integration of visual perception and textual comprehension, particularly across diverse document types with complex layouts. Moreover, existing fine-tuning datasets for this domain often fall short in providing the detailed contextual information for robust understanding, leading to hallucinations and limited comprehension of spatial relationships among visual elements. To address these challenges, we propose an innovative pipeline that utilizes adaptive generation of markup languages, such as Markdown, JSON, HTML, and TiKZ, to build highly structured document representations and deliver contextually-grounded responses. We introduce two fine-grained structured datasets: DocMark-Pile, comprising approximately 3.8M pretraining data pairs for document parsing, and DocMark-Instruct, featuring 624k fine-tuning data annotations for grounded instruction following.Extensive experiments demonstrate that our proposed model significantly outperforms existing state-of-the-art MLLMs across a range of visual document understanding benchmarks, facilitating advanced reasoning and comprehension capabilities in complex visual scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiao_Adaptive_Markup_Language_Generation_for_Contextually-Grounded_Visual_Document_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiao_Adaptive_Markup_Language_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xiao_2025_CVPR,
    author    = {Xiao, Han and Xie, Yina and Tan, Guanxin and Chen, Yinghao and Hu, Rui and Wang, Ke and Zhou, Aojun and Li, Hao and Shao, Hao and Lu, Xudong and Gao, Peng and Wen, Yafei and Chen, Xiaoxin and Ren, Shuai and Li, Hongsheng},
    title     = {Adaptive Markup Language Generation for Contextually-Grounded Visual Document Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29558-29568}
}

raw_affiliation:
[{"affiliation": "CUHK MMLab", "city": "Hong Kong", "country": "China"}, {"affiliation": "vivo AI Lab", "city": "Shenzhen", "country": "China"}, {"affiliation": "CPII under InnoHK", "city": "Hong Kong", "country": "China"}, {"affiliation": "Shanghai AI Lab & Shenzhen Institute of Advanced Technology, CAS", "city": "Shanghai", "country": "China"}]

affiliations:
["CUHK MMLab", "vivo AI Lab", "CPII under InnoHK", "Shanghai AI Lab & Shenzhen Institute of Advanced Technology, CAS"]


title: BARD-GS: Blur-Aware Reconstruction of Dynamic Scenes via Gaussian Splatting
authors: Yiren Lu, Yunlai Zhou, Disheng Liu, Tuo Liang, Yu Yin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16532-16542
raw_affiliation: [{"affiliation": "Case Western Reserve University", "city": "Cleveland", "country": "USA"}]
affiliations: ["Case Western Reserve University"]
cities: ["Cleveland"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lu_BARD-GS_Blur-Aware_Reconstruction_of_Dynamic_Scenes_via_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
3D Gaussian Splatting (3DGS) has shown remarkable potential for static scene reconstruction, and recent advancements have extended its application to dynamic scenes. However, the quality of reconstructions depends heavily on high-quality input images and precise camera poses, which is not that trivial to fulfill in the real-world scenarios. Capturing dynamic scenes with handheld monocular cameras, for instance, typically involves simultaneous movement of both the camera and objects within a single exposure. This combined motion frequently results in image blur that existing methods cannot adequately handle. To address these challenges, we introduce BARD-GS, a novel approach for robust dynamic scene reconstruction that effectively handles blurry inputs and imprecise camera poses. Our method comprises two main components: 1) camera motion deblurring and 2) object motion deblurring. By explicitly decomposing motion blur into camera motion blur and object motion blur and modeling them separately, we achieve significantly improved rendering results in dynamic regions. In addition, we collect a real-world motion blur dataset of dynamic scenes to evaluate our approach. Extensive experiments demonstrate that BARD-GS effectively reconstructs high-quality dynamic scenes under realistic conditions, significantly outperforming existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_BARD-GS_Blur-Aware_Reconstruction_of_Dynamic_Scenes_via_Gaussian_Splatting_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Lu_2025_CVPR,
    author    = {Lu, Yiren and Zhou, Yunlai and Liu, Disheng and Liang, Tuo and Yin, Yu},
    title     = {BARD-GS: Blur-Aware Reconstruction of Dynamic Scenes via Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16532-16542}
}


title: SALAD: Skeleton-aware Latent Diffusion for Text-driven Motion Generation and Editing
authors: Seokhyeon Hong, Chaelin Kim, Serin Yoon, Junghyun Nam, Sihun Cha, Junyong Noh
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7158-7168
raw_affiliation: [{"affiliation": "Visual Media Lab, KAIST", "city": "Daejeon", "country": "South Korea"}]
affiliations: ["Visual Media Lab, KAIST"]
cities: ["Daejeon"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hong_SALAD_Skeleton-aware_Latent_Diffusion_for_Text-driven_Motion_Generation_and_Editing_CVPR_2025_paper.html

abstract:
Text-driven motion generation has advanced significantly with the rise of denoising diffusion models. However, previous methods often oversimplify representations for the skeletal joints, temporal frames, and textual words, limiting their ability to fully capture the information within each modality and their interactions. Moreover, when using pre-trained models for downstream tasks, such as editing, they typically require additional efforts, including manual interventions, optimization, or fine-tuning. In this paper, we introduce a skeleton-aware latent diffusion (SALAD), a model that explicitly captures the intricate inter-relationships between joints, frames, and words. Furthermore, by leveraging cross-attention maps produced during the generation process, we enable the attention-based zero-shot text-driven motion editing using a pre-trained SALAD model, requiring no additional user input beyond text prompts. Our approach significantly outperforms previous methods in terms of text-motion alignment without compromising generation quality, and demonstrates practical versatility by providing diverse editing capabilities beyond generation. Code is available at project page.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hong_SALAD_Skeleton-aware_Latent_Diffusion_for_Text-driven_Motion_Generation_and_Editing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hong_SALAD_Skeleton-aware_Latent_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hong_2025_CVPR,
    author    = {Hong, Seokhyeon and Kim, Chaelin and Yoon, Serin and Nam, Junghyun and Cha, Sihun and Noh, Junyong},
    title     = {SALAD: Skeleton-aware Latent Diffusion for Text-driven Motion Generation and Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7158-7168}
}


title: Towards Universal AI-Generated Image Detection by Variational Information Bottleneck Network
authors: Haifeng Zhang, Qinghui He, Xiuli Bi, Weisheng Li, Bo Liu, Bin Xiao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23828-23837
affiliations: ["Chongqing University of Posts and Telecommunications"]
cities: ["Chongqing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Towards_Universal_AI-Generated_Image_Detection_by_Variational_Information_Bottleneck_Network_CVPR_2025_paper.html

abstract:
The rapid advancement of generative models has significantly improved the quality of generated images. Meanwhile, it challenges information authenticity and credibility. Current generated image detection methods based on large-scale pre-trained multimodal models have achieved impressive results. Although these models provide abundant features, the authentication task-related features are often submerged. Consequently, those authentication task-irrelated features cause models to learn superficial biases, thereby harming their generalization performance across different model genera (e.g., GANs and Diffusion Models). To this end, we proposed VIB-Net, which uses Variational Information Bottlenecks to enforce authentication task-related feature learning. We tested and analyzed the proposed method and existing methods on samples generated by 17 different generative models. Compared to SOTA methods, VIB-Net achieved a 5.55% improvement in mAP and a 9.33% increase in accuracy. Notably, in generalization tests on unseen generative models from different series, VIB-Net improved mAP by 12.48% and accuracy by 23.59% over SOTA methods. The code is available at https://github.com/oceanzhf/VIBAIGCDetect.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Towards_Universal_AI-Generated_Image_Detection_by_Variational_Information_Bottleneck_Network_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Haifeng and He, Qinghui and Bi, Xiuli and Li, Weisheng and Liu, Bo and Xiao, Bin},
    title     = {Towards Universal AI-Generated Image Detection by Variational Information Bottleneck Network},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23828-23837}
}

raw_affiliation:
[{"affiliation": "Chongqing University of Posts and Telecommunications", "city": "Chongqing", "country": "China"}]


title: HSI: A Holistic Style Injector for Arbitrary Style Transfer
authors: Shuhao Zhang, Hui Kang, Yang Liu, Fang Mei, Hongjuan Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23433-23442
cities: ["Changchun", "Changchun", "Changchun"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_HSI_A_Holistic_Style_Injector_for_Arbitrary_Style_Transfer_CVPR_2025_paper.html

abstract:
Attention-based arbitrary style transfer methods have gained significant attention recently due to their impressive ability to synthesize style details. However, the point-wise matching within the attention mechanism may overly focus on local patterns such that neglect the remarkable global features of style images. Additionally, when processing large images, the quadratic complexity of the attention mechanism will bring high computational load. To alleviate above problems, we propose Holistic Style Injector (HSI), a novel attention-style transformation module to deliver artistic expression of target style. Specifically, HSI performs stylization only based on global style representation that is more in line with the characteristics of style transfer, to avoid generating local disharmonious patterns in stylized images. Moreover, we propose a dual relation learning mechanism inside the HSI to dynamically render images by leveraging semantic similarity in content and style, ensuring the stylized images preserve the original content and improve style fidelity. Note that the proposed HSI achieves linear computational complexity because it establishes feature mapping through element-wise multiplication rather than matrix multiplication. Qualitative and quantitative results demonstrate that our method outperforms state-of-the-art approaches in both effectiveness and efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_HSI_A_Holistic_Style_Injector_for_Arbitrary_Style_Transfer_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_HSI_A_Holistic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Shuhao and Kang, Hui and Liu, Yang and Mei, Fang and Li, Hongjuan},
    title     = {HSI: A Holistic Style Injector for Arbitrary Style Transfer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23433-23442}
}

raw_affiliation:
[{"affiliation": "College of Computer Science and Technology, Jilin University", "city": "Changchun", "country": "China"}, {"affiliation": "Academic Affairs Office, Jilin University of Arts", "city": "Changchun", "country": "China"}, {"affiliation": "Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University", "city": "Changchun", "country": "China"}]

affiliations:
["College of Computer Science and Technology, Jilin University", "Academic Affairs Office, Jilin University of Arts", "Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University"]


title: LookingGlass: Generative Anamorphoses via Laplacian Pyramid Warping
authors: Pascal Chang, Sergio Sancho, Jingwei Tang, Markus Gross, Vinicius Azevedo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24-33
affiliations: ["ETH Z \u00a8urich", "DisneyResearch|Studios"]
cities: ["Zurich", "Unknown"]
countries: ["Switzerland", "Unknown"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chang_LookingGlass_Generative_Anamorphoses_via_Laplacian_Pyramid_Warping_CVPR_2025_paper.html

abstract:
Anamorphosis refers to a category of images that are intentionally distorted, making them unrecognizable when viewed directly. Their true form only reveals itself when seen from a specific viewpoint, which can be through some catadioptric device like a mirror or a lens. While the construction of these mathematical devices can be traced back to as early as the 17th century, they are only interpretable when viewed from a specific vantage point and tend to lose meaning when seen normally. In this paper, we revisit these famous optical illusions with a generative twist. With the help of latent rectified flow models, we propose a method to create anamorphic images that still retain a valid interpretation when viewed directly. To this end, we introduce Laplacian Pyramid Warping, a frequency-aware image warping technique key to generating high-quality visuals. Our work extends Visual Anagrams (Geng et al. 2024) to latent space models and to a wider range of spatial transforms, enabling the creation of novel generative perceptual illusions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chang_LookingGlass_Generative_Anamorphoses_via_Laplacian_Pyramid_Warping_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chang_LookingGlass_Generative_Anamorphoses_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Chang_2025_CVPR,
    author    = {Chang, Pascal and Sancho, Sergio and Tang, Jingwei and Gross, Markus and Azevedo, Vinicius},
    title     = {LookingGlass: Generative Anamorphoses via Laplacian Pyramid Warping},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24-33}
}

raw_affiliation:
[{"affiliation": "ETH Z \u00a8urich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "DisneyResearch|Studios", "city": "Unknown", "country": "Unknown"}]


title: V2V3D: View-to-View Denoised 3D Reconstruction for Light Field Microscopy
authors: Jiayin Zhao, Zhenqi Fu, Tao Yu, Hui Qiao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26451-26461
affiliations: ["Tsinghua University", "Shanghai AI Laboratory"]
cities: ["Beijing", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_V2V3D_View-to-View_Denoised_3D_Reconstruction_for_Light_Field_Microscopy_CVPR_2025_paper.html

abstract:
Light field microscopy (LFM) has gained significant attention due to its ability to capture snapshot-based, large-scale 3D fluorescence images. However, existing LFM reconstruction algorithms are highly sensitive to sensor noise or require hard-to-get ground-truth annotated data for training. To address these challenges, this paper introduces V2V3D, an unsupervised view2view-based framework that establishes a new paradigm for joint optimization of image denoising and 3D reconstruction in a unified architecture. We assume that the LF images are derived from a consistent 3D signal, with the noise in each view being independent. This enables V2V3D to incorporate the principle of noise2noise for effective denoising. To enhance the recovery of high-frequency details, we propose a novel wave-optics-based feature alignment technique, which transforms the point spread function, used for forward propagation in wave optics, into convolution kernels specifically designed for feature alignment. Moreover, we introduce an LFM dataset containing LF images and their corresponding 3D intensity volumes. Extensive experiments demonstrate that our approach achieves high computational efficiency and outperforms the other state-of-the-art methods. These advancements position V2V3D as a promising solution for 3D imaging under challenging conditions. Our code and dataset will be publicly accessible at https://joey1998hub.github.io/V2V3D/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_V2V3D_View-to-View_Denoised_3D_Reconstruction_for_Light_Field_Microscopy_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_V2V3D_View-to-View_Denoised_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Jiayin and Fu, Zhenqi and Yu, Tao and Qiao, Hui},
    title     = {V2V3D: View-to-View Denoised 3D Reconstruction for Light Field Microscopy},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26451-26461}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}]


title: DiN: Diffusion Model for Robust Medical VQA with Semantic Noisy Labels
authors: Erjian Guo, Zhen Zhao, Zicheng Wang, Tong Chen, Yunyi Liu, Luping Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14337-14346
affiliations: ["University of Sydney", "Shanghai AI Laboratory"]
cities: ["Sydney", "Shanghai"]
countries: ["Australia", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_DiN_Diffusion_Model_for_Robust_Medical_VQA_with_Semantic_Noisy_CVPR_2025_paper.html

abstract:
Medical Visual Question Answering (Med-VQA) systems benefit the interpretation of medical images containing critical clinical information. However, the challenge of noisy labels and limited high-quality datasets remains underexplored. To address this, we establish the first benchmark for noisy labels in Med-VQA by simulating human mislabeling with semantically designed noise types.  More importantly, we introduce the DiN framework, which leverages a diffusion model to handle noisy labels in Med-VQA. Unlike the dominant classification-based VQA approaches that directly predict answers, our Answer Diffuser (AD) module employs a coarse-to-fine process, refining answer candidates with a diffusion model for improved accuracy. The Answer Condition Generator (ACG) further enhances this process by generating task-specific conditional information via integrating answer embeddings with fused image-question features. To address label noise, our Noisy Label Refinement(NLR) module introduces a robust loss function and dynamic answer adjustment to further boost the performance of the AD module. Our DiN framework consistently outperforms existing methods across multiple benchmarks with varying noise levels.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_DiN_Diffusion_Model_for_Robust_Medical_VQA_with_Semantic_Noisy_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Erjian and Zhao, Zhen and Wang, Zicheng and Chen, Tong and Liu, Yunyi and Zhou, Luping},
    title     = {DiN: Diffusion Model for Robust Medical VQA with Semantic Noisy Labels},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14337-14346}
}

raw_affiliation:
[{"affiliation": "University of Sydney", "city": "Sydney", "country": "Australia"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}]


title: Splatter-360: Generalizable 360 Gaussian Splatting for Wide-baseline Panoramic Images
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21590-21599
affiliations: ["Tsinghua University", "Baidu VIS", "Zhejiang University"]
cities: ["Beijing", "Beijing", "Hangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Splatter-360_Generalizable_360_Gaussian_Splatting_for_Wide-baseline_Panoramic_Images_CVPR_2025_paper.html

authors:
Zheng Chen, Chenming Wu, Zhelun Shen, Chen Zhao, Weicai Ye, Haocheng Feng, Errui Ding, Song-Hai Zhang

abstract:
Wide-baseline panoramic images are frequently used in applications like VR and simulations to minimize capturing labor costs and storage needs. However, synthesizing novel views from these panoramic images in real time remains a significant challenge, especially due to panoramic imagery's high resolution and inherent distortions. Although existing 3D Gaussian splatting (3DGS) methods can produce photo-realistic views under narrow baselines, they often overfit the training views when dealing with wide-baseline panoramic images due to the difficulty in learning precise geometry from sparse 360^ \circ  views. This paper presents Splatter-360, a novel end-to-end generalizable 3DGS framework designed to handle wide-baseline panoramic images. Unlike previous approaches, Splatter-360 performs multi-view matching directly in the spherical domain by constructing a spherical cost volume through a spherical sweep algorithm, enhancing the network's depth perception and geometry estimation. Additionally, we introduce a 3D-aware bi-projection encoder to mitigate the distortions inherent in panoramic images and integrate cross-view attention to improve feature interactions across multiple viewpoints. This enables robust 3D-aware feature representations and real-time rendering capabilities. Experimental results on the HM3D and Replica demonstrate that Splatter-360 significantly outperforms state-of-the-art NeRF and 3DGS methods (e.g., PanoGRF, MVSplat, DepthSplat, and HiSplat) in both synthesis quality and generalization performance for wide-baseline panoramic images. Code and trained models are available at https://3d-aigc.github.io/Splatter-360/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Splatter-360_Generalizable_360_Gaussian_Splatting_for_Wide-baseline_Panoramic_Images_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Splatter-360_Generalizable_360_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Zheng and Wu, Chenming and Shen, Zhelun and Zhao, Chen and Ye, Weicai and Feng, Haocheng and Ding, Errui and Zhang, Song-Hai},
    title     = {Splatter-360: Generalizable 360 Gaussian Splatting for Wide-baseline Panoramic Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21590-21599}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Baidu VIS", "city": "Beijing", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]


title: ShowMak3r: Compositional TV Show Reconstruction
authors: Sangmin Kim, Seunguk Do, Jaesik Park
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 864-874
affiliations: ["Seoul National University, Republic of Korea"]
cities: ["Seoul"]
countries: ["Republic of Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_ShowMak3r_Compositional_TV_Show_Reconstruction_CVPR_2025_paper.html

abstract:
Reconstructing dynamic radiance fields from video clips is challenging, especially when entertainment videos like TV shows are given. Many challenges make the reconstruction difficult due to (1) actors occluding with each other and having diverse facial expressions, (2) cluttered stages, and (3) small baseline views or sudden shot changes. To address these issues, we present ShowMak3r, a comprehensive reconstruction pipeline that allows the editing of scenes like how video clips are made in a production control room. In ShowMak3r, a 3DLocator module locates recovered actors on the stage using depth prior, and estimates unseen human poses via interpolation. The proposed ShotMatcher module then tracks the actors under shot changes. Furthermore, ShowMak3r introduces a face-fitting network that dynamically recovers the actors' expressions. Experiments on Sitcoms3D dataset show that our pipeline can reassemble TV show scenes with new cameras at different timestamps. We also demonstrate that ShowMak3r enables interesting applications such as synthetic shot-making, actor relocation, insertion, deletion, and pose manipulation. Project page : https://nstar1125.github.io/showmak3r

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_ShowMak3r_Compositional_TV_Show_Reconstruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_ShowMak3r_Compositional_TV_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Sangmin and Do, Seunguk and Park, Jaesik},
    title     = {ShowMak3r: Compositional TV Show Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {864-874}
}

raw_affiliation:
[{"affiliation": "Seoul National University, Republic of Korea", "city": "Seoul", "country": "Republic of Korea"}]


title: CADRef: Robust Out-of-Distribution Detection via Class-Aware Decoupled Relative Feature Leveraging
authors: Zhiwei Ling, Yachen Chang, Hailiang Zhao, Xinkui Zhao, Kingsum Chow, Shuiguang Deng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4968-4977
raw_affiliation: [{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]
affiliations: ["Zhejiang University"]
cities: ["Hangzhou"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ling_CADRef_Robust_Out-of-Distribution_Detection_via_Class-Aware_Decoupled_Relative_Feature_Leveraging_CVPR_2025_paper.html

abstract:
Deep neural networks (DNNs) have been widely criticized for their overconfidence when dealing with out-of-distribution (OOD) samples, highlighting the critical need for effective OOD detection to ensure the safe deployment of DNNs in real-world settings. Existing post-hoc OOD detection methods primarily enhance the discriminative power of logit-based approaches by reshaping sample features, yet they often neglect critical information inherent in the features themselves. In this paper, we propose the \underline C lass-\underline A ware \underline Re lative \underline F eature-based method (CARef), which utilizes the error between a sample's feature and its class-aware average feature as a discriminative criterion. To further refine this approach, we introduce the \underline C lass-\underline A ware \underline D ecoupled \underline Re lative \underline F eature-based method (CADRef), which decouples sample features based on the alignment of signs between the relative feature and corresponding model weights, enhancing the discriminative capabilities of CARef.Extensive experimental results across multiple datasets and models demonstrate that both proposed methods exhibit effectiveness and robustness in OOD detection compared to state-of-the-art methods. Specifically, our two methods outperform the best baseline by 2.82% and 3.27% in AUROC, with improvements of 4.03% and 6.32% in FPR95, respectively.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ling_CADRef_Robust_Out-of-Distribution_Detection_via_Class-Aware_Decoupled_Relative_Feature_Leveraging_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ling_CADRef_Robust_Out-of-Distribution_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ling_2025_CVPR,
    author    = {Ling, Zhiwei and Chang, Yachen and Zhao, Hailiang and Zhao, Xinkui and Chow, Kingsum and Deng, Shuiguang},
    title     = {CADRef: Robust Out-of-Distribution Detection via Class-Aware Decoupled Relative Feature Leveraging},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4968-4977}
}


title: S^3-Face: SSS-Compliant Facial Reflectance Estimation via Diffusion Priors
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16051-16060
affiliations: ["Shanghai Jiao Tong University", "Imperial College London", "Xueshen AI"]
cities: ["Shanghai", "London", "Unknown"]
countries: ["China", "United Kingdom", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ren_S3-Face_SSS-Compliant_Facial_Reflectance_Estimation_via_Diffusion_Priors_CVPR_2025_paper.html

authors:
Xingyu Ren, Jiankang Deng, Yuhao Cheng, Wenhan Zhu, Yichao Yan, Xiaokang Yang, Stefanos Zafeiriou, Chao Ma

abstract:
Recent 3D face reconstruction methods have made remarkable advancements, yet achieving high-quality facial reflectance from monocular input remains challenging. Existing methods rely on the light-stage captured data to learn facial reflectance models. However, limited subject diversity in these datasets poses challenges in achieving good generalization and broad applicability. This motivates us to explore whether the extensive priors captured in recent generative diffusion models (e.g., Stable Diffusion) can enable more generalizable facial reflectance estimation as these models have been pre-trained on large-scale internet image collections containing rich visual patterns. In this paper, we introduce the use of Stable Diffusion as a prior for facial reflectance estimation, achieving robust results with minimal captured data for fine-tuning. We present S^3-Face, a comprehensive framework capable of producing SSS-compliant skin reflectance from in-the-wild images. Our method adopts a two-stage training approach: in the first stage, DSN-Net is trained to predict diffuse albedo, specular albedo, and normal maps from in-the-wild images using a novel joint reflectance attention module. In the second stage, HM-Net is trained to generate hemoglobin and melanin maps based on the diffuse albedo predicted in the first stage, yielding SSS-compliant and detailed reflectance maps. Extensive experiments demonstrate that our method achieves strong generalization and produces high-fidelity, SSS-compliant facial reflectance reconstructions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ren_S3-Face_SSS-Compliant_Facial_Reflectance_Estimation_via_Diffusion_Priors_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ren_S3-Face_SSS-Compliant_Facial_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Ren_2025_CVPR,
    author    = {Ren, Xingyu and Deng, Jiankang and Cheng, Yuhao and Zhu, Wenhan and Yan, Yichao and Yang, Xiaokang and Zafeiriou, Stefanos and Ma, Chao},
    title     = {S{\textasciicircum}3-Face: SSS-Compliant Facial Reflectance Estimation via Diffusion Priors},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16051-16060}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Imperial College London", "city": "London", "country": "United Kingdom"}, {"affiliation": "Xueshen AI", "city": "Unknown", "country": "Unknown"}]


title: FSBench: A Figure Skating Benchmark for Advancing Artistic Sports Understanding
authors: Rong Gao, Xin Liu, Zhuozhao Hu, Bohao Xing, Baiqiang Xia, Zitong Yu, Heikki Kälviäinen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13595-13605
cities: ["Lappeenranta", "Tianjin", "Helsinki", "Dongguan", "Troy", "Brno"]
countries: ["Finland", "China", "Finland", "China", "USA", "Czech Republic"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_FSBench_A_Figure_Skating_Benchmark_for_Advancing_Artistic_Sports_Understanding_CVPR_2025_paper.html

abstract:
Figure skating, known as the "Art on Ice," is among the most artistic sports, challenging to understand due to its blend of technical elements (like jumps and spins) and overall artistic expression. Existing figure skating datasets mainly focus on single tasks, such as action recognition or scoring, lacking comprehensive annotations for both technical and artistic evaluation. Current sports research is largely centered on ball games, with limited relevance to artistic sports like figure skating. To address this, we introduce FSAnno, a large-scale dataset advancing artistic sports understanding through figure skating. FSAnno includes an open-access training and test dataset, alongside a benchmark dataset, FSBench, for fair model evaluation. FSBench consists of FSBench-Text, with multiple-choice questions and explanations, and FSBench-Motion, containing multimodal data and Question and Answer (QA) pairs, supporting tasks from technical analysis to performance commentary. Initial tests on FSBench reveal significant limitations in existing models' understanding of artistic sports. We hope FSBench will become a key tool for evaluating and enhancing model comprehension of figure skating. All data, models, and more details are available at: https://github.com/Moomin-Fin/Ano.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_FSBench_A_Figure_Skating_Benchmark_for_Advancing_Artistic_Sports_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_FSBench_A_Figure_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gao_2025_CVPR,
    author    = {Gao, Rong and Liu, Xin and Hu, Zhuozhao and Xing, Bohao and Xia, Baiqiang and Yu, Zitong and K\"alvi\"ainen, Heikki},
    title     = {FSBench: A Figure Skating Benchmark for Advancing Artistic Sports Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13595-13605}
}

raw_affiliation:
[{"affiliation": "Lappeenranta-Lahti University of T echnology LUT", "city": "Lappeenranta", "country": "Finland"}, {"affiliation": "Tianjin University", "city": "Tianjin", "country": "China"}, {"affiliation": "AMD Silo AI", "city": "Helsinki", "country": "Finland"}, {"affiliation": "Great Bay University", "city": "Dongguan", "country": "China"}, {"affiliation": "Rensselaer Polytechnic Institute", "city": "Troy", "country": "USA"}, {"affiliation": "Brno University of T echnology", "city": "Brno", "country": "Czech Republic"}]

affiliations:
["Lappeenranta-Lahti University of T echnology LUT", "Tianjin University", "AMD Silo AI", "Great Bay University", "Rensselaer Polytechnic Institute", "Brno University of T echnology"]


title: Keep the Balance: A Parameter-Efficient Symmetrical Framework for RGB+X Semantic Segmentation
authors: Jiaxin Cai, Jingze Su, Qi Li, Wenjie Yang, Shu Wang, Tiesong Zhao, Shengfeng He, Wenxi Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10587-10598
affiliations: ["Fuzhou University", "Singapore Management University"]
cities: ["Fuzhou", "Singapore"]
countries: ["China", "Singapore"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cai_Keep_the_Balance_A_Parameter-Efficient_Symmetrical_Framework_for_RGBX_Semantic_CVPR_2025_paper.html

abstract:
Multimodal semantic segmentation is a critical challenge in computer vision, with early methods suffering from high computational costs and limited transferability due to full fine-tuning of RGB-based pre-trained parameters. Recent studies, while leveraging additional modalities as supplementary prompts to RGB, still predominantly rely on RGB, which restricts the full potential of other modalities. To address these issues, we propose a novel symmetric parameter-efficient fine-tuning framework for multimodal segmentation, featuring with a modality-aware prompting and adaptation scheme, to simultaneously adapt the capabilities of a powerful pre-trained model to both RGB and X modalities. Furthermore, prevalent approaches use the global cross-modality correlations of attention mechanism for modality fusion, which inadvertently introduces noise across modalities. To mitigate this noise, we propose a dynamic sparse cross-modality fusion module to facilitate effective and efficient cross-modality fusion. To further strengthen the above two modules, we propose a training strategy that leverages accurately predicted dual-modality results to self-teach the single-modality outcomes. In comprehensive experiments, we demonstrate that our method outperforms previous state-of-the-art approaches across six multimodal segmentation scenarios with minimal computation cost.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cai_Keep_the_Balance_A_Parameter-Efficient_Symmetrical_Framework_for_RGBX_Semantic_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cai_Keep_the_Balance_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cai_2025_CVPR,
    author    = {Cai, Jiaxin and Su, Jingze and Li, Qi and Yang, Wenjie and Wang, Shu and Zhao, Tiesong and He, Shengfeng and Liu, Wenxi},
    title     = {Keep the Balance: A Parameter-Efficient Symmetrical Framework for RGB+X Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10587-10598}
}

raw_affiliation:
[{"affiliation": "Fuzhou University", "city": "Fuzhou", "country": "China"}, {"affiliation": "Singapore Management University", "city": "Singapore", "country": "Singapore"}]


title: VideoDirector: Precise Video Editing via Text-to-Video Models
authors: Yukun Wang, Longguang Wang, Zhiyuan Ma, Qibin Hu, Kai Xu, Yulan Guo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2589-2598
cities: ["Shenzhen", "Beijing", "Changsha"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_VideoDirector_Precise_Video_Editing_via_Text-to-Video_Models_CVPR_2025_paper.html

abstract:
Despite the typical inversion-then-editing paradigm using text-to-image (T2I) models has demonstrated promising results, directly extending it to text-to-video (T2V) models still suffers severe artifacts such as color flickering and content distortion. Consequently, current video editing methods primarily rely on T2I models, which inherently lack temporal-coherence generative ability, often resulting in inferior editing results.  In this paper, we attribute the failure of the typical editing paradigm to: 1) Tightly Spatial-temporal Coupling. The vanilla pivotal-based inversion strategy struggles to disentangle spatial-temporal information in the video diffusion model; 2) Complicated Spatial-temporal Layout. The vanilla cross-attention control strategy is deficient in preserving the unedited content. To address these limitations, we propose a spatial-temporal decoupled guidance (STDG) and multi-frame null-text optimization strategy to provide pivotal temporal cues for more precise pivotal inversion. Furthermore, we introduce a self-attention control strategy to maintain higher fidelity for precise partial content editing. Experimental results demonstrate that our method (termed VideoDirector) effectively harnesses the powerful temporal generation capabilities of T2V models, producing edited videos with state-of-the-art performance in accuracy, motion smoothness, realism, and fidelity to unedited content.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_VideoDirector_Precise_Video_Editing_via_Text-to-Video_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_VideoDirector_Precise_Video_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yukun and Wang, Longguang and Ma, Zhiyuan and Hu, Qibin and Xu, Kai and Guo, Yulan},
    title     = {VideoDirector: Precise Video Editing via Text-to-Video Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2589-2598}
}

raw_affiliation:
[{"affiliation": "Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "National University of Defense Technology", "city": "Changsha", "country": "China"}]

affiliations:
["Shenzhen Campus of Sun Yat-sen University, Sun Yat-sen University", "Tsinghua University", "National University of Defense Technology"]


title: LLM-driven Multimodal and Multi-Identity Listening Head Generation
authors: Peiwen Lai, Weizhi Zhong, Yipeng Qin, Xiaohang Ren, Baoyuan Wang, Guanbin Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10656-10666
cities: ["Guangzhou", "Cardiff", "Beijing", "Shenzhen", "Guangzhou"]
countries: ["China", "United Kingdom", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lai_LLM-driven_Multimodal_and_Multi-Identity_Listening_Head_Generation_CVPR_2025_paper.html

abstract:
Generating natural listener responses in conversational scenarios is crucial for creating engaging digital humans and avatars. Recent work has shown that large language models (LLMs) can be effectively leveraged for this task, demonstrating remarkable capabilities in generating contextually appropriate listener behaviors. However, current LLM-based methods face two critical limitations: they rely solely on speech content, overlooking other crucial communication signals, and they entangle listener identity with response generation, compromising output fidelity and generalization. In this work, we present a novel framework that addresses these limitations while maintaining the advantages of LLMs. Our approach introduces a Multimodal-LM architecture that jointly processes speech content, acoustics, and speaker emotion, capturing the full spectrum of communication cues. Additionally, we propose an identity disentanglement strategy using instance normalization and adaptive instance normalization in a VQ-VAE framework, enabling high-fidelity listening head synthesis with flexible identity control. Extensive experiments demonstrate that our method significantly outperforms existing approaches in terms of response naturalness and fidelity, while enabling effective identity control without retraining.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lai_LLM-driven_Multimodal_and_Multi-Identity_Listening_Head_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lai_LLM-driven_Multimodal_and_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Lai_2025_CVPR,
    author    = {Lai, Peiwen and Zhong, Weizhi and Qin, Yipeng and Ren, Xiaohang and Wang, Baoyuan and Li, Guanbin},
    title     = {LLM-driven Multimodal and Multi-Identity Listening Head Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10656-10666}
}

raw_affiliation:
[{"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Cardiff University", "city": "Cardiff", "country": "United Kingdom"}, {"affiliation": "Xiaobing.AI", "city": "Beijing", "country": "China"}, {"affiliation": "Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "Guangdong Key Laboratory of Big Data Analysis and Processing", "city": "Guangzhou", "country": "China"}]

affiliations:
["Sun Yat-sen University", "Cardiff University", "Xiaobing.AI", "Peng Cheng Laboratory", "Guangdong Key Laboratory of Big Data Analysis and Processing"]


title: Towards Understanding How Knowledge Evolves in Large Vision-Language Models
authors: Sudong Wang, Yunjian Zhang, Yao Zhu, Jianing Li, Zizhe Wang, Yanwei Liu, Xiangyang Ji
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29858-29868
cities: ["Beijing", "Singapore", "Beijing"]
countries: ["China", "Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Towards_Understanding_How_Knowledge_Evolves_in_Large_Vision-Language_Models_CVPR_2025_paper.html

abstract:
Large Vision-Language Models (LVLMs) are gradually becoming the foundation for many artificial intelligence applications. However, understanding their internal working mechanisms has continued to puzzle researchers, which in turn limits the further enhancement of their capabilities. In this paper, we seek to investigate how multimodal knowledge evolves and eventually induces natural languages in LVLMs. We design a series of novel strategies for analyzing internal knowledge within LVLMs, and delve into the evolution of multimodal knowledge from three levels, including single token probabilities, token probability distributions, and feature encodings. In this process,  we identify two key nodes in knowledge evolution: the critical layers and the mutation layers, dividing the evolution process into three stages: rapid evolution, stabilization, and mutation. Our research is the first to reveal the trajectory of knowledge evolution in LVLMs, providing a fresh perspective for understanding their underlying mechanisms.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Towards_Understanding_How_Knowledge_Evolves_in_Large_Vision-Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Towards_Understanding_How_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Sudong and Zhang, Yunjian and Zhu, Yao and Li, Jianing and Wang, Zizhe and Liu, Yanwei and Ji, Xiangyang},
    title     = {Towards Understanding How Knowledge Evolves in Large Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29858-29868}
}

raw_affiliation:
[{"affiliation": "Institute of Information Engeering, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["Institute of Information Engeering, Chinese Academy of Sciences", "Nanyang Technological University", "Tsinghua University"]


title: A Unified, Resilient, and Explainable Adversarial Patch Detector
authors: Vishesh Kumar, Akshay Agarwal
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30387-30397
affiliations: ["Trustworthy BiometraVision Lab, IISER Bhopal"]
cities: ["Bhopal"]
countries: ["India"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kumar_A_Unified_Resilient_and_Explainable_Adversarial_Patch_Detector_CVPR_2025_paper.html

abstract:
Deep Neural Networks (DNNs), backbone architecture in `almost' every computer vision task, are vulnerable to adversarial attacks, particularly physical out-of-distribution (OOD) adversarial patches. Existing defense models often struggle with interpreting these attacks in ways that align with human visual perception. Our proposed AdvPatchXAI approach introduces a generalized, robust, and explainable defense algorithm designed to defend DNNs against physical adversarial threats. AdvPatchXAI employs a novel patch decorrelation loss that reduces feature redundancy and enhances the distinctiveness of patch representations, enabling better generalization across unseen adversarial scenarios. It learns prototypical parts self-supervised, enhancing interpretability and correlation with human vision. The model utilizes a sparse linear layer for classification, making the decision process globally interpretable through a set of learned prototypes and locally explainable by pinpointing relevant prototypes within an image. Our comprehensive evaluation shows that AdvPatchXAI closes the "semantic" gap between latent space and pixel space and effectively handles unseen adversarial patches even perturbed with unseen corruptions, thereby significantly advancing DNN robustness in practical settings(https://github.com/tbvl22/Unified-resilient-and-Explainable-Adversarial-Patch-detector).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kumar_A_Unified_Resilient_and_Explainable_Adversarial_Patch_Detector_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kumar_A_Unified_Resilient_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kumar_2025_CVPR,
    author    = {Kumar, Vishesh and Agarwal, Akshay},
    title     = {A Unified, Resilient, and Explainable Adversarial Patch Detector},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30387-30397}
}

raw_affiliation:
[{"affiliation": "Trustworthy BiometraVision Lab, IISER Bhopal", "city": "Bhopal", "country": "India"}]


authors: Weiming Ren, Huan Yang, Jie Min, Cong Wei, Wenhu Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3804-3814
affiliations: ["University of Waterloo", "Vector Institute", "01.AI"]
cities: ["Waterloo", "Toronto", "Beijing"]
countries: ["Canada", "Canada", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ren_VISTA_Enhancing_Long-Duration_and_High-Resolution_Video_Understanding_by_Video_Spatiotemporal_CVPR_2025_paper.html

title:
VISTA: Enhancing Long-Duration and High-Resolution Video Understanding by Video Spatiotemporal Augmentation

abstract:
Current large multimodal models (LMMs) face significant challenges in processing and comprehending long-duration or high-resolution videos, which is mainly due to the lack of high-quality datasets. To address this issue from a data-centric perspective, we propose VISTA, a simple yet effective video spatiotemporal augmentation framework that synthesizes long-duration and high-resolution video instruction-following pairs from existing video-caption datasets. VISTA spatially and temporally combines videos to create new synthetic videos with extended durations and enhanced resolutions, and subsequently produces question-answer pairs pertaining to these newly synthesized videos. Based on this paradigm, we develop seven video augmentation methods and curate VISTA-400K, a video instruction-following dataset aimed at enhancing long-duration and high-resolution video understanding. Finetuning various video LMMs on our data resulted in an average improvement of 3.3% across four challenging benchmarks for long-video understanding. Furthermore, we introduce the first comprehensive high-resolution video understanding benchmark HRVideoBench, on which our finetuned models achieve a 6.5% performance gain. These results highlight the effectiveness of our framework.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ren_VISTA_Enhancing_Long-Duration_and_High-Resolution_Video_Understanding_by_Video_Spatiotemporal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ren_VISTA_Enhancing_Long-Duration_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ren_2025_CVPR,
    author    = {Ren, Weiming and Yang, Huan and Min, Jie and Wei, Cong and Chen, Wenhu},
    title     = {VISTA: Enhancing Long-Duration and High-Resolution Video Understanding by Video Spatiotemporal Augmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3804-3814}
}

raw_affiliation:
[{"affiliation": "University of Waterloo", "city": "Waterloo", "country": "Canada"}, {"affiliation": "Vector Institute", "city": "Toronto", "country": "Canada"}, {"affiliation": "01.AI", "city": "Beijing", "country": "China"}]


title: Structured 3D Latents for Scalable and Versatile 3D Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21469-21480
affiliations: ["Tsinghua University", "USTC", "Microsoft Research"]
cities: ["Beijing", "Hefei", "Redmond"]
countries: ["China", "China", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xiang_Structured_3D_Latents_for_Scalable_and_Versatile_3D_Generation_CVPR_2025_paper.html

authors:
Jianfeng Xiang, Zelong Lv, Sicheng Xu, Yu Deng, Ruicheng Wang, Bowen Zhang, Dong Chen, Xin Tong, Jiaolong Yang

abstract:
We introduce a novel 3D generation method for versatile and high-quality 3D asset creation.The cornerstone is a unified Structured LATent (SLAT) representation which allows decoding to different output formats, such as Radiance Fields, 3D Gaussians, and meshes. This is achieved by integrating a sparsely-populated 3D grid with dense multiview visual features extracted from a powerful vision foundation model, comprehensively capturing both structural (geometry) and textural (appearance) information while maintaining flexibility during decoding.We employ rectified flow transformers tailored for SLAT as our 3D generation models and train models with up to 2 billion parameters on a large 3D asset dataset of 500K diverse objects. Our model generates high-quality results with text or image conditions, significantly surpassing existing methods, including recent ones at similar scales. We showcase flexible output format selection and local 3D editing capabilities which were not offered by previous models. Code, model, and data will be released.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiang_Structured_3D_Latents_for_Scalable_and_Versatile_3D_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiang_Structured_3D_Latents_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xiang_2025_CVPR,
    author    = {Xiang, Jianfeng and Lv, Zelong and Xu, Sicheng and Deng, Yu and Wang, Ruicheng and Zhang, Bowen and Chen, Dong and Tong, Xin and Yang, Jiaolong},
    title     = {Structured 3D Latents for Scalable and Versatile 3D Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21469-21480}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "USTC", "city": "Hefei", "country": "China"}, {"affiliation": "Microsoft Research", "city": "Redmond", "country": "USA"}]


title: GA3CE: Unconstrained 3D Gaze Estimation with Gaze-Aware 3D Context Encoding
authors: Yuki Kawana, Shintaro Shiba, Quan Kong, Norimasa Kobori
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3081-3090
raw_affiliation: [{"affiliation": "Woven by Toyota", "city": "Tokyo", "country": "Japan"}]
affiliations: ["Woven by Toyota"]
cities: ["Tokyo"]
countries: ["Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kawana_GA3CE_Unconstrained_3D_Gaze_Estimation_with_Gaze-Aware_3D_Context_Encoding_CVPR_2025_paper.html

abstract:
We propose a novel 3D gaze estimation approach that learns spatial relationships between the subject and objects in the scene, and outputs 3D gaze direction. Our method targets unconstrained settings, including cases where close-up views of the subject's eyes are unavailable, such as when the subject is distant or facing away. Previous approaches typically rely on either 2D appearance alone or incorporate limited spatial cues using depth maps in the non-learnable post-processing step. Estimating 3D gaze direction from 2D observations in these scenarios is challenging; variations in subject pose, scene layout, and gaze direction, combined with differing camera poses, yield diverse 2D appearances and 3D gaze directions even when targeting the same 3D scene. To address this issue, we propose GA3CE: Gaze-Aware 3D Context Encoding. Our method represents subject and scene using 3D poses and object positions, treating them as 3D context to learn spatial relationships in 3D space. Inspired by human vision, we align this context in an egocentric space, significantly reducing spatial complexity. Furthermore, we propose D^3 (direction-distance-decomposed) positional encoding to better capture the spatial relationship between 3D context and gaze direction in direction and distance space. Experiments demonstrate substantial improvements, reducing mean angle error by 13%-37% compared to leading baselines on benchmark datasets in single-frame settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kawana_GA3CE_Unconstrained_3D_Gaze_Estimation_with_Gaze-Aware_3D_Context_Encoding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kawana_GA3CE_Unconstrained_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kawana_2025_CVPR,
    author    = {Kawana, Yuki and Shiba, Shintaro and Kong, Quan and Kobori, Norimasa},
    title     = {GA3CE: Unconstrained 3D Gaze Estimation with Gaze-Aware 3D Context Encoding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3081-3090}
}


title: Self-Cross Diffusion Guidance for Text-to-Image Synthesis of Similar Subjects
authors: Weimin Qiu, Jieke Wang, Meng Tang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23528-23538
raw_affiliation: [{"affiliation": "University of California Merced", "city": "Merced", "country": "USA"}]
affiliations: ["University of California Merced"]
cities: ["Merced"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qiu_Self-Cross_Diffusion_Guidance_for_Text-to-Image_Synthesis_of_Similar_Subjects_CVPR_2025_paper.html

abstract:
Diffusion models achieved unprecedented fidelity and diversity for synthesizing image, video, 3D assets, etc. However, subject mixing is an unresolved issue for diffusion-based image synthesis, particularly for synthesizing multiple similar-looking subjects. We propose Self-Cross Diffusion Guidance to penalize the overlap between cross-attention maps and the aggregated self-attention map. Compared to previous methods based on self-attention or cross-attention alone, our guidance is more effective in eliminating subject mixing. What's more, our guidance addresses subject mixing for all relevant patches beyond the most discriminant one, e.g., the beak of a bird. For each subject, we aggregate self-attention maps of patches with higher cross-attention values. Thus, the aggregated self-attention map forms a region that the whole subject attends to. Our training-free method boosts the performance of both Unet-based and Transformer-based diffusion models such as the Stable Diffusion series. We also release a similar subjects dataset (SSD), a challenging benchmark, and utilize GPT-4o for automatic and reliable evaluation. Extensive qualitative and quantitative results demonstrate the effectiveness of our self-cross diffusion guidance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qiu_Self-Cross_Diffusion_Guidance_for_Text-to-Image_Synthesis_of_Similar_Subjects_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qiu_Self-Cross_Diffusion_Guidance_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qiu_2025_CVPR,
    author    = {Qiu, Weimin and Wang, Jieke and Tang, Meng},
    title     = {Self-Cross Diffusion Guidance for Text-to-Image Synthesis of Similar Subjects},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23528-23538}
}


title: RigGS: Rigging of 3D Gaussians for Modeling Articulated Objects in Videos
authors: Yuxin Yao, Zhi Deng, Junhui Hou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5592-5601
affiliations: ["City University of Hong Kong", "University of Science and Technology of China"]
cities: ["Hong Kong", "Hefei"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yao_RigGS_Rigging_of_3D_Gaussians_for_Modeling_Articulated_Objects_in_CVPR_2025_paper.html

abstract:
This paper considers the problem of modeling articulated objects captured in 2D videos to enable novel view synthesis, while also being easily editable, drivable, and reposable. To tackle this challenging problem, we propose RigGS, a new paradigm that leverages 3D Gaussian representation and skeleton-based motion representation to model dynamic objects without utilizing additional template priors. Specifically, we first propose skeleton-aware node-controlled deformation, which deforms a canonical 3D Gaussian representation over time to initialize the modeling process, producing candidate skeleton nodes that are further simplified into a sparse 3D skeleton according to their motion and semantic information. Subsequently, based on the resulting skeleton, we design learnable skin deformations and pose-dependent detailed deformations, thereby easily deforming the 3D Gaussian representation to generate new actions and render further high-quality images from novel views. Extensive experiments demonstrate that our method can generate realistic new actions easily for objects and achieve high-quality rendering.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yao_RigGS_Rigging_of_3D_Gaussians_for_Modeling_Articulated_Objects_in_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yao_RigGS_Rigging_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yao_2025_CVPR,
    author    = {Yao, Yuxin and Deng, Zhi and Hou, Junhui},
    title     = {RigGS: Rigging of 3D Gaussians for Modeling Articulated Objects in Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5592-5601}
}

raw_affiliation:
[{"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}]


authors: Feiran Li, Haiyang Jiang, Daisuke Iso
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5699-5708
affiliations: ["Sony Research", "Tokyo University", "Sony Research"]
cities: ["Tokyo", "Tokyo", "Tokyo"]
countries: ["Japan", "Japan", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Noise_Modeling_in_One_Hour_Minimizing_Preparation_Efforts_for_Self-supervised_CVPR_2025_paper.html

title:
Noise Modeling in One Hour: Minimizing Preparation Efforts for Self-supervised Low-Light RAW Image Denoising

abstract:
Noise synthesis is a promising solution for addressing the data shortage problem in data-driven low-light RAW image denoising. However, accurate noise synthesis methods often necessitate labor-intensive calibration and profiling procedures during preparation, preventing them from landing to practice at scale. This work introduces a practically simple noise synthesis pipeline based on detailed analyses of noise properties and extensive justification of widespread techniques. Compared to other approaches, our proposed pipeline eliminates the cumbersome system gain calibration and signal-independent noise profiling steps, reducing the preparation time for noise synthesis from days to hours. Meanwhile, our method exhibits strong denoising performance, showing an up to 0.54dB PSNR improvement over the current state-of-the-art noise synthesis technique.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Noise_Modeling_in_One_Hour_Minimizing_Preparation_Efforts_for_Self-supervised_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Noise_Modeling_in_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Feiran and Jiang, Haiyang and Iso, Daisuke},
    title     = {Noise Modeling in One Hour: Minimizing Preparation Efforts for Self-supervised Low-Light RAW Image Denoising},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5699-5708}
}

raw_affiliation:
[{"affiliation": "Sony Research", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Tokyo University", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Sony Research", "city": "Tokyo", "country": "Japan"}]


title: Adv-CPG: A Customized Portrait Generation Framework with Facial Adversarial Attacks
authors: Junying Wang, Hongyuan Zhang, Yuan Yuan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21001-21010
cities: ["Xi'an", "Hong Kong", "Shanghai"]
countries: ["China", "China", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Adv-CPG_A_Customized_Portrait_Generation_Framework_with_Facial_Adversarial_Attacks_CVPR_2025_paper.html

abstract:
Recent Customized Portrait Generation (CPG) methods, taking a facial image and a textual prompt as inputs, have attracted substantial attention. Although these methods generate high-fidelity portraits, they fail to prevent the generated portraits from being tracked and misused by malicious face recognition systems. To address this, this paper proposes a Customized Portrait Generation framework with facial Adversarial attacks (Adv-CPG). Specifically, to achieve facial privacy protection, we devise a lightweight local ID encryptor and an encryption enhancer. They implement progressive double-layer encryption protection by directly injecting the target identity and adding additional identity guidance, respectively. Furthermore, to accomplish fine-grained and personalized portrait generation, we develop a multi-modal image customizer capable of generating controlled fine-grained facial features. To the best of our knowledge, Adv-CPG is the first study that introduces facial adversarial attacks into CPG. Extensive experiments demonstrate the superiority of Adv-CPG, e.g., the average attack success rate of the proposed Adv-CPG is 28.1% and 2.86% higher compared to the SOTA noise-based attack methods and unconstrained attack methods, respectively.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Adv-CPG_A_Customized_Portrait_Generation_Framework_with_Facial_Adversarial_Attacks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Adv-CPG_A_Customized_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Junying and Zhang, Hongyuan and Yuan, Yuan},
    title     = {Adv-CPG: A Customized Portrait Generation Framework with Facial Adversarial Attacks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21001-21010}
}

raw_affiliation:
[{"affiliation": "School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Institute of Artificial Intelligence (TeleAI), China Telecom", "city": "Shanghai", "country": "China"}]

affiliations:
["School of Artificial Intelligence, OPtics and ElectroNics (iOPEN), Northwestern Polytechnical University", "The University of Hong Kong", "Institute of Artificial Intelligence (TeleAI), China Telecom"]


title: Fish-Vista: A Multi-Purpose Dataset for Understanding & Identification of Traits from Images
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24275-24285
cities: ["Blacksburg", "Columbus", "Oak Ridge", "Irvine", "Durham", "New Orleans", "Columbus"]
countries: ["USA", "USA", "USA", "USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mehrab_Fish-Vista_A_Multi-Purpose_Dataset_for_Understanding__Identification_of_Traits_CVPR_2025_paper.html

authors:
Kazi Sajeed Mehrab, M. Maruf, Arka Daw, Abhilash Neog, Harish Babu Manogaran, Mridul Khurana, Zhenyang Feng, Bahadir Altintas, Yasin Bakis, Elizabeth G Campolongo, Matthew J Thompson, Xiaojun Wang, Hilmar Lapp, Tanya Berger-Wolf, Paula Mabee, Henry Bart, Wei-Lun Chao, Wasila M Dahdul, Anuj Karpatne

abstract:
We introduce Fish-Visual Trait Analysis (Fish-Vista), the first organismal image dataset designed for the analysis of visual traits of aquatic species directly from images using machine learning and computer vision methods. Fish-Vista contains 69,269 annotated images spanning 4,316 fish species, curated and organized to serve three downstream tasks: species classification, trait identification, and trait segmentation. Our work makes two key contributions. First, we provide a fully reproducible data processing pipeline to process fish images sourced from various museum collections, contributing to the advancement of AI in biodiversity science. We annotate the images with carefully curated labels from biological databases and manual annotations to create an AI-ready dataset of visual traits. Second, our work offers fertile grounds for researchers to develop novel methods for a variety of problems in computer vision such as handling long-tailed distributions, out-of-distribution generalization, learning with weak labels, explainable AI, and segmenting small objects.  Dataset and code for Fish-Vista are available at https://github.com/Imageomics/Fish-Vista

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mehrab_Fish-Vista_A_Multi-Purpose_Dataset_for_Understanding__Identification_of_Traits_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mehrab_Fish-Vista_A_Multi-Purpose_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mehrab_2025_CVPR,
    author    = {Mehrab, Kazi Sajeed and Maruf, M. and Daw, Arka and Neog, Abhilash and Manogaran, Harish Babu and Khurana, Mridul and Feng, Zhenyang and Altintas, Bahadir and Bakis, Yasin and Campolongo, Elizabeth G and Thompson, Matthew J and Wang, Xiaojun and Lapp, Hilmar and Berger-Wolf, Tanya and Mabee, Paula and Bart, Henry and Chao, Wei-Lun and Dahdul, Wasila M and Karpatne, Anuj},
    title     = {Fish-Vista: A Multi-Purpose Dataset for Understanding \& Identification of Traits from Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24275-24285}
}

raw_affiliation:
[{"affiliation": "Virginia Tech", "city": "Blacksburg", "country": "USA"}, {"affiliation": "The Ohio State University", "city": "Columbus", "country": "USA"}, {"affiliation": "Oak Ridge National Laboratory", "city": "Oak Ridge", "country": "USA"}, {"affiliation": "University of California, Irvine", "city": "Irvine", "country": "USA"}, {"affiliation": "Duke University", "city": "Durham", "country": "USA"}, {"affiliation": "Tulane University", "city": "New Orleans", "country": "USA"}, {"affiliation": "Battelle", "city": "Columbus", "country": "USA"}]

affiliations:
["Virginia Tech", "The Ohio State University", "Oak Ridge National Laboratory", "University of California, Irvine", "Duke University", "Tulane University", "Battelle"]


authors: Zhaoyi Tian, Feifeng Wang, Shiwei Wang, Zihao Zhou, Yao Zhu, Liquan Shen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7320-7330
raw_affiliation: [{"affiliation": "Shangha University", "city": "Shanghai", "country": "China"}]
affiliations: ["Shangha University"]
cities: ["Shanghai"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tian_High_Dynamic_Range_Video_Compression_A_Large-Scale_Benchmark_Dataset_and_CVPR_2025_paper.html

title:
High Dynamic Range Video Compression: A Large-Scale Benchmark Dataset and A Learned Bit-depth Scalable Compression Algorithm

abstract:
Recently, learned video compression (LVC) is undergoing a period of rapid development. However, due to absence of large and high-quality high dynamic range (HDR) video training data, LVC on HDR video is still unexplored. In this paper, we are the first to collect a large-scale HDR video benchmark dataset, named HDRVD2K, featuring huge quantity, diverse scenes and multiple motion types. HDRVD2K fills gaps of video training data and facilitate the development of LVC on HDR videos. Based on HDRVD2K, we further propose the first learned bit-depth scalable video compression (LBSVC) network for HDR videos by effectively exploiting bit-depth redundancy between videos of multiple dynamic ranges. To achieve this, we first propose a compression-friendly bit-depth enhancement module (BEM) to effectively predict original HDR videos based on compressed tone-mapped low dynamic range (LDR) videos and dynamic range prior, instead of reducing redundancy only through spatio-temporal predictions. Our method greatly improves the reconstruction quality and compression performance on HDR videos. Extensive experiments demonstrate the effectiveness of HDRVD2K on learned HDR video compression and great compression performance of our proposed LBSVC network.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tian_High_Dynamic_Range_Video_Compression_A_Large-Scale_Benchmark_Dataset_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tian_High_Dynamic_Range_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tian_2025_CVPR,
    author    = {Tian, Zhaoyi and Wang, Feifeng and Wang, Shiwei and Zhou, Zihao and Zhu, Yao and Shen, Liquan},
    title     = {High Dynamic Range Video Compression: A Large-Scale Benchmark Dataset and A Learned Bit-depth Scalable Compression Algorithm},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7320-7330}
}


title: OffsetOPT: Explicit Surface Reconstruction without Normals
authors: Huan Lei
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11729-11738
raw_affiliation: [{"affiliation": "AIML, University of Adelaide", "city": "Adelaide", "country": "Australia"}]
affiliations: ["AIML, University of Adelaide"]
cities: ["Adelaide"]
countries: ["Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lei_OffsetOPT_Explicit_Surface_Reconstruction_without_Normals_CVPR_2025_paper.html

abstract:
Neural surface reconstruction has been dominated by implicit representations with marching cubes for explicit surface extraction. However, those methods typically require high-quality normals for accurate reconstruction. We propose OffsetOPT, a method that  reconstructs  explicit surfaces directly from 3D point clouds and eliminates the need for point normals. The approach comprises two stages: first, we train a neural network to predict surface triangles based on local point geometry, given uniformly distributed training point clouds. Next, we apply the frozen network to reconstruct surfaces from unseen point clouds by optimizing a per-point offset to maximize the accuracy of triangle predictions. Compared to state-of-the-art methods, OffsetOPT not only excels at reconstructing overall surfaces but also significantly preserves sharp surface features. We demonstrate its accuracy on popular benchmarks, including  small-scale shapes and large-scale open surfaces.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lei_OffsetOPT_Explicit_Surface_Reconstruction_without_Normals_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lei_OffsetOPT_Explicit_Surface_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lei_2025_CVPR,
    author    = {Lei, Huan},
    title     = {OffsetOPT: Explicit Surface Reconstruction without Normals},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11729-11738}
}


title: PCM : Picard Consistency Model for Fast Parallel Sampling of Diffusion Models
authors: Junhyuk So, Jiwoong Shin, Chaeyeon Jang, Eunhyeok Park
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23313-23322
cities: ["Pohang", "Pohang"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/So_PCM__Picard_Consistency_Model_for_Fast_Parallel_Sampling_of_CVPR_2025_paper.html

abstract:
Recently, diffusion models have achieved significant advances in vision, text, and robotics. However, they still face slow generation speeds due to sequential denoising processes. To address this, a parallel sampling method based on Picard iteration was introduced, effectively reducing sequential steps while ensuring exact convergence to the original output. Nonetheless, Picard iteration does not guarantee faster convergence, which can still result in slow generation in practice. In this work, we propose a new parallelization scheme, the Picard Consistency Model (PCM), which significantly reduces the number of generation steps in Picard iteration. Inspired by the consistency model, PCM is directly trained to predict the fixed-point solution, or the final output, at any stage of the convergence trajectory. Additionally, we introduce a new concept called model switching, which addresses PCM's limitations and ensures exact convergence. Extensive experiments demonstrate that PCM achieves up to a 2.71x speedup over sequential sampling and a 1.77x speedup over Picard iteration across various tasks, including image generation and robotic control.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/So_PCM__Picard_Consistency_Model_for_Fast_Parallel_Sampling_of_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/So_PCM__Picard_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{So_2025_CVPR,
    author    = {So, Junhyuk and Shin, Jiwoong and Jang, Chaeyeon and Park, Eunhyeok},
    title     = {PCM : Picard Consistency Model for Fast Parallel Sampling of Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23313-23322}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science and Engineering, POSTECH", "city": "Pohang", "country": "South Korea"}, {"affiliation": "Graduate School of Artificial Intelligence, POSTECH", "city": "Pohang", "country": "South Korea"}]

affiliations:
["Department of Computer Science and Engineering, POSTECH", "Graduate School of Artificial Intelligence, POSTECH"]


title: CoMapGS: Covisibility Map-based Gaussian Splatting for Sparse Novel View Synthesis
authors: Youngkyoon Jang, Eduardo Pérez-Pellitero
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26779-26788
raw_affiliation: [{"affiliation": "Huawei Noah\u2019s Ark Lab", "city": "London", "country": "UK"}]
affiliations: ["Huawei Noah\u2019s Ark Lab"]
cities: ["London"]
countries: ["UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jang_CoMapGS_Covisibility_Map-based_Gaussian_Splatting_for_Sparse_Novel_View_Synthesis_CVPR_2025_paper.html

abstract:
We propose Covisibility Map-based Gaussian Splatting (CoMapGS), designed to recover underrepresented sparse regions in sparse novel view synthesis. CoMapGS addresses both high- and low-uncertainty regions by constructing covisibility maps, enhancing initial point clouds, and applying uncertainty-aware weighted supervision using a proximity classifier. Our contributions are threefold: (1) CoMapGS reframes novel view synthesis by leveraging covisibility maps as a core component to address region-specific uncertainty; (2) Enhanced initial point clouds for both low- and high-uncertainty regions compensate for sparse COLMAP-derived point clouds, improving reconstruction quality and benefiting few-shot 3DGS methods; (3) Adaptive supervision with covisibility-score-based weighting and proximity classification achieves consistent performance gains across scenes with varying sparsity scores derived from covisibility maps. Experimental results demonstrate that CoMapGS outperforms state-of-the-art methods on datasets including Mip-NeRF 360 and LLFF.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jang_CoMapGS_Covisibility_Map-based_Gaussian_Splatting_for_Sparse_Novel_View_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jang_CoMapGS_Covisibility_Map-based_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Jang_2025_CVPR,
    author    = {Jang, Youngkyoon and P\'erez-Pellitero, Eduardo},
    title     = {CoMapGS: Covisibility Map-based Gaussian Splatting for Sparse Novel View Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26779-26788}
}


title: Any-Resolution AI-Generated Image Detection by Spectral Learning
authors: Dimitrios Karageorgiou, Symeon Papadopoulos, Ioannis Kompatsiaris, Efstratios Gavves
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18706-18717
affiliations: ["Information Technologies Institute, CERTH", "University of Amsterdam", "Archimedes/Athena RC"]
cities: ["Thessaloniki", "Amsterdam", "Athens"]
countries: ["Greece", "The Netherlands", "Greece"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Karageorgiou_Any-Resolution_AI-Generated_Image_Detection_by_Spectral_Learning_CVPR_2025_paper.html

abstract:
Recent works have established that AI models introduce spectral artifacts into generated images and propose approaches for learning to capture them using labeled data. However, the significant differences in such artifacts among different generative models hinder these approaches from generalizing to generators not seen during training. In this work, we build upon the key idea that the spectral distribution of real images constitutes both an invariant and highly discriminative pattern for AI-generated image detection. To model this under a self-supervised setup, we employ masked spectral learning using the pretext task of frequency reconstruction. Since generated images constitute out-of-distribution samples for this model, we propose spectral reconstruction similarity to capture this divergence. Moreover, we introduce spectral context attention, which enables our approach to efficiently capture subtle spectral inconsistencies in images of any resolution. Our spectral AI-generated image detection approach (SPAI) achieves a 5.5% absolute improvement in AUC over the previous state-of-the-art across 13 recent generative approaches, while exhibiting robustness against common online perturbations. Code is available on https://mever-team.github.io/spai.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Karageorgiou_Any-Resolution_AI-Generated_Image_Detection_by_Spectral_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Karageorgiou_Any-Resolution_AI-Generated_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Karageorgiou_2025_CVPR,
    author    = {Karageorgiou, Dimitrios and Papadopoulos, Symeon and Kompatsiaris, Ioannis and Gavves, Efstratios},
    title     = {Any-Resolution AI-Generated Image Detection by Spectral Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18706-18717}
}

raw_affiliation:
[{"affiliation": "Information Technologies Institute, CERTH", "city": "Thessaloniki", "country": "Greece"}, {"affiliation": "University of Amsterdam", "city": "Amsterdam", "country": "The Netherlands"}, {"affiliation": "Archimedes/Athena RC", "city": "Athens", "country": "Greece"}]


title: DivPrune: Diversity-based Visual Token Pruning for Large Multimodal Models
authors: Saeed Ranjbar Alvar, Gursimran Singh, Mohammad Akbari, Yong Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9392-9401
raw_affiliation: [{"affiliation": "Huawei Technologies Canada Co., Ltd.", "city": "Markham", "country": "Canada"}]
affiliations: ["Huawei Technologies Canada Co., Ltd."]
cities: ["Markham"]
countries: ["Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Alvar_DivPrune_Diversity-based_Visual_Token_Pruning_for_Large_Multimodal_Models_CVPR_2025_paper.html

abstract:
Large Multimodal Models (LMMs) have emerged as powerful models capable of understanding various data modalities, including text, images, and videos. LMMs encode both text and visual data into tokens that are then combined and processed by an integrated Large Language Model (LLM). Including visual tokens substantially increases the total token count, often by thousands. The increased input length for LLM significantly raises the complexity of inference, resulting in high latency in LMMs. To address this issue, token pruning methods, which remove part of the visual tokens, are proposed. The existing token pruning methods either require extensive calibration and fine-tuning or rely on suboptimal importance metrics which results in increased redundancy among the retained tokens. In this paper, we first formulate token pruning as Max-Min Diversity Problem (MMDP) where the goal is to select a subset such that the diversity among the selected tokens is maximized. Then, we solve the MMDP to obtain the selected subset and prune the rest. The proposed method, DivPrune, reduces redundancy and achieves the highest diversity of the selected tokens. By ensuring high diversity, the selected tokens better represent the original tokens, enabling effective performance even at high pruning ratios without requiring fine-tuning. Extensive experiments with various LMMs show that DivPrune achieves state-of-the-art accuracy over 16 image- and video-language datasets. Additionally, DivPrune reduces both the end-to-end latency and GPU memory usage for the tested models.  The code is available here.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Alvar_DivPrune_Diversity-based_Visual_Token_Pruning_for_Large_Multimodal_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Alvar_DivPrune_Diversity-based_Visual_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Alvar_2025_CVPR,
    author    = {Alvar, Saeed Ranjbar and Singh, Gursimran and Akbari, Mohammad and Zhang, Yong},
    title     = {DivPrune: Diversity-based Visual Token Pruning for Large Multimodal Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9392-9401}
}


authors: Yuechen Xie, Jie Song, Huiqiong Wang, Mingli Song
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23817-23827
cities: ["Hangzhou", "Ningbo", "Hangzhou", "Hangzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_Training_Data_Provenance_Verification_Did_Your_Model_Use_Synthetic_Data_CVPR_2025_paper.html

title:
Training Data Provenance Verification: Did Your Model Use Synthetic Data from My Generative Model for Training?

abstract:
High-quality open-source text-to-image models have lowered the threshold for obtaining photorealistic images significantly, but also face potential risks of misuse. Specifically, suspects may use synthetic data generated by these generative models to train models for specific tasks without permission, when lacking real data resources especially. Protecting these generative models is crucial for the well-being of their owners. In this work, we propose the first method to this important yet unresolved issue, called Training data Provenance Verification (TrainProVe). The rationale behind TrainProVe is grounded in the principle of generalization error bound, which suggests that, for two models with the same task, if the distance between their training data distributions is smaller, their generalization ability will be closer. We validate the efficacy of TrainProVe across four text-to-image models (Stable Diffusion v1.4, latent consistency model, PixArt-\alpha, and Stable Cascade). The results show that TrainProVe achieves a verification accuracy of over 99% in determining the provenance of suspicious model training data, surpassing all previous methods. Code is available at https://github.com/xieyc99/TrainProVe.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_Training_Data_Provenance_Verification_Did_Your_Model_Use_Synthetic_Data_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_Training_Data_Provenance_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Yuechen and Song, Jie and Wang, Huiqiong and Song, Mingli},
    title     = {Training Data Provenance Verification: Did Your Model Use Synthetic Data from My Generative Model for Training?},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23817-23827}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Ningbo Innovation Center, Zhejiang University", "city": "Ningbo", "country": "China"}, {"affiliation": "State Key Laboratory of Blockchain and Security, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security", "city": "Hangzhou", "country": "China"}]

affiliations:
["Zhejiang University", "Ningbo Innovation Center, Zhejiang University", "State Key Laboratory of Blockchain and Security, Zhejiang University", "Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security"]


title: 3D-AVS: LiDAR-based 3D Auto-Vocabulary Segmentation
authors: Weijie Wei, Osman Ülger, Fatemeh Karimi Nejadasl, Theo Gevers, Martin R. Oswald
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8910-8920
raw_affiliation: [{"affiliation": "University of Amsterdam", "city": "Amsterdam", "country": "the Netherlands"}]
affiliations: ["University of Amsterdam"]
cities: ["Amsterdam"]
countries: ["the Netherlands"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wei_3D-AVS_LiDAR-based_3D_Auto-Vocabulary_Segmentation_CVPR_2025_paper.html

abstract:
Open-vocabulary segmentation methods offer promising capabilities in detecting unseen object categories, but the category must be aware and needs to be provided by a human, either via a text prompt or pre-labeled datasets, thus limiting their scalability. We propose 3D-AVS, a method for Auto-Vocabulary Segmentation of 3D point clouds for which the vocabulary is unknown and auto-generated for each input at runtime, thus eliminating the human in the loop and typically providing a substantially larger vocabulary for richer annotations. 3D-AVS first recognizes semantic entities from image or point cloud data and then segments all points with the automatically generated vocabulary. Our method incorporates both image-based and point-based recognition, enhancing robustness under challenging lighting conditions where geometric information from LiDAR is especially valuable. Our point-based recognition features a Sparse Masked Attention Pooling (SMAP) module to enrich the diversity of recognized objects. To address the challenges of evaluating unknown vocabularies and avoid annotation biases from label synonyms, hierarchies, or semantic overlaps, we introduce the annotation-free Text-Point Semantic Similarity (TPSS) metric for assessing generated vocabulary quality. Our evaluations on nuScenes and ScanNet200 demonstrate 3D-AVS's ability to generate semantic classes with accurate point-wise segmentations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wei_3D-AVS_LiDAR-based_3D_Auto-Vocabulary_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wei_3D-AVS_LiDAR-based_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wei_2025_CVPR,
    author    = {Wei, Weijie and \"Ulger, Osman and Nejadasl, Fatemeh Karimi and Gevers, Theo and Oswald, Martin R.},
    title     = {3D-AVS: LiDAR-based 3D Auto-Vocabulary Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8910-8920}
}


title: STOP: Integrated Spatial-Temporal Dynamic Prompting for Video Understanding
authors: Zichen Liu, Kunlun Xu, Bing Su, Xu Zou, Yuxin Peng, Jiahuan Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13776-13786
cities: ["Beijing", "Beijing", "Wuhan"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_STOP_Integrated_Spatial-Temporal_Dynamic_Prompting_for_Video_Understanding_CVPR_2025_paper.html

abstract:
Pre-trained on tremendous image-text pairs, vision-language models like CLIP have demonstrated promising zero-shot generalization across numerous image-based tasks. However, extending these capabilities to video tasks remains challenging due to limited labeled video data and high training costs. Recent video prompting methods attempt to adapt CLIP for video tasks by introducing learnable prompts, but they typically rely on a single static prompt for all video sequences, overlooking the diverse temporal dynamics and spatial variations that exist across frames. This limitation significantly hinders the model's ability to capture essential temporal information for effective video understanding. To address this, we propose an integrated Spatial-TempOral dynamic Prompting (STOP) model which consists of two complementary modules, the intra-frame spatial prompting and inter-frame temporal prompting. Our intra-frame spatial prompts are designed to adaptively highlight discriminative regions within each frame by leveraging intra-frame attention and temporal variation, allowing the model to focus on areas with substantial temporal dynamics and capture fine-grained spatial details. Additionally, to highlight the varying importance of frames for video understanding, we further introduce inter-frame temporal prompts, dynamically inserting prompts between frames with high temporal variance as measured by frame similarity. This enables the model to prioritize key frames and enhances its capacity to understand temporal dependencies across sequences. Extensive experiments on various video benchmarks demonstrate that STOP consistently achieves superior performance against state-of-the-art methods. The code is available at https://github.com/zhoujiahuan1991/CVPR2025-STOP

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_STOP_Integrated_Spatial-Temporal_Dynamic_Prompting_for_Video_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_STOP_Integrated_Spatial-Temporal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Zichen and Xu, Kunlun and Su, Bing and Zou, Xu and Peng, Yuxin and Zhou, Jiahuan},
    title     = {STOP: Integrated Spatial-Temporal Dynamic Prompting for Video Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13776-13786}
}

raw_affiliation:
[{"affiliation": "Wangxuan Institute of Computer Technology, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Gaoling School of Artificial Intelligence, Renmin University of China", "city": "Beijing", "country": "China"}, {"affiliation": "School of Artificial Intelligence and Automation, Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}]

affiliations:
["Wangxuan Institute of Computer Technology, Peking University", "Gaoling School of Artificial Intelligence, Renmin University of China", "School of Artificial Intelligence and Automation, Huazhong University of Science and Technology"]


authors: Haoyue Liu, Jinghan Xu, Yi Chang, Hanyu Zhou, Haozhi Zhao, Lin Wang, Luxin Yan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17649-17659
cities: ["Wuhan", "Wuhan", "Singapore"]
countries: ["China", "China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_TimeTracker_Event-based_Continuous_Point_Tracking_for_Video_Frame_Interpolation_with_CVPR_2025_paper.html

title:
TimeTracker: Event-based Continuous Point Tracking for Video Frame Interpolation with Non-linear Motion

abstract:
Video frame interpolation (VFI) that leverages the bio-inspired event cameras as guidance has recently shown better performance and memory efficiency than the frame-based methods, thanks to the event cameras' advantages, such as high temporal resolution. A hurdle for event-based VFI is how to effectively deal with non-linear motion, caused by the dynamic changes in motion direction and speed within the scene. Existing methods either use events to estimate sparse optical flow or fuse events with image features to estimate dense optical flow. Unfortunately, motion errors often degrade the VFI quality as the continuous motion cues from events do not align with the dense spatial information of images in the temporal dimension. In this paper, we find that object motion is continuous in space, tracking local regions over continuous time enables more accurate identification of spatiotemporal feature correlations. In light of this, we propose a novel continuous point tracking-based VFI framework, named TimeTracker. Specifically, we first design a Scene-Aware Region Segmentation (SARS) module to divide the scene into similar patches. Then, a Continuous Trajectory guided Motion Estimation (CTME) module is proposed to track the continuous motion trajectory of each patch through events. Finally, intermediate frames at any given time are generated through global motion optimization and frame refinement. Moreover, we collect a real-world dataset that features fast non-linear motion. Extensive experiments show that our method outperforms prior arts in both motion estimation and frame interpolation quality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_TimeTracker_Event-based_Continuous_Point_Tracking_for_Video_Frame_Interpolation_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_TimeTracker_Event-based_Continuous_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Haoyue and Xu, Jinghan and Chang, Yi and Zhou, Hanyu and Zhao, Haozhi and Wang, Lin and Yan, Luxin},
    title     = {TimeTracker: Event-based Continuous Point Tracking for Video Frame Interpolation with Non-linear Motion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17649-17659}
}

raw_affiliation:
[{"affiliation": "National Key Lab of Multispectral Information Intelligent Processing Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "School of Artificial Intelligence and Automation, Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "School of Electrical and Electronic Engineering, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]

affiliations:
["National Key Lab of Multispectral Information Intelligent Processing Technology", "School of Artificial Intelligence and Automation, Huazhong University of Science and Technology", "School of Electrical and Electronic Engineering, Nanyang Technological University"]


authors: Zhaoyu Zhang, Yang Hua, Guanxiong Sun, Hui Wang, Seán McLoone
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30682-30691
affiliations: ["Queen\u2019s University Belfast", "Huawei UKRD"]
cities: ["Belfast", "Reading"]
countries: ["UK", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Improving_the_Training_of_Data-Efficient_GANs_via_Quality_Aware_Dynamic_CVPR_2025_paper.html

title:
Improving the Training of Data-Efficient GANs via Quality Aware Dynamic Discriminator Rejection Sampling

abstract:
Data-Efficient Generative Adversarial Nets (DE-GANs) have become more and more popular in recent years. Existing methods apply data augmentation, noise injection and pre-trained models to maximumly increase the number of training samples thus improving the training of DE-GANs. However, none of these methods considers the sample quality during training, which can also significantly influence the training of DE-GANs. Focusing on sample quality during training, in this paper, we are the first to incorporate discriminator rejection sampling (DRS) into the training process and introduce a novel method, called quality aware dynamic discriminator rejection sampling (QADDRS). Specifically, QADDRS consists of two steps: (1) the sample quality aware step, which aims to obtain the sorted critic scores, i.e., the ordered discriminator outputs, on real/fake samples in the current training stage; (2) the dynamic rejection step that obtains dynamic rejection number N, where N is controlled by the overfitting degree of discriminator (D) during training. When updating the parameters of D, the N high critic score real samples and the N low critic score fake samples in the minibatch are rejected dynamically based on the overfitting degree of D. As a result, QADDRS can avoid D becoming overly confident in distinguishing both real and fake samples, thereby alleviating the overfitting of D issue during training. Extensive experiments on several datasets demonstrate that integrating QADDRS into different DE-GANs can achieve better performance and deliver state-of-the-art results. Codes are available at https://github.com/zzhang05/QADDRS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Improving_the_Training_of_Data-Efficient_GANs_via_Quality_Aware_Dynamic_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Improving_the_Training_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Zhaoyu and Hua, Yang and Sun, Guanxiong and Wang, Hui and McLoone, Se\'an},
    title     = {Improving the Training of Data-Efficient GANs via Quality Aware Dynamic Discriminator Rejection Sampling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30682-30691}
}

raw_affiliation:
[{"affiliation": "Queen\u2019s University Belfast", "city": "Belfast", "country": "UK"}, {"affiliation": "Huawei UKRD", "city": "Reading", "country": "UK"}]


authors: Guoyu Lu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16508-16519
raw_affiliation: [{"affiliation": "Binghamton University", "city": "Binghamton", "country": "USA"}]
affiliations: ["Binghamton University"]
cities: ["Binghamton"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lu_Shading_Meets_Motion_Self-supervised_Indoor_3D_Reconstruction_Via_Simultaneous_Shape-from-Shading_CVPR_2025_paper.html

title:
Shading Meets Motion: Self-supervised Indoor 3D Reconstruction Via Simultaneous Shape-from-Shading and Structure-from-Motion

abstract:
Scene reconstruction has a wide range of applications in computer vision and robotics. To build practical constraints and feature Scene reconstruction has a wide range of applications in computer vision and robotics. To build practical constraints and feature correspondences, rich textures and distinguished gradient variations are particularly required in classic and learning-based SfM. When building low-texture regions with repeated patterns, especially mostly-white indoor rooms, there is a significant drop in performance. In this work, we propose Shading-SfM-Net, a novel framework for simultaneously learning a shape-from-shading network based on the inverse rendering constraint and a structure-from-motion framework based on warped keypoint and geometric consistency, to improve structure-from-motion and surface reconstruction for low-texture indoor scenes. Shading-SfM-Net tightly incorporates the surface shape consistency and 3D geometric registration loss in order to dig into their mutual information and further overcome the instability on flat regions. We evaluate the proposed framework on texture-less indoor scenes (NYUv2 and ScanNet), and show that by simultaneously learning shading, motion and shape, our pipeline is able to achieve state-of-the-art performance with superior generalization capability for unseen texture-less datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_Shading_Meets_Motion_Self-supervised_Indoor_3D_Reconstruction_Via_Simultaneous_Shape-from-Shading_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Lu_2025_CVPR,
    author    = {Lu, Guoyu},
    title     = {Shading Meets Motion: Self-supervised Indoor 3D Reconstruction Via Simultaneous Shape-from-Shading and Structure-from-Motion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16508-16519}
}


title: Believing is Seeing: Unobserved Object Detection using Generative Models
authors: Subhransu S. Bhattacharjee, Dylan Campbell, Rahul Shome
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19366-19377
affiliations: ["School of Computing, The Australian National University"]
cities: ["Canberra"]
countries: ["Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bhattacharjee_Believing_is_Seeing_Unobserved_Object_Detection_using_Generative_Models_CVPR_2025_paper.html

abstract:
Can objects that are not visible in an image---but are in the vicinity of the camera---be detected? This study introduces the novel tasks of 2D, 2.5D and 3D unobserved object detection for predicting the location of nearby objects that are occluded or lie outside the image frame.  We adapt several state-of-the-art pre-trained generative models to address this task, including 2D and 3D diffusion models and vision-language models, and show that they can be used to infer the presence of objects that are not directly observed.  To benchmark this task, we propose a suite of metrics that capture different aspects of performance.  Our empirical evaluation on indoor scenes from the RealEstate10k and NYU Depth v2 datasets demonstrate results that motivate the use of generative models for the unobserved object detection task.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bhattacharjee_Believing_is_Seeing_Unobserved_Object_Detection_using_Generative_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bhattacharjee_Believing_is_Seeing_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bhattacharjee_2025_CVPR,
    author    = {Bhattacharjee, Subhransu S. and Campbell, Dylan and Shome, Rahul},
    title     = {Believing is Seeing: Unobserved Object Detection using Generative Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19366-19377}
}

raw_affiliation:
[{"affiliation": "School of Computing, The Australian National University", "city": "Canberra", "country": "Australia"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22864-22874
cities: ["Tokyo", "Hangzhou", "Hangzhou", "Hangzhou", "Hong Kong"]
countries: ["Japan", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shi_MotionStone_Decoupled_Motion_Intensity_Modulation_with_Diffusion_Transformer_for_Image-to-Video_CVPR_2025_paper.html

title:
MotionStone: Decoupled Motion Intensity Modulation with Diffusion Transformer for Image-to-Video Generation

authors:
Shuwei Shi, Biao Gong, Xi Chen, Dandan Zheng, Shuai Tan, Zizheng Yang, Yuyuan Li, Jingwen He, Kecheng Zheng, Jingdong Chen, Ming Yang, Yinqiang Zheng

abstract:
The image-to-video (I2V) generation is conditioned on the static image, which has been enhanced recently by the motion intensity as an additional control signal. These motion-aware models are appealing to generate diverse motion patterns, yet there lacks a reliable motion estimator for training such models on large-scale video set in the wild. Traditional metrics, e.g., SSIM or optical flow, are hard to generalize to arbitrary videos, while, it is very tough for human annotators to label the abstract motion intensity neither. Furthermore, the motion intensity shall reveal both local object motion and global camera movement, which has not been studied before. This paper addresses the challenge with a new motion estimator, capable of measuring the decoupled motion intensities of objects and cameras in video. We leverage the contrastive learning on randomly paired videos and distinguish the video with greater motion intensity. Such a paradigm is friendly for annotation and easy to scale up to achieve stable performance on motion estimation. We then present a new I2V model, named MotionStone, developed with the decoupled motion estimator. Experimental results demonstrate the stability of the proposed motion estimator and the state-of-the-art performance of MotionStone on I2V generation. These advantages warrant the decoupled motion estimator to serve as a general plug-in enhancer for both data processing and video generation training.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shi_MotionStone_Decoupled_Motion_Intensity_Modulation_with_Diffusion_Transformer_for_Image-to-Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shi_MotionStone_Decoupled_Motion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shi_2025_CVPR,
    author    = {Shi, Shuwei and Gong, Biao and Chen, Xi and Zheng, Dandan and Tan, Shuai and Yang, Zizheng and Li, Yuyuan and He, Jingwen and Zheng, Kecheng and Chen, Jingdong and Yang, Ming and Zheng, Yinqiang},
    title     = {MotionStone: Decoupled Motion Intensity Modulation with Diffusion Transformer for Image-to-Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22864-22874}
}

raw_affiliation:
[{"affiliation": "The University of Tokyo", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Tongyi Lab", "city": "Hangzhou", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["The University of Tokyo", "Ant Group", "Tongyi Lab", "Zhejiang University", "The Chinese University of Hong Kong"]


title: NLPrompt: Noise-Label Prompt Learning for Vision-Language Models
authors: Bikang Pan, Qun Li, Xiaoying Tang, Wei Huang, Zhen Fang, Feng Liu, Jingya Wang, Jingyi Yu, Ye Shi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19963-19973
cities: ["Shanghai", "Shenzhen", "Tokyo", "Sydney", "Melbourne"]
countries: ["China", "China", "Japan", "Australia", "Australia"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pan_NLPrompt_Noise-Label_Prompt_Learning_for_Vision-Language_Models_CVPR_2025_paper.html

abstract:
The emergence of vision-language foundation models, such as CLIP, has revolutionized image-text representation, enabling a broad range of applications via prompt learning. Despite its promise, real-world datasets often contain noisy labels that can degrade prompt learning performance. In this paper, we demonstrate that using mean absolute error (MAE) loss in prompt learning, named PromptMAE, significantly enhances robustness against noisy labels while maintaining high accuracy. Though MAE is straightforward and recognized for its robustness, it is rarely used in noisy-label learning due to its slow convergence and poor performance outside prompt learning scenarios. To elucidate the robustness of PromptMAE, we leverage feature learning theory to show that MAE can suppress the influence of noisy samples, thereby improving the signal-to-noise ratio and enhancing overall robustness. Additionally, we introduce PromptOT, a prompt-based optimal transport data purification method to enhance the robustness further. PromptOT employs text encoder representations in vision-language models as prototypes to construct an optimal transportation matrix. This matrix effectively partitions datasets into clean and noisy subsets, allowing for the application of cross-entropy loss to the clean subset and MAE loss to the noisy subset. Our Noise-Label Prompt Learning method, named NLPrompt, offers a simple and efficient approach that leverages the expressive representation and precise alignment capabilities of vision-language models for robust prompt learning.  We validate NLPrompt through extensive experiments across various noise settings, demonstrating significant performance improvements.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pan_NLPrompt_Noise-Label_Prompt_Learning_for_Vision-Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pan_NLPrompt_Noise-Label_Prompt_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Pan_2025_CVPR,
    author    = {Pan, Bikang and Li, Qun and Tang, Xiaoying and Huang, Wei and Fang, Zhen and Liu, Feng and Wang, Jingya and Yu, Jingyi and Shi, Ye},
    title     = {NLPrompt: Noise-Label Prompt Learning for Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19963-19973}
}

raw_affiliation:
[{"affiliation": "ShanghaiTech University", "city": "Shanghai", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Shenzhen", "country": "China"}, {"affiliation": "RIKEN Center for Advanced Intelligence Project", "city": "Tokyo", "country": "Japan"}, {"affiliation": "University of Technology Sydney", "city": "Sydney", "country": "Australia"}, {"affiliation": "University of Melbourne", "city": "Melbourne", "country": "Australia"}]

affiliations:
["ShanghaiTech University", "The Chinese University of Hong Kong", "RIKEN Center for Advanced Intelligence Project", "University of Technology Sydney", "University of Melbourne"]


title: MEGA: Masked Generative Autoencoder for Human Mesh Recovery
authors: Guénolé Fiche, Simon Leglaive, Xavier Alameda-Pineda, Francesc Moreno-Noguer
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5366-5378
cities: ["Paris", "Grenoble", "Grenoble", "Seattle"]
countries: ["France", "France", "France", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fiche_MEGA_Masked_Generative_Autoencoder_for_Human_Mesh_Recovery_CVPR_2025_paper.html

abstract:
Human Mesh Recovery (HMR) from a single RGB image is a highly ambiguous problem, as an infinite set of 3D interpretations can explain the 2D observation equally well. Nevertheless, most HMR methods overlook this issue and make a single prediction without accounting for this ambiguity. A few approaches generate a distribution of human meshes, enabling the sampling of multiple predictions; however, none of them is competitive with the latest single-output model when making a single prediction. This work proposes a new approach based on masked generative modeling. By tokenizing the human pose and shape, we formulate the HMR task as generating a sequence of discrete tokens conditioned on an input image. We introduce MEGA, a MaskEd Generative Autoencoder trained to recover human meshes from images and partial human mesh token sequences. Given an image, our flexible generation scheme allows us to predict a single human mesh in deterministic mode or to generate multiple human meshes in stochastic mode. Experiments on in-the-wild benchmarks show that MEGA achieves state-of-the-art performance in deterministic and stochastic modes, outperforming single-output and multi-output approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fiche_MEGA_Masked_Generative_Autoencoder_for_Human_Mesh_Recovery_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fiche_MEGA_Masked_Generative_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fiche_2025_CVPR,
    author    = {Fiche, Gu\'enol\'e and Leglaive, Simon and Alameda-Pineda, Xavier and Moreno-Noguer, Francesc},
    title     = {MEGA: Masked Generative Autoencoder for Human Mesh Recovery},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5366-5378}
}

raw_affiliation:
[{"affiliation": "CentraleSup\u00b4elec (IETR UMR CNRS 6164)", "city": "Paris", "country": "France"}, {"affiliation": "Naver Labs Europe", "city": "Grenoble", "country": "France"}, {"affiliation": "Inria, Univ. Grenoble Alpes, CNRS, LJK", "city": "Grenoble", "country": "France"}, {"affiliation": "Amazon", "city": "Seattle", "country": "USA"}]

affiliations:
["CentraleSup\u00b4elec (IETR UMR CNRS 6164)", "Naver Labs Europe", "Inria, Univ. Grenoble Alpes, CNRS, LJK", "Amazon"]


title: PBR-NeRF: Inverse Rendering with Physics-Based Neural Fields
authors: Sean Wu, Shamik Basu, Tim Broedermann, Luc Van Gool, Christos Sakaridis
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10974-10984
affiliations: ["ETH Z \"urich", "University Of Bologna", "INSAIT , So\"fia University St. Kliment Ohridski"]
cities: ["Z \"urich", "Bologna", "So\"fia"]
countries: ["Switzerland", "Italy", "Bulgaria"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_PBR-NeRF_Inverse_Rendering_with_Physics-Based_Neural_Fields_CVPR_2025_paper.html

abstract:
We tackle the ill-posed inverse rendering problem in 3D reconstruction with a Neural Radiance Field (NeRF) approach informed by Physics-Based Rendering (PBR) theory, named PBR-NeRF. Our method addresses a key limitation in most NeRF and 3D Gaussian Splatting approaches: they estimate view-dependent appearance without modeling scene materials and illumination. To address this limitation, we present an inverse rendering (IR) model capable of jointly estimating scene geometry, materials, and illumination. Our model builds upon recent NeRF-based IR approaches, but crucially introduces two novel physics-based priors that better constrain the IR estimation. Our priors are rigorously formulated as intuitive loss terms and achieve state-of-the-art material estimation without compromising novel view synthesis quality. Our method is easily adaptable to other inverse rendering and 3D reconstruction frameworks that require material estimation. We demonstrate the importance of extending current neural rendering approaches to fully model scene properties beyond geometry and view-dependent appearance. Code is publicly available at: https://github.com/s3anwu/pbrnerf.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_PBR-NeRF_Inverse_Rendering_with_Physics-Based_Neural_Fields_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_PBR-NeRF_Inverse_Rendering_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Sean and Basu, Shamik and Broedermann, Tim and Van Gool, Luc and Sakaridis, Christos},
    title     = {PBR-NeRF: Inverse Rendering with Physics-Based Neural Fields},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10974-10984}
}

raw_affiliation:
[{"affiliation": "ETH Z \"urich", "city": "Z \"urich", "country": "Switzerland"}, {"affiliation": "University Of Bologna", "city": "Bologna", "country": "Italy"}, {"affiliation": "INSAIT , So\"fia University St. Kliment Ohridski", "city": "So\"fia", "country": "Bulgaria"}]


title: Disentangling Safe and Unsafe Image Corruptions via Anisotropy and Locality
authors: Ramchandran Muthukumar, Ambar Pal, Jeremias Sulam, Rene Vidal
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9954-9963
affiliations: ["Johns Hopkins University", "Amazon Web Services", "University of Pennsylvania"]
cities: ["Baltimore", "Seattle", "Philadelphia"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Muthukumar_Disentangling_Safe_and_Unsafe_Image_Corruptions_via_Anisotropy_and_Locality_CVPR_2025_paper.html

abstract:
State-of-the-art machine learning systems are vulnerable to small perturbations to their input, where _small_ is defined according to a threat model that assigns a positive threat to each perturbation. Most prior works define a task-agnostic, isotropic, and global threat, like the l_p norm, where the magnitude of the perturbation fully determines the degree of the threat and neither the direction of the attack nor its position in space matter. However, common corruptions in computer vision, such as blur, compression, or occlusions, are not well captured by such treat models. This paper proposes a novel threat model called \texttt Projected Displacement  (PD) to study robustness beyond existing isotropic and global threat models. The proposed threat model measures the threat of a perturbation via its alignment with _unsafe directions_, defined as directions in the input space along which a perturbation of sufficient magnitude changes the ground truth class label. Unsafe directions are identified locally for each input based on observed training data. In this way, the PD-threat model exhibits anisotropy and locality. The PD-threat model is computationally efficient and can be easily integrated into existing robustness pipelines. Experiments on Imagenet-1k data indicate that, for any input, the set of perturbations with small PD threat includes _safe_ perturbations of large l_p norm that preserve the true label, such as noise, blur and compression, while simultaneously excluding _unsafe_ perturbations that alter the true label. Unlike perceptual threat models based on embeddings of large-vision models, the PD-threat model can be readily computed for arbitrary classification tasks without pre-training or finetuning. Further additional task information such as sensitivity to image regions or concept hierarchies can be easily integrated into the assessment of threat and thus the PD threat model presents practitioners a flexible, task-driven threat specification that alleviates the limitations of l_p-threat models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Muthukumar_Disentangling_Safe_and_Unsafe_Image_Corruptions_via_Anisotropy_and_Locality_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Muthukumar_Disentangling_Safe_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Muthukumar_2025_CVPR,
    author    = {Muthukumar, Ramchandran and Pal, Ambar and Sulam, Jeremias and Vidal, Rene},
    title     = {Disentangling Safe and Unsafe Image Corruptions via Anisotropy and Locality},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9954-9963}
}

raw_affiliation:
[{"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}, {"affiliation": "Amazon Web Services", "city": "Seattle", "country": "USA"}, {"affiliation": "University of Pennsylvania", "city": "Philadelphia", "country": "USA"}]


title: Prometheus: 3D-Aware Latent Diffusion Models for Feed-Forward Text-to-3D Scene Generation
authors: Yuanbo Yang, Jiahao Shao, Xinyang Li, Yujun Shen, Andreas Geiger, Yiyi Liao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2857-2869
affiliations: ["Zhejiang University", "Xiamen University", "Ant Group", "University of T\textasciicircumbingen"]
cities: ["Hangzhou", "Xiamen", "Hangzhou", "T\u00fcbingen"]
countries: ["China", "China", "China", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Prometheus_3D-Aware_Latent_Diffusion_Models_for_Feed-Forward_Text-to-3D_Scene_Generation_CVPR_2025_paper.html

abstract:
In this work, we introduce Prometheus, a 3D-aware latent diffusion model for text-to-3D generation at both object and scene levels in seconds. We formulate 3D scene generation as multi-view, feed-forward, pixel-aligned 3D Gaussian generation within the latent diffusion paradigm. To ensure generalizability, we build our model upon pre-trained text-to-image generation model with only minimal adjustments and further train it using a large number of images from both single-view and multi-view datasets. Furthermore, we introduce an RGB-D latent space into 3D Gaussian generation to disentangle appearance and geometry information, enabling efficient feed-forward generation of 3D Gaussians with better fidelity and geometry. Extensive experimental results demonstrate the effectiveness of our method in both feed-forward 3D Gaussian reconstruction and text-to-3D generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Prometheus_3D-Aware_Latent_Diffusion_Models_for_Feed-Forward_Text-to-3D_Scene_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Prometheus_3D-Aware_Latent_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Yuanbo and Shao, Jiahao and Li, Xinyang and Shen, Yujun and Geiger, Andreas and Liao, Yiyi},
    title     = {Prometheus: 3D-Aware Latent Diffusion Models for Feed-Forward Text-to-3D Scene Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2857-2869}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "University of T\textasciicircumbingen", "city": "T\u00fcbingen", "country": "Germany"}]


title: No Pains, More Gains: Recycling Sub-Salient Patches for Efficient High-Resolution Image Recognition
authors: Rong Qin, Xin Liu, Xingyu Liu, Jiaxuan Liu, Jinglei Shi, Liang Lin, Jufeng Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14965-14975
cities: ["Tianjin", "Shenzhen", "Shenzhen", "Guangzhou", "Dalian"]
countries: ["China", "China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qin_No_Pains_More_Gains_Recycling_Sub-Salient_Patches_for_Efficient_High-Resolution_CVPR_2025_paper.html

abstract:
Over the last decade, many notable methods have emerged to tackle the computational resource challenge of the high resolution image recognition (HRIR). They typically focus on identifying and aggregating a few salient regions for classification, discarding sub-salient areas for low training consumption. Nevertheless, many HRIR tasks necessitate the exploration of wider regions to model objects and contexts, which limits their performance in such scenarios. To address this issue, we present a DBPS strategy to enable training with more patches at low consumption. Specifically, in addition to a fundamental buffer that stores the embeddings of most salient patches, DBPS further employs an auxiliary buffer to recycle those sub-salient ones. To reduce the computational cost associated with gradients of sub-salient patches, these patches are primarily used in the forward pass to provide sufficient information for classification. Meanwhile, only the gradients of the salient patches are back-propagated to update the entire network. Moreover, we design a Multiple Instance Learning (MIL) architecture that leverages aggregated information from salient patches to filter out uninformative background within sub-salient patches for better accuracy. Besides, we introduce the random patch drop to accelerate training process and uncover informative regions. Experiment results demonstrate the superiority of our method in terms of both accuracy and training consumption against other advanced methods. The code is available in the https://github.com/Qinrong-NKU/DBPS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qin_No_Pains_More_Gains_Recycling_Sub-Salient_Patches_for_Efficient_High-Resolution_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Qin_2025_CVPR,
    author    = {Qin, Rong and Liu, Xin and Liu, Xingyu and Liu, Jiaxuan and Shi, Jinglei and Lin, Liang and Yang, Jufeng},
    title     = {No Pains, More Gains: Recycling Sub-Salient Patches for Efficient High-Resolution Image Recognition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14965-14975}
}

raw_affiliation:
[{"affiliation": "VCIP & TMCC & DISSec, College of Computer Science, Nankai University, Tianjin, China.", "city": "Tianjin", "country": "China"}, {"affiliation": "Pengcheng Laboratory, Shenzhen, China.", "city": "Shenzhen", "country": "China"}, {"affiliation": "Nankai International Advanced Research Institute (SHENZHEN\u00b7FUTIAN), Shenzhen, China.", "city": "Shenzhen", "country": "China"}, {"affiliation": "School of Computer Science and Engineering, Sun Y at-Sen University, Guangzhou, China.", "city": "Guangzhou", "country": "China"}, {"affiliation": "Key Lab of SCCI, Dalian University of Technology, Dalian, China.", "city": "Dalian", "country": "China"}]

affiliations:
["VCIP & TMCC & DISSec, College of Computer Science, Nankai University, Tianjin, China.", "Pengcheng Laboratory, Shenzhen, China.", "Nankai International Advanced Research Institute (SHENZHEN\u00b7FUTIAN), Shenzhen, China.", "School of Computer Science and Engineering, Sun Y at-Sen University, Guangzhou, China.", "Key Lab of SCCI, Dalian University of Technology, Dalian, China."]


title: SphereUFormer: A U-Shaped Transformer for Spherical 360 Perception
authors: Yaniv Benny, Lior Wolf
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 940-950
affiliations: ["Tel Aviv University", "Tel Aviv University"]
cities: ["Tel Aviv", "Tel Aviv"]
countries: ["Israel", "Israel"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Benny_SphereUFormer_A_U-Shaped_Transformer_for_Spherical_360_Perception_CVPR_2025_paper.html

abstract:
This paper proposes a novel method for omnidirectional 360\degree perception. Most common previous methods relied on equirectangular projection. This representation is easily applicable to 2D operation layers but introduces distortions into the image. Other methods attempted to remove the distortions by maintaining a sphere representation but relied on complicated convolution kernels that failed to show competitive results. In this work, we introduce a transformer-based architecture that, by incorporating a novel "Spherical Local Self-Attention" and other spherically-oriented modules, successfully operates in the spherical domain and outperforms the state-of-the-art in 360\degree perception benchmarks for depth estimation and semantic segmentation. Our code is available at https://github.com/yanivbenny/sphere_uformer.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Benny_SphereUFormer_A_U-Shaped_Transformer_for_Spherical_360_Perception_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Benny_SphereUFormer_A_U-Shaped_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Benny_2025_CVPR,
    author    = {Benny, Yaniv and Wolf, Lior},
    title     = {SphereUFormer: A U-Shaped Transformer for Spherical 360 Perception},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {940-950}
}

raw_affiliation:
[{"affiliation": "Tel Aviv University", "city": "Tel Aviv", "country": "Israel"}, {"affiliation": "Tel Aviv University", "city": "Tel Aviv", "country": "Israel"}]


authors: Yankai Jiang, Peng Zhang, Donglin Yang, Yuan Tian, Hai Lin, Xiaosong Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25971-25981
affiliations: ["Shanghai AI Laboratory", "Zhejiang University", "The University of British Columbia"]
cities: ["Shanghai", "Hangzhou", "Vancouver"]
countries: ["China", "China", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_Advancing_Generalizable_Tumor_Segmentation_with_Anomaly-Aware_Open-Vocabulary_Attention_Maps_and_CVPR_2025_paper.html

title:
Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models

abstract:
We explore Generalizable Tumor Segmentation, aiming to train a single model for zero-shot tumor segmentation across diverse anatomical regions. Existing methods face limitations related to segmentation quality, scalability, and the range of applicable imaging modalities. In this paper, we uncover the potential of the internal representations within frozen medical foundation diffusion models as highly efficient zero-shot learners for tumor segmentation by introducing a novel framework named DiffuGTS. DiffuGTS creates anomaly-aware open-vocabulary attention maps based on text prompts to enable generalizable anomaly segmentation without being restricted by a predefined training category list. To further improve and refine anomaly segmentation masks, DiffuGTS leverages the diffusion model, transforming pathological regions into high-quality pseudo-healthy counterparts through latent space inpainting, and applies a novel pixel-level and feature-level residual learning approach, resulting in segmentation masks with significantly enhanced quality and generalization. Comprehensive experiments on four datasets and seven tumor categories demonstrate the superior performance of our method, surpassing current state-of-the-art models across multiple zero-shot settings. Codes are available at https://github.com/Yankai96/DiffuGTS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_Advancing_Generalizable_Tumor_Segmentation_with_Anomaly-Aware_Open-Vocabulary_Attention_Maps_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_Advancing_Generalizable_Tumor_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Yankai and Zhang, Peng and Yang, Donglin and Tian, Yuan and Lin, Hai and Wang, Xiaosong},
    title     = {Advancing Generalizable Tumor Segmentation with Anomaly-Aware Open-Vocabulary Attention Maps and Frozen Foundation Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25971-25981}
}

raw_affiliation:
[{"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "The University of British Columbia", "city": "Vancouver", "country": "Canada"}]


title: Towards Generalizable Scene Change Detection
authors: Jae-Woo Kim, Ue-Hwan Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24463-24473
affiliations: ["Gwangju Institute of Science and Technology, Gwangju, South Korea"]
cities: ["Gwangju"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Towards_Generalizable_Scene_Change_Detection_CVPR_2025_paper.html

abstract:
While current state-of-the-art Scene Change Detection (SCD) approaches achieve impressive results in well-trained research data, they become unreliable under unseen environments and different temporal conditions; in-domain performance drops from 77.6% to 8.0% in a previously unseen environment and to 4.6% under a different temporal condition---calling for generalizable SCD and benchmark. In this work, we propose the Generalizable Scene Change Detection Framework (GeSCF), which addresses unseen domain performance and temporal consistency---to meet the growing demand for anything SCD. Our method leverages the pre-trained Segment Anything Model (SAM) in a zero-shot manner. For this, we design Initial Pseudo-mask Generation and Geometric-Semantic Mask Matching---seamlessly turning user-guided prompt and single-image based segmentation into scene change detection for a pair of inputs without guidance. Furthermore, we define the Generalizable Scene Change Detection (GeSCD) benchmark along with novel metrics and an evaluation protocol to facilitate SCD research in generalizability. In the process, we introduce the ChangeVPR dataset, a collection of challenging image pairs with diverse environmental scenarios---including urban, suburban, and rural settings. Extensive experiments across various datasets demonstrate that GeSCF achieves an average performance gain of 19.2% on existing SCD datasets and 30.0% on the ChangeVPR dataset, nearly doubling the prior art performance. We believe our work can lay a solid foundation for robust and generalizable SCD research.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Towards_Generalizable_Scene_Change_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_Towards_Generalizable_Scene_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Jae-Woo and Kim, Ue-Hwan},
    title     = {Towards Generalizable Scene Change Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24463-24473}
}

raw_affiliation:
[{"affiliation": "Gwangju Institute of Science and Technology, Gwangju, South Korea", "city": "Gwangju", "country": "South Korea"}]


authors: Yuchuan Li, Jae-Mo Kang, Il-Min Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10183-10192
affiliations: ["Queen\u2019s University", "Kyungpook National University", "Queen\u2019s University"]
cities: ["Kingston", "Daegu", "Kingston"]
countries: ["Canada", "South Korea", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Beyond_Clean_Training_Data_A_Versatile_and_Model-Agnostic_Framework_for_CVPR_2025_paper.html

title:
Beyond Clean Training Data: A Versatile and Model-Agnostic Framework for Out-of-Distribution Detection with Contaminated Training Data

abstract:
In real-world AI applications, training datasets are often contaminated, containing a mix of in-distribution (ID) and out-of-distribution (OOD) samples without labels. This contamination poses a significant challenge for developing and training OOD detection models, as nearly all existing methods assume access to a clean training dataset of only ID samples--a condition rarely met in real-world scenarios. Customizing each existing OOD detection method to handle such contamination is impractical, given the vast number of diverse methods designed for clean data. To address this issue, we propose a universal, model-agnostic framework that integrates with nearly all existing OOD detection methods, enabling training on contaminated datasets while achieving high OOD detection accuracy on test datasets. Additionally, our framework provides an accurate estimation of the unknown proportion of OOD samples within the training dataset--an important and distinct challenge in its own right. Our approach introduces a novel dynamic weighting function and transition mechanism within an iterative training structure, enabling both reliable estimation of the OOD sample proportion of the training data and precise OOD detection on test data. Extensive evaluations across diverse datasets, including ImageNet-1k, demonstrate that our framework accurately estimates OOD sample proportions of training data and substantially enhances OOD detection accuracy on test data.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Beyond_Clean_Training_Data_A_Versatile_and_Model-Agnostic_Framework_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Beyond_Clean_Training_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Yuchuan and Kang, Jae-Mo and Kim, Il-Min},
    title     = {Beyond Clean Training Data: A Versatile and Model-Agnostic Framework for Out-of-Distribution Detection with Contaminated Training Data},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10183-10192}
}

raw_affiliation:
[{"affiliation": "Queen\u2019s University", "city": "Kingston", "country": "Canada"}, {"affiliation": "Kyungpook National University", "city": "Daegu", "country": "South Korea"}, {"affiliation": "Queen\u2019s University", "city": "Kingston", "country": "Canada"}]


title: Incomplete Multi-modal Brain Tumor Segmentation via Learnable Sorting State Space Model
authors: Zheyu Zhang, Yayuan Lu, Feipeng Ma, Yueyi Zhang, Huanjing Yue, Xiaoyan Sun
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25982-25992
cities: ["Hefei", "Hefei", "Tianjin"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Incomplete_Multi-modal_Brain_Tumor_Segmentation_via_Learnable_Sorting_State_Space_CVPR_2025_paper.html

abstract:
Brain tumor segmentation plays a crucial role in clinical diagnosis, yet the frequent unavailability of certain MRI modalities poses a significant challenge. In this paper, we introduce the Learnable Sorting State Space Model (LS3M), a novel framework designed to maximize the utilization of available modalities for brain tumor segmentation. LS3M excels at efficiently modeling long-range dependencies based on the Mamba design, while incorporating differentiable permutation matrices that reorder input sequences based on modality-specific characteristics. This dynamic reordering ensures that critical spatial inductive biases and long-range semantic correlations inherent in 3D brain MRI are preserved, which is crucial for imcomplete multi-modal brain tumor segmentation.Once the input sequences are reordered using the generated permutation matrix, the Series State Space Model (S3M) block models the relationships between them, capturing both local and long-range dependencies. This enables effective representation of intra-modal and inter-modal relationships, significantly improving segmentation accuracy. Extensive experiments on the BraTS2018 and BraTS2020 datasets demonstrate that LS3M outperforms existing methods, offering a robust solution for brain tumor segmentation, particularly in scenarios with missing modalities.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Incomplete_Multi-modal_Brain_Tumor_Segmentation_via_Learnable_Sorting_State_Space_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Incomplete_Multi-modal_Brain_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Zheyu and Lu, Yayuan and Ma, Feipeng and Zhang, Yueyi and Yue, Huanjing and Sun, Xiaoyan},
    title     = {Incomplete Multi-modal Brain Tumor Segmentation via Learnable Sorting State Space Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25982-25992}
}

raw_affiliation:
[{"affiliation": "MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing, Institute of Artificial Intelligence, Hefei Comprehensive National Science Center", "city": "Hefei", "country": "China"}, {"affiliation": "School of Electrical and Information Engineering, Tianjin University", "city": "Tianjin", "country": "China"}]

affiliations:
["MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, University of Science and Technology of China", "Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing, Institute of Artificial Intelligence, Hefei Comprehensive National Science Center", "School of Electrical and Information Engineering, Tianjin University"]


title: FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors
authors: Changlong Shi, He Zhao, Bingjie Zhang, Mingyuan Zhou, Dandan Guo, Yi Chang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30651-30660
affiliations: ["Jilin University", "CSIRO\u2019s Data61", "The University of Texas at Austin"]
cities: ["Changchun", "Sydney", "Austin"]
countries: ["China", "Australia", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shi_FedAWA_Adaptive_Optimization_of_Aggregation_Weights_in_Federated_Learning_Using_CVPR_2025_paper.html

abstract:
Federated Learning (FL) has emerged as a promising framework for distributed machine learning, enabling collaborative model training without sharing local data, thereby preserving privacy and enhancing security. However, data heterogeneity resulting from differences across user behaviors, preferences, and device characteristics poses a significant challenge for federated learning. Most previous works overlook the adjustment of aggregation weights, relying solely on dataset size for weight assignment, which often leads to unstable convergence and reduced model performance. Recently, several studies have sought to refine aggregation strategies by incorporating dataset characteristics and model alignment. However, adaptively adjusting aggregation weights while ensuring data security--without requiring additional proxy data--remains a significant challenge. In this work, we propose Federated learning with Adaptive Weight Aggregation (FedAWA), a novel method that adaptively adjusts aggregation weights based on client vectors during the learning process. The client vector captures the direction of model updates, reflecting local data variations, and is used to optimize the aggregation weight without requiring additional datasets or violating privacy. By assigning higher aggregation weights to local models whose updates align closely with the global optimization direction, FedAWA enhances the stability and generalization of the global model. Extensive experiments under diverse scenarios demonstrate the superiority of our method, providing a promising solution to the challenges of data heterogeneity in federated learning.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shi_FedAWA_Adaptive_Optimization_of_Aggregation_Weights_in_Federated_Learning_Using_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shi_FedAWA_Adaptive_Optimization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shi_2025_CVPR,
    author    = {Shi, Changlong and Zhao, He and Zhang, Bingjie and Zhou, Mingyuan and Guo, Dandan and Chang, Yi},
    title     = {FedAWA: Adaptive Optimization of Aggregation Weights in Federated Learning Using Client Vectors},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30651-30660}
}

raw_affiliation:
[{"affiliation": "Jilin University", "city": "Changchun", "country": "China"}, {"affiliation": "CSIRO\u2019s Data61", "city": "Sydney", "country": "Australia"}, {"affiliation": "The University of Texas at Austin", "city": "Austin", "country": "USA"}]


authors: Xingchao Yang, Takafumi Taketomi, Yuki Endo, Yoshihiro Kanamori
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 326-337
affiliations: ["CyberAgent", "University of Tsukuba"]
cities: ["Tokyo", "Tsukuba"]
countries: ["Japan", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_FreeUV_Ground-Truth-Free_Realistic_Facial_UV_Texture_Recovery_via_Cross-Assembly_Inference_CVPR_2025_paper.html

title:
FreeUV: Ground-Truth-Free Realistic Facial UV Texture Recovery via Cross-Assembly Inference Strategy

abstract:
Recovering high-quality 3D facial textures from single-view 2D images is a challenging task, especially under constraints of limited data and complex facial details such as makeup, wrinkles, and occlusions. In this paper, we introduce FreeUV, a novel ground-truth-free UV texture recovery framework that eliminates the need for annotated or synthetic UV data. FreeUV leverages pre-trained stable diffusion model alongside a Cross-Assembly inference strategy to fulfill this objective. In FreeUV, separate networks are trained independently to focus on realistic appearance and structural consistency, and these networks are combined during inference to generate coherent textures. Our approach accurately captures intricate facial features and demonstrates robust performance across diverse poses and occlusions. Extensive experiments validate FreeUV's effectiveness, with results surpassing state-of-the-art methods in both quantitative and qualitative metrics. Additionally, FreeUV enables new applications, including local editing, facial feature interpolation, and multi-view texture recovery. By reducing data requirements, FreeUV offers a scalable solution for generating high-fidelity 3D facial textures suitable for real-world scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_FreeUV_Ground-Truth-Free_Realistic_Facial_UV_Texture_Recovery_via_Cross-Assembly_Inference_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_FreeUV_Ground-Truth-Free_Realistic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Xingchao and Taketomi, Takafumi and Endo, Yuki and Kanamori, Yoshihiro},
    title     = {FreeUV: Ground-Truth-Free Realistic Facial UV Texture Recovery via Cross-Assembly Inference Strategy},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {326-337}
}

raw_affiliation:
[{"affiliation": "CyberAgent", "city": "Tokyo", "country": "Japan"}, {"affiliation": "University of Tsukuba", "city": "Tsukuba", "country": "Japan"}]


authors: Zitang Zhou, Ke Mei, Yu Lu, Tianyi Wang, Fengyun Rao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3152-3162
cities: ["Shenzhen", "Beijing", "Hangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_HarmonySet_A_Comprehensive_Dataset_for_Understanding_Video-Music_Semantic_Alignment_and_CVPR_2025_paper.html

title:
HarmonySet: A Comprehensive Dataset for Understanding Video-Music Semantic Alignment and Temporal Synchronization

abstract:
This paper introduces HarmonySet, a comprehensive dataset designed to advance video-music understanding. HarmonySet consists of 48,328 diverse video-music pairs, annotated with detailed information on rhythmic synchronization, emotional alignment, thematic coherence, and cultural relevance. We propose a multi-step human-machine collaborative framework for efficient annotation, combining human insights with machine-generated descriptions to identify key transitions and assess alignment across multiple dimensions. Additionally, we introduce a novel evaluation framework with tasks and metrics to assess the multi-dimensional alignment of video and music, including rhythm, emotion, theme, and cultural context. Our extensive experiments demonstrate that HarmonySet, along with the proposed evaluation framework, significantly improves the ability of multimodal models to capture and analyze the intricate relationships between video and music. Project page: https://harmonyset.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_HarmonySet_A_Comprehensive_Dataset_for_Understanding_Video-Music_Semantic_Alignment_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_HarmonySet_A_Comprehensive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Zitang and Mei, Ke and Lu, Yu and Wang, Tianyi and Rao, Fengyun},
    title     = {HarmonySet: A Comprehensive Dataset for Understanding Video-Music Semantic Alignment and Temporal Synchronization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3152-3162}
}

raw_affiliation:
[{"affiliation": "WeChat Vision, Tencent Inc.", "city": "Shenzhen", "country": "China"}, {"affiliation": "Beijing University of Posts and Telecommunications", "city": "Beijing", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]

affiliations:
["WeChat Vision, Tencent Inc.", "Beijing University of Posts and Telecommunications", "Zhejiang University"]


authors: Zichong Meng, Yiming Xie, Xiaogang Peng, Zeyu Han, Huaizu Jiang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27859-27871
raw_affiliation: [{"affiliation": "Northeastern University", "city": "Boston", "country": "USA"}]
affiliations: ["Northeastern University"]
cities: ["Boston"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Meng_Rethinking_Diffusion_for_Text-Driven_Human_Motion_Generation_Redundant_Representations_Evaluation_CVPR_2025_paper.html

title:
Rethinking Diffusion for Text-Driven Human Motion Generation: Redundant Representations, Evaluation, and Masked Autoregression

abstract:
Since 2023, Vector Quantization (VQ)-based discrete generation methods have rapidly dominated human motion generation, primarily surpassing diffusion-based continuous generation methods in standard performance metrics. However, VQ-based methods have inherent limitations. Representing continuous motion data as limited discrete tokens leads to inevitable information loss, reduces the diversity of generated motions, and restricts their ability to function effectively as motion priors or generation guidance. In contrast, the continuous space generation nature of diffusion-based methods makes them well-suited to address these limitations and with even potential for model scalability. In this work, we systematically investigate why current VQ-based methods perform well and explore the limitations of existing diffusion-based methods from the perspective of motion data representation and distribution. Drawing on these insights, we preserve the inherent strengths of a diffusion-based human motion generation model and gradually optimize it with inspiration from VQ-based approaches. Our approach introduces a human motion diffusion model enabled to perform masked autoregression, optimized with a reformed data representation and distribution. Additionally, we propose a more robust evaluation method to assess different approaches. Extensive experiments on various datasets demonstrate our method outperforms previous methods and achieves state-of-the-art performances.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Meng_Rethinking_Diffusion_for_Text-Driven_Human_Motion_Generation_Redundant_Representations_Evaluation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Meng_Rethinking_Diffusion_for_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Meng_2025_CVPR,
    author    = {Meng, Zichong and Xie, Yiming and Peng, Xiaogang and Han, Zeyu and Jiang, Huaizu},
    title     = {Rethinking Diffusion for Text-Driven Human Motion Generation: Redundant Representations, Evaluation, and Masked Autoregression},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27859-27871}
}


title: StyleMaster: Stylize Your Video with Artistic Generation and Translation
authors: Zixuan Ye, Huijuan Huang, Xintao Wang, Pengfei Wan, Di Zhang, Wenhan Luo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2630-2640
affiliations: ["Hong Kong University of Science and Technology", "KuaiShou Technology"]
cities: ["Hong Kong", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ye_StyleMaster_Stylize_Your_Video_with_Artistic_Generation_and_Translation_CVPR_2025_paper.html

abstract:
Style control has been popular in video generation models. Existing methods often generate videos far from the given style, cause content leakage, and struggle to transfer one video to the desired style. Our first observation is that the style extraction stage matters, whereas existing methods emphasize global style but ignore local textures. In order to bring texture features while preventing content leakage, we filter content-related patches while retaining style ones based on prompt-patch similarity; for global style extraction, we generate a paired style dataset through model illusion to facilitate contrastive learning, which greatly enhances the absolute style consistency. Moreover, to fill in the image-to-video gap, we train a lightweight motion adapter on still videos, which implicitly enhances stylization extent, and enables our image-trained model to be seamlessly applied to videos. Benefited from these efforts, our approach, StyleMaster, not only achieves significant improvement in both style resemblance and temporal coherence, but also can easily generalize to video style transfer with a gray tile ControlNet. Extensive experiments and visualizations demonstrate that StyleMaster significantly outperforms competitors, effectively generating high-quality stylized videos that align with textual content and closely resemble the style of reference images.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ye_StyleMaster_Stylize_Your_Video_with_Artistic_Generation_and_Translation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ye_StyleMaster_Stylize_Your_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ye_2025_CVPR,
    author    = {Ye, Zixuan and Huang, Huijuan and Wang, Xintao and Wan, Pengfei and Zhang, Di and Luo, Wenhan},
    title     = {StyleMaster: Stylize Your Video with Artistic Generation and Translation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2630-2640}
}

raw_affiliation:
[{"affiliation": "Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "KuaiShou Technology", "city": "Beijing", "country": "China"}]


title: Unsupervised Continual Domain Shift Learning with Multi-Prototype Modeling
authors: Haopeng Sun, Yingwei Zhang, Lumin Xu, Sheng Jin, Ping Luo, Chen Qian, Wentao Liu, Yiqiang Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10131-10141
cities: ["Beijing", "Beijing", "Hong Kong", "Hong Kong", "Hong Kong", "Shanghai"]
countries: ["China", "China", "China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_Unsupervised_Continual_Domain_Shift_Learning_with_Multi-Prototype_Modeling_CVPR_2025_paper.html

abstract:
In real-world applications, deep neural networks may encounter constantly changing environments, where the test data originates from continually shifting unlabeled target domains. This problem, known as Unsupervised Continual Domain Shift Learning (UCDSL), poses practical difficulties. Existing methods for UCDSL aim to learn domain-invariant representations for all target domains. However, due to the existence of adaptivity gap, the invariant representation may theoretically lead to large joint errors. To overcome the limitation, we propose a novel UCDSL method, called Multi-Prototype Modeling (MPM). Our model comprises two key components: (1) Multi-Prototype Learning (MPL) for acquiring domain-specific representations using multiple domain-specific prototypes. MPL achieves domain-specific error minimization instead of enforcing feature alignment across different domains. (2) Bi-Level Graph Enhancer (BiGE) for enhancing domain-level and category-level representations, resulting in more accurate predictions. We provide theoretical and empirical analysis to demonstrate the effectiveness of our proposed method. We evaluate our approach on multiple benchmark datasets and show that our model surpasses state-of-the-art methods across all datasets, highlighting its effectiveness and robustness in handling unsupervised continual domain shift learning. Codes will be publicly accessible.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_Unsupervised_Continual_Domain_Shift_Learning_with_Multi-Prototype_Modeling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_Unsupervised_Continual_Domain_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Haopeng and Zhang, Yingwei and Xu, Lumin and Jin, Sheng and Luo, Ping and Qian, Chen and Liu, Wentao and Chen, Yiqiang},
    title     = {Unsupervised Continual Domain Shift Learning with Multi-Prototype Modeling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10131-10141}
}

raw_affiliation:
[{"affiliation": "Beijing Key Lab. of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "SenseTime Research and Tetras.AI", "city": "Hong Kong", "country": "China"}, {"affiliation": "HKU Shanghai Intelligent Computing Research Center", "city": "Shanghai", "country": "China"}]

affiliations:
["Beijing Key Lab. of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "The Chinese University of Hong Kong", "The University of Hong Kong", "SenseTime Research and Tetras.AI", "HKU Shanghai Intelligent Computing Research Center"]


title: OmniGuard: Hybrid Manipulation Localization via Augmented Versatile Deep Image Watermarking
authors: Xuanyu Zhang, Zecheng Tang, Zhipei Xu, Runyi Li, Youmin Xu, Bin Chen, Feng Gao, Jian Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3008-3018
cities: ["Beijing", "Shenzhen", "Beijing", "Shenzhen"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_OmniGuard_Hybrid_Manipulation_Localization_via_Augmented_Versatile_Deep_Image_Watermarking_CVPR_2025_paper.html

abstract:
With the rapid growth of generative AI and its widespread application in image editing, new risks have emerged regarding the authenticity and integrity of digital content. Existing versatile watermarking approaches suffer from trade-offs between tamper localization precision and visual quality. Constrained by the limited flexibility of previous framework, their localized watermark must remain fixed across all images. Under AIGC-editing, their copyright extraction accuracy is also unsatisfactory. To address these challenges, we propose OmniGuard, a novel augmented versatile watermarking approach that integrates proactive embedding with passive, blind extraction for robust copyright protection and tamper localization. OmniGuard employs a hybrid forensic framework that enables flexible localization watermark selection and introduces a degradation-aware tamper extraction network for precise localization under challenging conditions. Additionally, a lightweight AIGC-editing simulation layer is designed to enhance robustness across global and local editing. Extensive experiments show that OmniGuard achieves superior fidelity, robustness, and flexibility. Compared to the recent state-of-the-art approach EditGuard, our method outperforms it by 4.25dB in PSNR of the container image, 20.7% in F1-Score under noisy conditions, and 14.8% in average bit accuracy.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_OmniGuard_Hybrid_Manipulation_Localization_via_Augmented_Versatile_Deep_Image_Watermarking_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_OmniGuard_Hybrid_Manipulation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Xuanyu and Tang, Zecheng and Xu, Zhipei and Li, Runyi and Xu, Youmin and Chen, Bin and Gao, Feng and Zhang, Jian},
    title     = {OmniGuard: Hybrid Manipulation Localization via Augmented Versatile Deep Image Watermarking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3008-3018}
}

raw_affiliation:
[{"affiliation": "School of Electronic and Computer Engineering, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Peking University Shenzhen Graduate School-Rabbitpre AIGC Joint Research Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "School of Arts, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School, Peking University", "city": "Shenzhen", "country": "China"}]

affiliations:
["School of Electronic and Computer Engineering, Peking University", "Peking University Shenzhen Graduate School-Rabbitpre AIGC Joint Research Laboratory", "School of Arts, Peking University", "Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School, Peking University"]


title: Open-Canopy: Towards Very High Resolution Forest Monitoring
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1395-1406
cities: ["Paris", "Paris", "Paris", "Paris", "Paris", "Paris", "Nancy"]
countries: ["France", "France", "France", "France", "France", "France", "France"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fogel_Open-Canopy_Towards_Very_High_Resolution_Forest_Monitoring_CVPR_2025_paper.html

authors:
Fajwel Fogel, Yohann Perron, Nikola Besic, Laurent Saint-André, Agnès Pellissier-Tanon, Martin Schwartz, Thomas Boudras, Ibrahim Fayad, Alexandre d'Aspremont, Loic Landrieu, Philippe Ciais

abstract:
Estimating canopy height and its changes at meter resolution from satellite imagery is a significant challenge in computer vision with critical environmental applications. However, the lack of open-access datasets at this resolution hinders the reproducibility and evaluation of models. We introduce Open-Canopy, the first open-access, country-scale benchmark for very high-resolution (1.5 m) canopy height estimation, covering over 87,000 km2 across France with 1.5 m resolution satellite imagery and aerial LiDAR data. Additionally, we present Open-Canopy-, a benchmark for canopy height change detection between images from different years at tree level--a challenging task for current computer vision models. We evaluate state-of-the-art architectures on these benchmarks, highlighting significant challenges and opportunities for improvement. Our datasets and code are publicly available at https://github.com/fajwel/Open-Canopy.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fogel_Open-Canopy_Towards_Very_High_Resolution_Forest_Monitoring_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fogel_Open-Canopy_Towards_Very_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fogel_2025_CVPR,
    author    = {Fogel, Fajwel and Perron, Yohann and Besic, Nikola and Saint-Andr\'e, Laurent and Pellissier-Tanon, Agn\`es and Schwartz, Martin and Boudras, Thomas and Fayad, Ibrahim and d'Aspremont, Alexandre and Landrieu, Loic and Ciais, Philippe},
    title     = {Open-Canopy: Towards Very High Resolution Forest Monitoring},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1395-1406}
}

raw_affiliation:
[{"affiliation": "LSCE/IPSL, CEA-CNRS-UVSQ", "city": "Paris", "country": "France"}, {"affiliation": "LIF, IGN, ENSG", "city": "Paris", "country": "France"}, {"affiliation": "LIGM, Ecole des Ponts, IP Paris, CNRS, UGE", "city": "Paris", "country": "France"}, {"affiliation": "CNRS & \u00c9cole Normale Sup\u00e9rieure", "city": "Paris", "country": "France"}, {"affiliation": "EFEO", "city": "Paris", "country": "France"}, {"affiliation": "Kayrros", "city": "Paris", "country": "France"}, {"affiliation": "INRAE, BEF", "city": "Nancy", "country": "France"}]

affiliations:
["LSCE/IPSL, CEA-CNRS-UVSQ", "LIF, IGN, ENSG", "LIGM, Ecole des Ponts, IP Paris, CNRS, UGE", "CNRS & \u00c9cole Normale Sup\u00e9rieure", "EFEO", "Kayrros", "INRAE, BEF"]


authors: Hao Yin, Guangzong Si, Zilei Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14625-14634
affiliations: ["University of Science and Technology of China"]
cities: ["Hefei"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yin_ClearSight_Visual_Signal_Enhancement_for_Object_Hallucination_Mitigation_in_Multimodal_CVPR_2025_paper.html

title:
ClearSight: Visual Signal Enhancement for Object Hallucination Mitigation in Multimodal Large Language Models

abstract:
Contrastive decoding strategies are widely used to mitigate object hallucinations in multimodal large language models (MLLMs). By reducing over-reliance on language priors, these strategies ensure that generated content remains closely grounded in visual inputs, producing contextually accurate outputs. Since contrastive decoding requires no additional training or external tools, it offers both computational efficiency and versatility, making it highly attractive. However, these methods present two main limitations: (1) bluntly suppressing language priors can compromise coherence and accuracy of generated content, and (2) processing contrastive inputs adds computational load, significantly slowing inference speed. To address these challenges, we propose Visual Amplification Fusion (VAF), a plug-and-play technique that enhances attention to visual signals within the model's middle layers, where modality fusion predominantly occurs. This approach enables more effective capture of visual features, reducing the model's bias toward language modality. Experimental results demonstrate that VAF significantly reduces hallucinations across various MLLMs without affecting inference speed, while maintaining coherence and accuracy in generated outputs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yin_ClearSight_Visual_Signal_Enhancement_for_Object_Hallucination_Mitigation_in_Multimodal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yin_ClearSight_Visual_Signal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yin_2025_CVPR,
    author    = {Yin, Hao and Si, Guangzong and Wang, Zilei},
    title     = {ClearSight: Visual Signal Enhancement for Object Hallucination Mitigation in Multimodal Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14625-14634}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}]


title: Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget
authors: Vikash Sehwag, Xianghao Kong, Jingtao Li, Michael Spranger, Lingjuan Lyu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28596-28608
affiliations: ["Sony AI", "University of California, Riverside"]
cities: ["Unknown", "Riverside"]
countries: ["Unknown", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sehwag_Stretching_Each_Dollar_Diffusion_Training_from_Scratch_on_a_Micro-Budget_CVPR_2025_paper.html

abstract:
As scaling laws in generative AI push performance, they simultaneously concentrate the development of these models among actors with large computational resources. With a focus on text-to-image (T2I) generative models, we aim to unlock this bottleneck by demonstrating very low-cost training of large-scale T2I diffusion transformer models. As the computational cost of transformers increases with the number of patches in each image, we propose randomly masking up to 75% of the image patches during training. We propose a deferred masking strategy that preprocesses all patches using a patch-mixer before masking, thus significantly reducing the performance degradation with masking, making it superior to model downscaling in reducing computational cost. We also incorporate the latest improvements in transformer architecture, such as the use of mixture-of-experts layers, to improve performance and further identify the critical benefit of using synthetic images in micro-budget training. Finally, using only 37M publicly available real and synthetic images, we train a 1.16 billion parameter sparse transformer with only 1,890 USD economical cost and achieve a 12.7 FID in zero-shot generation on the COCO dataset. Notably, our model achieves competitive performance across both automated and human-centric evaluations, as well as high-quality generations, while incurring 118xlower costs than Stable Diffusion models and 14xlower costs than the current state-of-the-art approach, which costs \28,400. We also further investigate the influence of synthetic images on performance and demonstrate that micro-budget training on only synthetic images is sufficient for achieving high-quality data generation. Our end-to-end training pipeline and model checkpoints are available at https://github.com/SonyResearch/micro_diffusion.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sehwag_Stretching_Each_Dollar_Diffusion_Training_from_Scratch_on_a_Micro-Budget_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sehwag_Stretching_Each_Dollar_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sehwag_2025_CVPR,
    author    = {Sehwag, Vikash and Kong, Xianghao and Li, Jingtao and Spranger, Michael and Lyu, Lingjuan},
    title     = {Stretching Each Dollar: Diffusion Training from Scratch on a Micro-Budget},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28596-28608}
}

raw_affiliation:
[{"affiliation": "Sony AI", "city": "Unknown", "country": "Unknown"}, {"affiliation": "University of California, Riverside", "city": "Riverside", "country": "USA"}]


title: Guiding Human-Object Interactions with Rich Geometry and Relations
authors: Mengqing Xue, Yifei Liu, Ling Guo, Shaoli Huang, Changxing Ding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22714-22723
affiliations: ["South China University of Technology", "Pazhou Lab", "Tencent AI Lab"]
cities: ["Guangzhou", "Guangzhou", "Unknown"]
countries: ["China", "China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xue_Guiding_Human-Object_Interactions_with_Rich_Geometry_and_Relations_CVPR_2025_paper.html

abstract:
Human-object interaction (HOI) synthesis is crucial for creating immersive and realistic experiences for applications such as virtual reality. Existing methods often rely on simplified object representations, such as the object's centroid or the nearest point to a human, to achieve physically plausible motions. However, these approaches may overlook geometric complexity, resulting in suboptimal interaction fidelity. To address this limitation, we introduce ROG, a novel diffusion-based framework that models the spatiotemporal relationships inherent in HOIs with rich geometric detail. For efficient object representation, we select boundary-focused and fine-detail key points from the object mesh, ensuring a comprehensive depiction of the object's geometry. This representation is used to construct an interactive distance field (IDF), capturing the robust HOI dynamics. Furthermore, we develop a diffusion-based relation model that integrates spatial and temporal attention mechanisms, enabling a better understanding of intricate HOI relationships. This relation model refines the generated motion's IDF, guiding the motion generation process to produce relation-aware and semantically aligned movements. Experimental evaluations demonstrate that ROG significantly outperforms state-of-the-art methods in the realism and semantic accuracy of synthesized HOIs. This paper's code will be released.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xue_Guiding_Human-Object_Interactions_with_Rich_Geometry_and_Relations_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xue_Guiding_Human-Object_Interactions_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xue_2025_CVPR,
    author    = {Xue, Mengqing and Liu, Yifei and Guo, Ling and Huang, Shaoli and Ding, Changxing},
    title     = {Guiding Human-Object Interactions with Rich Geometry and Relations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22714-22723}
}

raw_affiliation:
[{"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Pazhou Lab", "city": "Guangzhou", "country": "China"}, {"affiliation": "Tencent AI Lab", "city": "Unknown", "country": "Unknown"}]


title: TacoDepth: Towards Efficient Radar-Camera Depth Estimation with One-stage Fusion
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10523-10533
cities: ["Singapore", "Wuhan", "Unknown"]
countries: ["Singapore", "China", "Unknown"]
oral: yes
award_candidate: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_TacoDepth_Towards_Efficient_Radar-Camera_Depth_Estimation_with_One-stage_Fusion_CVPR_2025_paper.html

authors:
Yiran Wang, Jiaqi Li, Chaoyi Hong, Ruibo Li, Liusheng Sun, Xiao Song, Zhe Wang, Zhiguo Cao, Guosheng Lin

abstract:
Radar-Camera depth estimation aims to predict dense and accurate metric depth by fusing input images and Radar data. Model efficiency is crucial for this task in pursuit of real-time processing on autonomous vehicles and robotic platforms. However, due to the sparsity of Radar returns, the prevailing methods adopt multi-stage frameworks with intermediate quasi-dense depth, which are time-consuming and not robust. To address these challenges, we propose TacoDepth, an efficient and accurate Radar-Camera depth estimation model with one-stage fusion. Specifically, the graph-based Radar structure extractor and the pyramid-based Radar fusion module are designed to capture and integrate the graph structures of Radar point clouds, delivering superior model efficiency and robustness without relying on the intermediate depth results. Moreover, TacoDepth can be flexible for different inference modes, providing a better balance of speed and accuracy. Extensive experiments are conducted to demonstrate the efficacy of our method. Compared with the previous state-of-the-art approach, TacoDepth improves depth accuracy and processing speed by 12.8% and 91.8%. Our work provides a new perspective on efficient Radar-Camera depth estimation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_TacoDepth_Towards_Efficient_Radar-Camera_Depth_Estimation_with_One-stage_Fusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_TacoDepth_Towards_Efficient_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yiran and Li, Jiaqi and Hong, Chaoyi and Li, Ruibo and Sun, Liusheng and Song, Xiao and Wang, Zhe and Cao, Zhiguo and Lin, Guosheng},
    title     = {TacoDepth: Towards Efficient Radar-Camera Depth Estimation with One-stage Fusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10523-10533}
}

raw_affiliation:
[{"affiliation": "S-Lab, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "School of AIA, Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "SenseTime Research", "city": "Unknown", "country": "Unknown"}]

affiliations:
["S-Lab, Nanyang Technological University", "School of AIA, Huazhong University of Science and Technology", "SenseTime Research"]


title: Physical Plausibility-aware Trajectory Prediction via Locomotion Embodiment
authors: Hiromu Taketsugu, Takeru Oba, Takahiro Maeda, Shohei Nobuhara, Norimichi Ukita
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12324-12334
affiliations: ["Toyota Technological Institute", "Kyoto Institute of Technology"]
cities: ["Toyota", "Kyoto"]
countries: ["Japan", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Taketsugu_Physical_Plausibility-aware_Trajectory_Prediction_via_Locomotion_Embodiment_CVPR_2025_paper.html

abstract:
Humans can predict future human trajectories even from momentary observations by using human pose-related cues. However, previous Human Trajectory Prediction (HTP) methods leverage the pose cues implicitly, resulting in implausible predictions. To address this, we propose Locomotion Embodiment, a framework that explicitly evaluates the physical plausibility of the predicted trajectory by locomotion generation under the laws of physics. While the plausibility of locomotion is learned with an indifferentiable physics simulator, it is replaced by our differentiable Locomotion Value function to train an HTP network in a data-driven manner. In particular, our proposed Embodied Locomotion loss is beneficial for efficiently training a stochastic HTP network using multiple heads. Furthermore, the Locomotion Value filter is proposed to filter out implausible trajectories at inference. Experiments demonstrate that our method enhances even the state-of-the-art HTP methods across diverse datasets and problem settings. Our code is available at: https://github.com/ImIntheMiddle/EmLoco.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Taketsugu_Physical_Plausibility-aware_Trajectory_Prediction_via_Locomotion_Embodiment_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Taketsugu_Physical_Plausibility-aware_Trajectory_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Taketsugu_2025_CVPR,
    author    = {Taketsugu, Hiromu and Oba, Takeru and Maeda, Takahiro and Nobuhara, Shohei and Ukita, Norimichi},
    title     = {Physical Plausibility-aware Trajectory Prediction via Locomotion Embodiment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12324-12334}
}

raw_affiliation:
[{"affiliation": "Toyota Technological Institute", "city": "Toyota", "country": "Japan"}, {"affiliation": "Kyoto Institute of Technology", "city": "Kyoto", "country": "Japan"}]


title: CADDreamer: CAD Object Generation from Single-view Images
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21448-21457
cities: ["Dallas", "Hong Kong", "Hong Kong", "Nanjing", "Beijing", "College Station"]
countries: ["USA", "China", "China", "China", "China", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_CADDreamer_CAD_Object_Generation_from_Single-view_Images_CVPR_2025_paper.html

authors:
Yuan Li, Cheng Lin, Yuan Liu, Xiaoxiao Long, Chenxu Zhang, Ningna Wang, Xin Li, Wenping Wang, Xiaohu Guo

abstract:
The field of diffusion-based 3D generation has experienced tremendous progress in recent times. However, existing 3D generative models often produce overly dense and unstructured meshes, which are in stark contrast to the compact, structured and clear-edged CAD models created by human modelers. We introduce CADDreamer, a novel method for generating CAD objects from a single image. This method proposes a primitive-aware multi-view diffusion model, which perceives both local geometry and high-level structural semantics during the generation process. We encode primitive semantics into the color domain, and enforce the strong priors in pre-trained diffusion models to align with the well-defined primitives. As a result, we can infer multi-view normal maps and semantic maps from a single image, thereby reconstructing a mesh with primitive labels. Correspondingly, we propose a set of fitting and optimization methods to deal with the inevitable noise and distortion in generated primitives, ultimately producing a complete and seamless Boundary Representation (B-rep) of a Computer-Aided Design (CAD) model. Experimental results demonstrate that our method can effectively recover high-quality CAD objects from single-view images. Compared to existing 3D generation methods, the models produced by CADDreamer are compact in representation, clear in structure, sharp in boundaries, and watertight in topology.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_CADDreamer_CAD_Object_Generation_from_Single-view_Images_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_CADDreamer_CAD_Object_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Yuan and Lin, Cheng and Liu, Yuan and Long, Xiaoxiao and Zhang, Chenxu and Wang, Ningna and Li, Xin and Wang, Wenping and Guo, Xiaohu},
    title     = {CADDreamer: CAD Object Generation from Single-view Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21448-21457}
}

raw_affiliation:
[{"affiliation": "University of T exas at Dallas", "city": "Dallas", "country": "USA"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Hong Kong University of Science and T echnology", "city": "Hong Kong", "country": "China"}, {"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "ByteDance", "city": "Beijing", "country": "China"}, {"affiliation": "T exas A&M University", "city": "College Station", "country": "USA"}]

affiliations:
["University of T exas at Dallas", "The University of Hong Kong", "Hong Kong University of Science and T echnology", "Nanjing University", "ByteDance", "T exas A&M University"]


title: Vision-Language Model IP Protection via Prompt-based Learning
authors: Lianyu Wang, Meng Wang, Huazhu Fu, Daoqiang Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9497-9506
cities: ["Beijing", "Singapore", "Singapore"]
countries: ["China", "Singapore", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Vision-Language_Model_IP_Protection_via_Prompt-based_Learning_CVPR_2025_paper.html

abstract:
Vision-language models (VLMs) like CLIP (Contrastive Language-Image Pre-Training) have seen remarkable success in visual recognition, highlighting the increasing need to safeguard the intellectual property (IP) of well-trained models. Effective IP protection extends beyond ensuring authorized usage; it also necessitates restricting model deployment to authorized data domains, particularly when the model is fine-tuned for specific target domains. However, current IP protection methods often rely solely on the visual backbone, which may lack sufficient semantic richness. To bridge this gap, we introduce IP-CLIP, a lightweight IP protection strategy tailored to CLIP, employing a prompt-based learning approach. By leveraging the frozen visual backbone of CLIP, we extract both image style and content information, incorporating them into the learning of IP prompt. This strategy acts as a robust barrier, effectively preventing the unauthorized transfer of features from authorized domains to unauthorized ones. Additionally, we propose a style-enhancement branch that constructs feature banks for both authorized and unauthorized domains. This branch integrates self-enhanced and cross-domain features, further strengthening IP-CLIP's capability to block features from unauthorized domains. Finally, we present new three metrics designed to better balance the performance degradation of authorized and unauthorized domains. Comprehensive experiments in various scenarios demonstrate its promising potential for application in IP protection tasks for VLMs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Vision-Language_Model_IP_Protection_via_Prompt-based_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Vision-Language_Model_IP_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Lianyu and Wang, Meng and Fu, Huazhu and Zhang, Daoqiang},
    title     = {Vision-Language Model IP Protection via Prompt-based Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9497-9506}
}

raw_affiliation:
[{"affiliation": "The Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, China", "city": "Beijing", "country": "China"}, {"affiliation": "Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore", "city": "Singapore", "country": "Singapore"}]

affiliations:
["The Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, China", "Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore", "Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore"]


authors: Haoyue Bai, Yiyou Sun, Wei Cheng, Haifeng Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28821-28830
cities: ["Madison", "Berkeley", "Princeton", "Princeton"]
countries: ["USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bai_Wheres_the_Liability_in_the_Generative_Era_Recovery-based_Black-Box_Detection_CVPR_2025_paper.html

title:
Where's the Liability in the Generative Era? Recovery-based Black-Box Detection of AI-Generated Content

abstract:
The recent proliferation of photorealistic images created by generative models has sparked both excitement and concern, as these images are increasingly indistinguishable from real ones to the human eye. While offering new creative and commercial possibilities, the potential for misuse, such as in misinformation and fraud, highlights the need for effective detection methods. Current detection approaches often rely on access to model weights or require extensive collections of real image datasets, limiting their scalability and practical application in real-world scenarios. In this work, we introduce a novel black-box detection framework that requires only API access, sidestepping the need for model weights or large auxiliary datasets. Our approach leverages a corrupt-and-recover strategy: by masking part of an image and assessing the model's ability to reconstruct it, we measure the likelihood that the image was generated by the model itself. For black-box models that do not support masked-image inputs, we incorporate a cost-efficient surrogate model trained to align with the target model's distribution, enhancing detection capability. Our framework demonstrates strong performance, outperforming baseline methods by 4.31% in mean average precision across eight diffusion model variant datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bai_Wheres_the_Liability_in_the_Generative_Era_Recovery-based_Black-Box_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bai_Wheres_the_Liability_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bai_2025_CVPR,
    author    = {Bai, Haoyue and Sun, Yiyou and Cheng, Wei and Chen, Haifeng},
    title     = {Where's the Liability in the Generative Era? Recovery-based Black-Box Detection of AI-Generated Content},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28821-28830}
}

raw_affiliation:
[{"affiliation": "University of Wisconsin-Madison", "city": "Madison", "country": "USA"}, {"affiliation": "University of California, Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "NEC Laboratories America", "city": "Princeton", "country": "USA"}, {"affiliation": "NEC Laboratories America", "city": "Princeton", "country": "USA"}]

affiliations:
["University of Wisconsin-Madison", "University of California, Berkeley", "NEC Laboratories America", "NEC Laboratories America"]


title: Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5870-5880
affiliations: ["HKUST(GZ)", "HKUST", "Guangzhou Quwan Network Technology"]
cities: ["Guangzhou", "Hong Kong", "Guangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_Kiss3DGen_Repurposing_Image_Diffusion_Models_for_3D_Asset_Generation_CVPR_2025_paper.html

authors:
Jiantao Lin, Xin Yang, Meixi Chen, Yingjie Xu, Dongyu Yan, Leyi Wu, Xinli Xu, Lie Xu, Shunsi Zhang, Ying-Cong Chen

abstract:
Diffusion models have achieved great success in generating 2D images. However, the quality and generalizability of 3D content generation remain limited. State-of-the-art methods often require large-scale 3D assets for training, which are challenging to collect. In this work, we introduce Kiss3DGen (Keep It Simple and Straightforward in 3D Generation), an efficient framework for generating, editing, and enhancing 3D objects by repurposing a well-trained 2D image diffusion model for 3D generation. Specifically, we fine-tune a diffusion model to generate "3D Bundle Image", a tiled representation composed of multi-view images and their corresponding normal maps. The normal maps are then used to reconstruct a 3D mesh, and the multi-view images provide texture mapping, resulting in a complete 3D model. This simple method effectively transforms the 3D generation problem into a 2D image generation task, maximizing the utilization of knowledge in pretrained diffusion models. Furthermore, we demonstrate that our Kiss3DGen model is compatible with various diffusion model techniques, enabling advanced features such as 3D editing, mesh and texture enhancement, etc. Through extensive experiments, we demonstrate the effectiveness of our approach, showcasing its ability to produce high-quality 3D models efficiently.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_Kiss3DGen_Repurposing_Image_Diffusion_Models_for_3D_Asset_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_Kiss3DGen_Repurposing_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Jiantao and Yang, Xin and Chen, Meixi and Xu, Yingjie and Yan, Dongyu and Wu, Leyi and Xu, Xinli and Xu, Lie and Zhang, Shunsi and Chen, Ying-Cong},
    title     = {Kiss3DGen: Repurposing Image Diffusion Models for 3D Asset Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5870-5880}
}

raw_affiliation:
[{"affiliation": "HKUST(GZ)", "city": "Guangzhou", "country": "China"}, {"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}, {"affiliation": "Guangzhou Quwan Network Technology", "city": "Guangzhou", "country": "China"}]


title: DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations
authors: Krishna Sri Ipsit Mantri, Carola-Bibiane Schönlieb, Bruno Ribeiro, Chaim Baskin, Moshe Eliasof
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25218-25229
cities: ["West Lafayette", "Cambridge", "West Lafayette", "Beer Sheva", "Cambridge"]
countries: ["USA", "UK", "USA", "Israel", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mantri_DiTASK_Multi-Task_Fine-Tuning_with_Diffeomorphic_Transformations_CVPR_2025_paper.html

abstract:
Pre-trained Vision Transformers now serve as powerful tools for computer vision. Yet, efficiently adapting them for multiple tasks remains a challenge that arises from the need to modify the rich hidden representations encoded by the learned weight matrices, without inducing interference between tasks. Current parameter-efficient methods like LoRA, which apply low-rank updates, force tasks to compete within constrained subspaces, ultimately degrading performance. We introduce DiTASK, a novel Diffeomorphic Multi-Task Fine-Tuning approach that maintains pre-trained representations by preserving weight matrix singular vectors, while enabling task-specific adaptations through neural diffeomorphic transformations of the singular values. By following this approach, DiTASK enables both shared and task-specific feature modulations with minimal added parameters.  Our theoretical analysis shows that DiTASK achieves full-rank updates during optimization, preserving the geometric structure of pre-trained features, and establishing a new paradigm for efficient multi-task learning (MTL). Our experiments on PASCAL MTL and NYUD show that DiTASK achieves state-of-the-art performance across four dense prediction tasks, using 75% fewer parameters than existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mantri_DiTASK_Multi-Task_Fine-Tuning_with_Diffeomorphic_Transformations_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mantri_DiTASK_Multi-Task_Fine-Tuning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mantri_2025_CVPR,
    author    = {Mantri, Krishna Sri Ipsit and Sch\"onlieb, Carola-Bibiane and Ribeiro, Bruno and Baskin, Chaim and Eliasof, Moshe},
    title     = {DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25218-25229}
}

raw_affiliation:
[{"affiliation": "Purdue University", "city": "West Lafayette", "country": "USA"}, {"affiliation": "University of Cambridge", "city": "Cambridge", "country": "UK"}, {"affiliation": "Purdue University", "city": "West Lafayette", "country": "USA"}, {"affiliation": "Ben-Gurion University of the Negev", "city": "Beer Sheva", "country": "Israel"}, {"affiliation": "University of Cambridge", "city": "Cambridge", "country": "UK"}]

affiliations:
["Purdue University", "University of Cambridge", "Purdue University", "Ben-Gurion University of the Negev", "University of Cambridge"]


title: OW-OVD: Unified Open World and Open Vocabulary Object Detection
authors: Xing Xi, Yangyang Huang, Ronghua Luo, Yu Qiu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25454-25464
affiliations: ["School of Computer Science & Engineering, South China University of Technology"]
cities: ["Guangzhou"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xi_OW-OVD_Unified_Open_World_and_Open_Vocabulary_Object_Detection_CVPR_2025_paper.html

abstract:
Open world perception expands traditional closed-set frameworks, which assume a predefined set of known categories, to encompass dynamic real-world environments. Open World Object Detection (OWOD) and Open Vocabulary Object Detection (OVD) are two main research directions, each addressing unique challenges in dynamic environments. However, existing studies often focus on only one of these tasks, leaving the combined challenges of OWOD and OVD largely underexplored. In this paper, we propose a novel detector, OW-OVD, which inherits the zero-shot generalization capability of OVD detectors while incorporating the ability to actively detect unknown objects and progressively optimize performance through incremental learning, as seen in OWOD detectors. To achieve this, we start with a standard OVD detector and adapt it for OWOD tasks. For attribute selection, we propose the Visual Similarity Attribute Selection (VSAS) method, which identifies the most generalizable attributes by computing similarity distributions across annotated and unannotated regions. Additionally, to ensure the diversity of attributes, we incorporate a similarity constraint in the iterative process. Finally, to preserve the standard inference process of OVD, we propose the Hybrid Attribute-Uncertainty Fusion (HAUF) method. This method combines attribute similarity with known class uncertainty to infer the likelihood of an object belonging to an unknown class. We validated the effectiveness of OW-OVD through evaluations on two OWOD benchmarks, M-OWODB and S-OWODB. The results demonstrate that OW-OVD outperforms existing state-of-the-art models, achieving a +15.3 improvement in unknown object recall (U-Recall) and a +15.5 increase in unknown class average precision (U-mAP). Our code is available at: https://github.com/xxyzll/OW_OVD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xi_OW-OVD_Unified_Open_World_and_Open_Vocabulary_Object_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xi_OW-OVD_Unified_Open_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xi_2025_CVPR,
    author    = {Xi, Xing and Huang, Yangyang and Luo, Ronghua and Qiu, Yu},
    title     = {OW-OVD: Unified Open World and Open Vocabulary Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25454-25464}
}

raw_affiliation:
[{"affiliation": "School of Computer Science & Engineering, South China University of Technology", "city": "Guangzhou", "country": "China"}]


title: Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing
authors: Bingliang Zhang, Wenda Chu, Julius Berner, Chenlin Meng, Anima Anandkumar, Yang Song
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20895-20905
affiliations: ["California Institute of Technology", "NVIDIA", "Stanford University", "OpenAI"]
cities: ["Pasadena", "Santa Clara", "Stanford", "San Francisco"]
countries: ["USA", "USA", "USA", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Improving_Diffusion_Inverse_Problem_Solving_with_Decoupled_Noise_Annealing_CVPR_2025_paper.html

abstract:
Diffusion models have recently achieved success in solving Bayesian inverse problems with learned data priors. Current methods build on top of the diffusion sampling process, where each denoising step makes small modifications to samples from the previous step. However, this process struggles to correct errors from earlier sampling steps, leading to worse performance in complicated nonlinear inverse problems, such as phase retrieval. To address this challenge, we propose a new method called Decoupled Annealing Posterior Sampling (DAPS) that relies on a novel noise annealing process. Specifically, we decouple consecutive steps in a diffusion sampling trajectory, allowing them to vary considerably from one another while ensuring their time-marginals anneal to the true posterior as we reduce noise levels. This approach enables the exploration of a larger solution space, improving the success rate for accurate reconstructions. We demonstrate that DAPS significantly improves sample quality and stability across multiple image restoration tasks, particularly in complicated nonlinear inverse problems.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Improving_Diffusion_Inverse_Problem_Solving_with_Decoupled_Noise_Annealing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Improving_Diffusion_Inverse_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Bingliang and Chu, Wenda and Berner, Julius and Meng, Chenlin and Anandkumar, Anima and Song, Yang},
    title     = {Improving Diffusion Inverse Problem Solving with Decoupled Noise Annealing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20895-20905}
}

raw_affiliation:
[{"affiliation": "California Institute of Technology", "city": "Pasadena", "country": "USA"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "OpenAI", "city": "San Francisco", "country": "USA"}]


title: AvatarArtist: Open-Domain 4D Avatarization
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10758-10769
affiliations: ["HKUST", "Ant Group", "City University of Hong Kong"]
cities: ["Hong Kong", "Hangzhou", "Hong Kong"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_AvatarArtist_Open-Domain_4D_Avatarization_CVPR_2025_paper.html

authors:
Hongyu Liu, Xuan Wang, Ziyu Wan, Yue Ma, Jingye Chen, Yanbo Fan, Yujun Shen, Yibing Song, Qifeng Chen

abstract:
This work focuses on open-domain 4D avatarization, with the purpose of creating a 4D avatar from a portrait image in an arbitrary style. We select parametric triplanes as the intermediate 4D representation, and propose a practical training paradigm that takes advantage of both generative adversarial networks (GANs) and diffusion models. Our design stems from the observation that 4D GANs excel at bridging images and triplanes without supervision yet usually face challenges in handling diverse data distributions. A robust 2D diffusion prior emerges as the solution, assisting the GAN in transferring its expertise across various domains. The synergy between these experts permits the construction of a multi-domain image-triplane dataset, which drives the development of a general 4D avatar creator. Extensive experiments suggest that our model, termed AvatarArtist, is capable of producing high-quality 4D avatars with strong robustness to various source image domains. The code, the data, and the models will be made publicly available to facilitate future studies.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_AvatarArtist_Open-Domain_4D_Avatarization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_AvatarArtist_Open-Domain_4D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Hongyu and Wang, Xuan and Wan, Ziyu and Ma, Yue and Chen, Jingye and Fan, Yanbo and Shen, Yujun and Song, Yibing and Chen, Qifeng},
    title     = {AvatarArtist: Open-Domain 4D Avatarization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10758-10769}
}

raw_affiliation:
[{"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}, {"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "China"}]


title: DesignDiffusion: High-Quality Text-to-Design Image Generation with Diffusion Models
authors: Zhendong Wang, Jianmin Bao, Shuyang Gu, Dong Chen, Wengang Zhou, Houqiang Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20906-20915
affiliations: ["University of Science and Technology of China", "Microsoft Research Asia"]
cities: ["Hefei", "Beijing"]
countries: ["China", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_DesignDiffusion_High-Quality_Text-to-Design_Image_Generation_with_Diffusion_Models_CVPR_2025_paper.html

abstract:
In this paper, we present DesignDiffusion, a simple yet effective framework for the novel task of synthesizing design images from textual descriptions. A primary challenge lies in generating accurate and style-consistent textual and visual content. Existing works in a related task of visual text generation often focus on generating text within given specific regions, which limits the creativity of generation models, resulting in style or color inconsistencies between textual and visual elements if applied to design image generation. To address this issue, we propose an end-to-end, one-stage diffusion-based framework that avoids intricate components like position and layout modeling. Specifically, the proposed framework directly synthesizes textual and visual design elements from user prompts. It utilizes a distinctive character embedding derived from the visual text to enhance the input prompt, along with a character localization loss for enhanced supervision during text generation. Furthermore, we employ a self-play Direct Preference Optimization fine-tuning strategy to improve the quality and accuracy of the synthesized visual text. Extensive experiments demonstrate that DesignDiffusion achieves state-of-the-art performance in design image generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_DesignDiffusion_High-Quality_Text-to-Design_Image_Generation_with_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_DesignDiffusion_High-Quality_Text-to-Design_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zhendong and Bao, Jianmin and Gu, Shuyang and Chen, Dong and Zhou, Wengang and Li, Houqiang},
    title     = {DesignDiffusion: High-Quality Text-to-Design Image Generation with Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20906-20915}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Microsoft Research Asia", "city": "Beijing", "country": "China"}]


authors: Chen Liao, Yan Shen, Dan Li, Zhongli Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18000-18010
raw_affiliation: [{"affiliation": "Beijing Jiaotong University", "city": "Beijing", "country": "China"}]
affiliations: ["Beijing Jiaotong University"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liao_Using_Powerful_Prior_Knowledge_of_Diffusion_Model_in_Deep_Unfolding_CVPR_2025_paper.html

title:
Using Powerful Prior Knowledge of Diffusion Model in Deep Unfolding Networks for Image Compressive Sensing

abstract:
Recently, Deep Unfolding Networks (DUNs) have achieved impressive reconstruction quality in the field of image Compressive Sensing (CS) by unfolding iterative optimization algorithms into neural networks. The reconstruction quality of DUNs depends on the learned prior knowledge, so introducing stronger prior knowledge can further improve reconstruction quality. On the other hand, pre-trained diffusion models contain powerful prior knowledge and have a solid theoretical foundation and strong scalability, but it requires a large number of iterative steps to achieve reconstruction. In this paper, we propose to use the powerful prior knowledge of pre-trained diffusion model in DUNs to achieve high-quality reconstruction with less steps for image CS. Specifically, we first design an iterative optimization algorithm named Diffusion Message Passing (DMP), which embeds a pre-trained diffusion model into each iteration process of DMP. Then, we deeply unfold the DMP algorithm into a neural network named DMP-DUN. The proposed DMP-DUN can use lightweight neural networks to achieve mapping from measurement data to the intermediate steps of the reverse diffusion process and directly approximate the divergence of the diffusion model, thereby further improving reconstruction efficiency. Extensive experiments show that our proposed DMP-DUN achieves state-of-the-art performance and requires at least only 2 steps to reconstruct the image. Codes are available at https://github.com/FengodChen/DMP-DUN-CVPR2025.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liao_Using_Powerful_Prior_Knowledge_of_Diffusion_Model_in_Deep_Unfolding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liao_Using_Powerful_Prior_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liao_2025_CVPR,
    author    = {Liao, Chen and Shen, Yan and Li, Dan and Wang, Zhongli},
    title     = {Using Powerful Prior Knowledge of Diffusion Model in Deep Unfolding Networks for Image Compressive Sensing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18000-18010}
}


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8428-8437
affiliations: ["Shenzhen University", "Tsinghua University", "Kuaishou Technology"]
cities: ["Shenzhen", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Koala-36M_A_Large-scale_Video_Dataset_Improving_Consistency_between_Fine-grained_Conditions_CVPR_2025_paper.html

title:
Koala-36M: A Large-scale Video Dataset Improving Consistency between Fine-grained Conditions and Video Content

authors:
Qiuheng Wang, Yukai Shi, Jiarong Ou, Rui Chen, Ke Lin, Jiahao Wang, Boyuan Jiang, Haotian Yang, Mingwu Zheng, Xin Tao, Fei Yang, Pengfei Wan, Di Zhang

abstract:
With the continuous progress of visual generation technologies, the scale of video datasets has grown exponentially. The quality of these datasets plays a pivotal role in the performance of video generation models. We assert that temporal splitting, detailed captions, and video quality filtering are  three crucial determinants of dataset quality. However, existing datasets exhibit various limitations in these areas. To address these challenges, we introduce Koala-36M, a large-scale, high-quality video dataset featuring accurate temporal splitting, detailed captions, and superior video quality. The essence  of our approach lies in improving the consistency between fine-grained conditions and video content. Specifically, we employ a linear classifier on probability distributions to enhance the accuracy of transition detection, ensuring better temporal consistency. We then provide structured captions for the splitted videos, with an average length of 200 words, to improve text-video alignment. Additionally, we develop a Video Training Suitability Score (VTSS) that integrates multiple sub-metrics, allowing us to filter high-quality videos from the original corpus. Finally, we incorporate several metrics into the training process of the generation model, further refining the fine-grained conditions. Our experiments demonstrate the effectiveness of our data processing pipeline and the quality of the proposed Koala-36M dataset. Our dataset and code have been released at https://koala36m.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Koala-36M_A_Large-scale_Video_Dataset_Improving_Consistency_between_Fine-grained_Conditions_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Koala-36M_A_Large-scale_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Qiuheng and Shi, Yukai and Ou, Jiarong and Chen, Rui and Lin, Ke and Wang, Jiahao and Jiang, Boyuan and Yang, Haotian and Zheng, Mingwu and Tao, Xin and Yang, Fei and Wan, Pengfei and Zhang, Di},
    title     = {Koala-36M: A Large-scale Video Dataset Improving Consistency between Fine-grained Conditions and Video Content},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8428-8437}
}

raw_affiliation:
[{"affiliation": "Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Kuaishou Technology", "city": "Beijing", "country": "China"}]


title: VASparse: Towards Efficient Visual Hallucination Mitigation via Visual-Aware Token Sparsification
authors: Xianwei Zhuang, Zhihong Zhu, Yuxin Xie, Liming Liang, Yuexian Zou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4189-4199
cities: ["Shenzhen", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhuang_VASparse_Towards_Efficient_Visual_Hallucination_Mitigation_via_Visual-Aware_Token_Sparsification_CVPR_2025_paper.html

abstract:
Large Vision-Language Models (LVLMs) may produce outputs that are unfaithful to reality, also known as visual hallucinations (VH), which significantly impedes their real-world usage. To alleviate VH, various decoding strategies have been proposed to enhance visual information. However, many of these methods may require secondary decoding and rollback, which significantly reduces inference speed. In this work, we propose an efficient plug-and-play decoding algorithm via Visual-Aware Sparsification (VASparse) from the perspective of token sparsity for mitigating VH. VASparse is inspired by empirical observations: (1) the sparse activation of attention in LVLMs, and (2) visual-agnostic tokens sparsification exacerbates VH. Based on these insights, we propose a novel token sparsification strategy that balances efficiency and trustworthiness.  Specifically, VASparse implements a visual-aware token selection strategy during decoding to reduce redundant tokens while preserving visual context effectively.  Additionally, we innovatively introduce a sparse-based visual contrastive decoding method to recalibrate the distribution of hallucinated outputs without the time overhead associated with secondary decoding. Subsequently, VASparse recalibrates attention scores to penalize attention sinking of LVLMs towards text tokens. Extensive experiments across four popular benchmarks confirm the effectiveness of VASparse in mitigating VH across different LVLM families without requiring additional training or post-processing. Impressively, VASparse achieves state-of-the-art performance for mitigating VH while maintaining competitive decoding speed. Code is available at https://github.com/mengchuang123/VASparse-github.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhuang_VASparse_Towards_Efficient_Visual_Hallucination_Mitigation_via_Visual-Aware_Token_Sparsification_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhuang_VASparse_Towards_Efficient_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhuang_2025_CVPR,
    author    = {Zhuang, Xianwei and Zhu, Zhihong and Xie, Yuxin and Liang, Liming and Zou, Yuexian},
    title     = {VASparse: Towards Efficient Visual Hallucination Mitigation via Visual-Aware Token Sparsification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4189-4199}
}

raw_affiliation:
[{"affiliation": "Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School, Peking University", "city": "Shenzhen", "country": "China"}, {"affiliation": "School of Electronic and Computer Engineering, Peking University", "city": "Beijing", "country": "China"}]

affiliations:
["Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School, Peking University", "School of Electronic and Computer Engineering, Peking University"]


authors: Kevin Miller, Aditya Gangrade, Samarth Mishra, Kate Saenko, Venkatesh Saligrama
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4313-4321
cities: ["Boston", "Boston", "Boston", "Boston", "Boston"]
countries: ["USA", "USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Miller_SPARC_Score_Prompting_and_Adaptive_Fusion_for_Zero-Shot_Multi-Label_Recognition_CVPR_2025_paper.html

title:
SPARC: Score Prompting and Adaptive Fusion for Zero-Shot Multi-Label Recognition in Vision-Language Models

abstract:
Zero-shot multi-label recognition (MLR) with Vision-Language Models (VLMs) faces significant challenges without training data, model tuning, or architectural modifications. Existing approaches require prompt tuning or architectural adaptations, limiting zero-shot applicability. Our work proposes a novel solution treating VLMs as black boxes, leveraging scores without training data or ground truth. We make two contributions. First, we find that VLM scores suffer from image- and prompt-specific biases, and that simple standardization is surprisingly effective at removing these and boosting MLR performance. And second, we introduce compound prompts grounded in realistic object combinations. Our analysis reveals "AND"/"OR" signal ambiguities that cause maximum compound scores to be surprisingly suboptimal compared to second-highest scores. We introduce an adaptive fusion method to address this issue. Our method enhances other zero-shot approaches, consistently improving their results. Experiments show superior mean Average Precision (mAP) compared to methods requiring training data, achieved through refined object ranking for robust zero-shot MLR. Code can be found at https://github.com/kjmillerCURIS/SPARC.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Miller_SPARC_Score_Prompting_and_Adaptive_Fusion_for_Zero-Shot_Multi-Label_Recognition_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Miller_SPARC_Score_Prompting_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Miller_2025_CVPR,
    author    = {Miller, Kevin and Gangrade, Aditya and Mishra, Samarth and Saenko, Kate and Saligrama, Venkatesh},
    title     = {SPARC: Score Prompting and Adaptive Fusion for Zero-Shot Multi-Label Recognition in Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4313-4321}
}

raw_affiliation:
[{"affiliation": "Boston University", "city": "Boston", "country": "USA"}, {"affiliation": "Boston University", "city": "Boston", "country": "USA"}, {"affiliation": "Boston University", "city": "Boston", "country": "USA"}, {"affiliation": "Boston University and Meta AI (FAIR)", "city": "Boston", "country": "USA"}, {"affiliation": "Boston University", "city": "Boston", "country": "USA"}]

affiliations:
["Boston University", "Boston University", "Boston University", "Boston University and Meta AI (FAIR)", "Boston University"]


title: UniGoal: Towards Universal Zero-shot Goal-oriented Navigation
authors: Hang Yin, Xiuwei Xu, Linqing Zhao, Ziwei Wang, Jie Zhou, Jiwen Lu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19057-19066
affiliations: ["Tsinghua University", "Nanyang Technological University"]
cities: ["Beijing", "Singapore"]
countries: ["China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yin_UniGoal_Towards_Universal_Zero-shot_Goal-oriented_Navigation_CVPR_2025_paper.html

abstract:
In this paper, we propose a general framework for universal zero-shot goal-oriented navigation. Existing zero-shot methods build inference framework upon large language models (LLM) for specific tasks, which differs a lot in overall pipeline and fails to generalize across different types of goal. Towards the aim of universal zero-shot navigation, we propose a uniform graph representation to unify different goals, including object category, instance image and text description. We also convert the observation of agent into an online maintained scene graph. With this consistent scene and goal representation, we preserve most structural information compared with pure text and are able to leverage LLM for explicit graph-based reasoning.Specifically, we conduct graph matching between the scene graph and goal graph at each time instant and propose different strategies to generate long-term goal of exploration according to different matching states. The agent first iteratively searches subgraph of goal when zero-matched. With partial matching, the agent then utilizes coordinate projection and anchor pair alignment to infer the goal location. Finally scene graph correction and goal verification are applied for perfect matching. We also present a blacklist mechanism to enable robust switch between stages.Extensive experiments on several benchmarks show that our UniGoal achieves state-of-the-art zero-shot performance on three studied navigation tasks with a single model, even outperforming task-specific zero-shot methods and supervised universal methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yin_UniGoal_Towards_Universal_Zero-shot_Goal-oriented_Navigation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yin_UniGoal_Towards_Universal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yin_2025_CVPR,
    author    = {Yin, Hang and Xu, Xiuwei and Zhao, Linqing and Wang, Ziwei and Zhou, Jie and Lu, Jiwen},
    title     = {UniGoal: Towards Universal Zero-shot Goal-oriented Navigation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19057-19066}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]


title: Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation
authors: Kunpeng Qiu, Zhiqiang Gao, Zhiying Zhou, Mingjie Sun, Yongxin Guo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15672-15681
cities: ["Singapore", "Suzhou", "Wenzhou", "Suzhou", "Hong Kong"]
countries: ["Singapore", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qiu_Noise-Consistent_Siamese-Diffusion_for_Medical_Image_Synthesis_and_Segmentation_CVPR_2025_paper.html

abstract:
Deep learning has revolutionized medical image segmentation, yet its full potential remains constrained by the paucity of annotated datasets. While diffusion models have emerged as a promising approach for generating synthetic image-mask pairs to augment these datasets, they paradoxically suffer from the same data scarcity challenges they aim to mitigate. Traditional mask-only models frequently yield low-fidelity images due to their inability to adequately capture morphological intricacies, which can critically compromise the robustness and reliability of segmentation models. To alleviate this limitation, we introduce Siamese-Diffusion, a novel dual-component model comprising Mask-Diffusion and Image-Diffusion. During training, a Noise Consistency Loss is introduced between these components to enhance the morphological fidelity of Mask-Diffusion in the parameter space. During sampling, only Mask-Diffusion is used, ensuring diversity and scalability. Comprehensive experiments demonstrate the superiority of our method. Siamese-Diffusion boosts SANet's mDice and mIoU by 3.6% and 4.4% on the Polyps, while UNet improves by 1.52% and 1.64% on the ISIC2018.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qiu_Noise-Consistent_Siamese-Diffusion_for_Medical_Image_Synthesis_and_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qiu_Noise-Consistent_Siamese-Diffusion_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qiu_2025_CVPR,
    author    = {Qiu, Kunpeng and Gao, Zhiqiang and Zhou, Zhiying and Sun, Mingjie and Guo, Yongxin},
    title     = {Noise-Consistent Siamese-Diffusion for Medical Image Synthesis and Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15672-15681}
}

raw_affiliation:
[{"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "National University of Singapore Suzhou Research Institute", "city": "Suzhou", "country": "China"}, {"affiliation": "Wenzhou-Kean University", "city": "Wenzhou", "country": "China"}, {"affiliation": "Soochow University", "city": "Suzhou", "country": "China"}, {"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["National University of Singapore", "National University of Singapore Suzhou Research Institute", "Wenzhou-Kean University", "Soochow University", "City University of Hong Kong"]


title: DefectFill: Realistic Defect Generation with Inpainting Diffusion Model for Visual Inspection
authors: Jaewoo Song, Daemin Park, Kanghyun Baek, Sangyub Lee, Jooyoung Choi, Eunji Kim, Sungroh Yoon
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18718-18727
cities: ["Seoul", "Seoul", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Song_DefectFill_Realistic_Defect_Generation_with_Inpainting_Diffusion_Model_for_Visual_CVPR_2025_paper.html

abstract:
Developing effective visual inspection models remains challenging due to the scarcity of defect data. While image generation models have been used to synthesize defect images, producing highly realistic defects remains difficult. We propose DefectFill, a novel method for realistic defect generation that requires only a few reference defect images. It leverages a fine-tuned inpainting diffusion model, optimized with our custom loss functions incorporating defect, object, and attention terms. It enables precise capture of detailed, localized defect features and their seamless integration into defect-free objects. Additionally, our Low-Fidelity Selection method further enhances the defect sample quality. Experiments show that DefectFill generates high-quality defect images, enabling visual inspection models to achieve state-of-the-art performance on the MVTec AD dataset.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Song_DefectFill_Realistic_Defect_Generation_with_Inpainting_Diffusion_Model_for_Visual_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Song_DefectFill_Realistic_Defect_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Song_2025_CVPR,
    author    = {Song, Jaewoo and Park, Daemin and Baek, Kanghyun and Lee, Sangyub and Choi, Jooyoung and Kim, Eunji and Yoon, Sungroh},
    title     = {DefectFill: Realistic Defect Generation with Inpainting Diffusion Model for Visual Inspection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18718-18727}
}

raw_affiliation:
[{"affiliation": "Department of Electrical and Computer Engineering, Seoul National University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Global Technology Research, Samsung Electronics", "city": "Seoul", "country": "South Korea"}, {"affiliation": "IPAI, 4AIIS, ASRI, INMC, ISRC, Seoul National University", "city": "Seoul", "country": "South Korea"}]

affiliations:
["Department of Electrical and Computer Engineering, Seoul National University", "Global Technology Research, Samsung Electronics", "IPAI, 4AIIS, ASRI, INMC, ISRC, Seoul National University"]


title: Less is More: Efficient Image Vectorization with Adaptive Parameterization
authors: Kaibo Zhao, Liang Bao, Yufei Li, Xu Su, Ke Zhang, Xiaotian Qiao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18166-18175
cities: ["Xi'an", "Guangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Less_is_More_Efficient_Image_Vectorization_with_Adaptive_Parameterization_CVPR_2025_paper.html

abstract:
Image vectorization aims to convert raster images to vector ones, allowing for easy scaling and editing.Existing works mainly rely on preset parameters (i.e., a fixed number of paths and control points), ignoring the complexity of the image and posing significant challenges to practical applications.We demonstrate that such an assumption is often incorrect, as the preset paths or control points may be neither essential nor enough to achieve accurate and editable vectorization results.Based on this key insight, in this paper, we propose AdaVec, an efficient image vectorization method with adaptive parametrization, where the paths and control points can be adjusted dynamically based on the complexity of the input raster image.In particular, we first decompose the input raster image into a set of pure-colored layers that are aligned with human perception.For each layer with varying shape complexity, we propose a novel allocation mechanism to adaptively adjust the control point distribution.We further adopt a differentiable rendering process to compose and optimize the shape and color parameters of each layer iteratively.Extensive experiments demonstrate that AdaVec outperforms the baselines qualitatively and quantitatively, in terms of computational efficiency,  vectorization accuracy, and editing flexibility.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Less_is_More_Efficient_Image_Vectorization_with_Adaptive_Parameterization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_Less_is_More_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Kaibo and Bao, Liang and Li, Yufei and Su, Xu and Zhang, Ke and Qiao, Xiaotian},
    title     = {Less is More: Efficient Image Vectorization with Adaptive Parameterization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18166-18175}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology, Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "Guangzhou Institute of Technology, Xidian University", "city": "Guangzhou", "country": "China"}]

affiliations:
["School of Computer Science and Technology, Xidian University", "Guangzhou Institute of Technology, Xidian University"]


authors: Gongxi Zhu, Donghao Li, Hanlin Gu, Yuan Yao, Lixin Fan, Yuxing Han
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20643-20653
affiliations: ["Tsinghua University", "The Hong Kong University of Science and Technology", "Webank"]
cities: ["Beijing", "Hong Kong", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_FedMIA_An_Effective_Membership_Inference_Attack_Exploiting_All_for_One_CVPR_2025_paper.html

title:
FedMIA: An Effective Membership Inference Attack Exploiting "All for One" Principle in Federated Learning

abstract:
Federated Learning (FL) is a promising approach for training machine learning models on decentralized data while preserving privacy. However, privacy risks, particularly Membership Inference Attacks (MIAs), which aim to determine whether a specific data point belongs to a target client's training set, remain a significant concern. Existing methods for implementing MIAs in FL primarily analyze updates from the target client, focusing on metrics such as loss, gradient norm, and gradient difference. However, these methods fail to leverage updates from non-target clients, potentially underutilizing available information.In this paper, we first formulate a one-tailed likelihood-ratio hypothesis test based on the likelihood of updates from non-target clients. Building upon this formulation, we introduce a three-stage Membership Inference Attack (MIA) method, called FedMIA, which follows the "all for one"--leveraging updates from all clients across multiple communication rounds to enhance MIA effectiveness. Both theoretical analysis and extensive experimental results demonstrate that FedMIA outperforms existing MIAs in both classification and generative tasks. Additionally, it can be integrated as an extension to existing methods and is robust against various defense strategies, Non-IID data, and different federated structures. Our code is available in https://github.com/Liar-Mask/FedMIA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_FedMIA_An_Effective_Membership_Inference_Attack_Exploiting_All_for_One_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_FedMIA_An_Effective_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Gongxi and Li, Donghao and Gu, Hanlin and Yao, Yuan and Fan, Lixin and Han, Yuxing},
    title     = {FedMIA: An Effective Membership Inference Attack Exploiting ''All for One'' Principle in Federated Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20643-20653}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "Webank", "city": "Shenzhen", "country": "China"}]


title: Erase Diffusion: Empowering Object Removal Through Calibrating Diffusion Pathways
authors: Yi Liu, Hao Zhou, Benlei Cui, Wenxiang Shang, Ran Lin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2418-2427
raw_affiliation: [{"affiliation": "Alibaba Group", "city": "Hangzhou", "country": "China"}]
affiliations: ["Alibaba Group"]
cities: ["Hangzhou"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Erase_Diffusion_Empowering_Object_Removal_Through_Calibrating_Diffusion_Pathways_CVPR_2025_paper.html

abstract:
Erase inpainting, or object removal, aims to precisely remove target objects within masked regions while preserving the overall consistency of the surrounding content. Despite diffusion-based methods have made significant strides in the field of image inpainting, challenges remain regarding the emergence of unexpected objects or artifacts. We assert that the inexact diffusion pathways established by existing standard optimization paradigms constrain the efficacy of object removal. To tackle these challenges, we propose a novel Erase Diffusion, termed EraDiff, aimed at unleashing the potential power of standard diffusion in the context of object removal. In contrast to standard diffusion, the  EraDiff adapts both the optimization paradigm and the network to improve the coherence and elimination of the erasure results.We first introduce a Chain-Rectifying Optimization (CRO) paradigm, a sophisticated diffusion process specifically designed to align with the objectives of erasure. This paradigm establishes innovative diffusion transition pathways that simulate the gradual elimination of objects during optimization, allowing the model to accurately capture the intent of object removal. Furthermore, to mitigate deviations caused by artifacts during the sampling pathways, we develop a simple yet effective Self-Rectifying Attention (SRA) mechanism. The SRA calibrates the sampling pathways by altering self-attention activation, allowing the model to effectively bypass artifacts while further enhancing the coherence of the generated content. With this design, our proposed EraDiff achieves state-of-the-art performance on the OpenImages V5 dataset and demonstrates significant superiority in real-world scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Erase_Diffusion_Empowering_Object_Removal_Through_Calibrating_Diffusion_Pathways_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Erase_Diffusion_Empowering_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yi and Zhou, Hao and Cui, Benlei and Shang, Wenxiang and Lin, Ran},
    title     = {Erase Diffusion: Empowering Object Removal Through Calibrating Diffusion Pathways},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2418-2427}
}


title: Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4375-4385
cities: ["Columbus", "Tsukuba", "Blacksburg", "Princeton", "Troy"]
countries: ["USA", "Japan", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chowdhury_Prompt-CAM_Making_Vision_Transformers_Interpretable_for_Fine-Grained_Analysis_CVPR_2025_paper.html

authors:
Arpita Chowdhury, Dipanjyoti Paul, Zheda Mai, Jianyang Gu, Ziheng Zhang, Kazi Sajeed Mehrab, Elizabeth G. Campolongo, Daniel Rubenstein, Charles V. Stewart, Anuj Karpatne, Tanya Berger-Wolf, Yu Su, Wei-Lun Chao

abstract:
We present a simple approach to make pre-trained Vision Transformers (ViTs) interpretable for fine-grained analysis, aiming to identify and localize the traits that distinguish visually similar categories, such as bird species. Pre-trained ViTs, such as DINO, have demonstrated remarkable capabilities in extracting localized, discriminative features. However, saliency maps like Grad-CAM often fail to identify these traits, producing blurred, coarse heatmaps that highlight entire objects instead. We propose a novel approach, Prompt Class Attention Map (Prompt-CAM), to address this limitation.  Prompt-CAM learns class-specific prompts for a pre-trained ViT and uses the corresponding outputs for classification. To correctly classify an image, the true-class prompt must attend to unique image patches not present in other classes' images  (i.e., traits). As a result, the true class's multi-head attention maps reveal traits and their locations. Implementation-wise, Prompt-CAM is almost a "free lunch," requiring only a modification to the prediction head of Visual Prompt Tuning (VPT). This makes Prompt-CAM easy to train and apply, in stark contrast to other interpretable methods that require designing specific models and training processes. Extensive empirical studies on a dozen datasets from various domains (e.g., birds, fishes, insects, fungi, flowers, food, and cars) validate the superior interpretation capability of Prompt-CAM. The source code and demo are available at https://github.com/Imageomics/Prompt_CAM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chowdhury_Prompt-CAM_Making_Vision_Transformers_Interpretable_for_Fine-Grained_Analysis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chowdhury_Prompt-CAM_Making_Vision_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chowdhury_2025_CVPR,
    author    = {Chowdhury, Arpita and Paul, Dipanjyoti and Mai, Zheda and Gu, Jianyang and Zhang, Ziheng and Mehrab, Kazi Sajeed and Campolongo, Elizabeth G. and Rubenstein, Daniel and Stewart, Charles V. and Karpatne, Anuj and Berger-Wolf, Tanya and Su, Yu and Chao, Wei-Lun},
    title     = {Prompt-CAM: Making Vision Transformers Interpretable for Fine-Grained Analysis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4375-4385}
}

raw_affiliation:
[{"affiliation": "The Ohio State University", "city": "Columbus", "country": "USA"}, {"affiliation": "University of Tsukuba", "city": "Tsukuba", "country": "Japan"}, {"affiliation": "Virginia Tech", "city": "Blacksburg", "country": "USA"}, {"affiliation": "Princeton University", "city": "Princeton", "country": "USA"}, {"affiliation": "Rensselaer Polytechnic Institute", "city": "Troy", "country": "USA"}]

affiliations:
["The Ohio State University", "University of Tsukuba", "Virginia Tech", "Princeton University", "Rensselaer Polytechnic Institute"]


title: Instruction-based Image Manipulation by Watching How Things Move
authors: Mingdeng Cao, Xuaner Zhang, Yinqiang Zheng, Zhihao Xia
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2704-2713
affiliations: ["The University of Tokyo", "Adobe"]
cities: ["Tokyo", "San Jose"]
countries: ["Japan", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cao_Instruction-based_Image_Manipulation_by_Watching_How_Things_Move_CVPR_2025_paper.html

abstract:
This paper introduces a novel dataset construction pipeline that samples pairs of frames from videos and uses multimodal large language models (MLLMs) to generate editing instructions for training instruction-based image manipulation models. Video frames inherently preserve the identity of subjects and scenes, ensuring consistent content preservation during editing. Additionally, video data captures diverse, natural dynamics--such as non-rigid subject motion and complex camera movements--that are difficult to model otherwise, making it an ideal source for scalable dataset construction. Using this approach, we create a new dataset to train InstructMove, a model capable of instruction-based complex manipulations that are difficult to achieve with synthetically generated datasets. Our model demonstrates state-of-the-art performance in tasks such as adjusting subject poses, rearranging elements, and altering camera perspectives.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cao_Instruction-based_Image_Manipulation_by_Watching_How_Things_Move_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cao_Instruction-based_Image_Manipulation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cao_2025_CVPR,
    author    = {Cao, Mingdeng and Zhang, Xuaner and Zheng, Yinqiang and Xia, Zhihao},
    title     = {Instruction-based Image Manipulation by Watching How Things Move},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2704-2713}
}

raw_affiliation:
[{"affiliation": "The University of Tokyo", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Adobe", "city": "San Jose", "country": "USA"}]


title: DPFlow: Adaptive Optical Flow Estimation with a Dual-Pyramid Framework
authors: Henrique Morimitsu, Xiaobin Zhu, Roberto M. Cesar, Xiangyang Ji, Xu-Cheng Yin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17810-17820
cities: ["Beijing", "Sao Paulo", "Beijing"]
countries: ["China", "Brazil", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Morimitsu_DPFlow_Adaptive_Optical_Flow_Estimation_with_a_Dual-Pyramid_Framework_CVPR_2025_paper.html

abstract:
Optical flow estimation is essential for video processing tasks, such as restoration and action recognition. The quality of videos is constantly increasing, with current standards reaching 8K resolution. However, optical flow methods are usually designed for low resolution and do not generalize to large inputs due to their rigid architectures. They adopt downscaling or input tiling to reduce the input size, causing a loss of details and global information. There is also a lack of optical flow benchmarks to judge the actual performance of existing methods on high-resolution samples. Previous works only conducted qualitative high-resolution evaluations on hand-picked samples. This paper fills this gap in optical flow estimation in two ways. We propose DPFlow, an adaptive optical flow architecture capable of generalizing up to 8K resolution inputs while trained with only low-resolution samples. We also introduce Kubric-NK, a new benchmark for evaluating optical flow methods with input resolutions ranging from 1K to 8K. Our high-resolution evaluation pushes the boundaries of existing methods and reveals new insights about their generalization capabilities. Extensive experimental results show that DPFlow achieves state-of-the-art results on the MPI-Sintel, KITTI 2015, Spring, and other high-resolution benchmarks. The code and dataset are available at https://github.com/hmorimitsu/ptlflow/tree/main/ptlflow/models/dpflow.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Morimitsu_DPFlow_Adaptive_Optical_Flow_Estimation_with_a_Dual-Pyramid_Framework_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Morimitsu_DPFlow_Adaptive_Optical_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Morimitsu_2025_CVPR,
    author    = {Morimitsu, Henrique and Zhu, Xiaobin and Cesar, Roberto M. and Ji, Xiangyang and Yin, Xu-Cheng},
    title     = {DPFlow: Adaptive Optical Flow Estimation with a Dual-Pyramid Framework},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17810-17820}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology Beijing", "city": "Beijing", "country": "China"}, {"affiliation": "University of S\u02dcao Paulo", "city": "Sao Paulo", "country": "Brazil"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["University of Science and Technology Beijing", "University of S\u02dcao Paulo", "Tsinghua University"]


authors: Xiao-Hui Li, Fei Yin, Cheng-Lin Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15021-15032
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_DocSAM_Unified_Document_Image_Segmentation_via_Query_Decomposition_and_Heterogeneous_CVPR_2025_paper.html

title:
DocSAM: Unified Document Image Segmentation via Query Decomposition and Heterogeneous Mixed Learning

abstract:
Document image segmentation is crucial in document analysis and recognition but remains challenging due to the heterogeneity of document formats and diverse segmentation tasks. Existing methods often treat these tasks separately, leading to limited generalization and resource wastage.This paper introduces DocSAM, a transformer-based unified framework for various document image segmentation tasks, including document layout analysis, multi-granularity text segmentation, and table structure recognition by modelling these tasks as a combination of instance and semantic segmentation.Specifically, DocSAM uses a Sentence BERT to map category names from each dataset into semantic queries of the same dimension as instance queries. These queries interact through attention mechanisms and are cross-attended with image features to predict instance and semantic segmentation masks. To predict instance categories, instance queries are dot-producted with semantic queries, and scores are normalized using softmax.As a result, DocSAM can be jointly trained on heterogeneous datasets, enhancing robustness and generalization while reducing computing and storage resources. Comprehensive evaluations show that DocSAM outperforms existing methods in accuracy, efficiency, and adaptability, highlighting its potential for advancing document image understanding and segmentation in various applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_DocSAM_Unified_Document_Image_Segmentation_via_Query_Decomposition_and_Heterogeneous_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_DocSAM_Unified_Document_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Xiao-Hui and Yin, Fei and Liu, Cheng-Lin},
    title     = {DocSAM: Unified Document Image Segmentation via Query Decomposition and Heterogeneous Mixed Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15021-15032}
}

raw_affiliation:
[{"affiliation": "MAIS, Institute of Automation of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Artificial Intelligence, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["MAIS, Institute of Automation of Chinese Academy of Sciences", "School of Artificial Intelligence, University of Chinese Academy of Sciences"]


title: Ferret: An Efficient Online Continual Learning Framework under Varying Memory Constraints
authors: Yuhao Zhou, Yuxin Tian, Jindi Lv, Mingjia Shi, Yuanxi Li, Qing Ye, Shuhao Zhang, Jiancheng Lv
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4850-4861
cities: ["Chengdu", "Singapore", "Urbana", "Wuhan"]
countries: ["China", "Singapore", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Ferret_An_Efficient_Online_Continual_Learning_Framework_under_Varying_Memory_CVPR_2025_paper.html

abstract:
In the realm of high-frequency data streams, achieving real-time learning within varying memory constraints is paramount. This paper presents Ferret, a comprehensive framework designed to enhance online accuracy of Online Continual Learning (OCL) algorithms while dynamically adapting to varying memory budgets.Ferret employs a fine-grained pipeline parallelism strategy combined with an iterative gradient compensation algorithm, ensuring seamless handling of high-frequency data with minimal latency, and effectively counteracting the challenge of stale gradients in parallel training. To adapt to varying memory budgets, its automated model partitioning and pipeline planning optimizes performance regardless of memory limitations. Extensive experiments across 20 benchmarks and 5 integrated OCL algorithms show Ferret's remarkable efficiency, achieving up to 3.7x lower memory overhead to reach the same online accuracy compared to competing methods.Furthermore, Ferret consistently outperforms these methods across diverse memory budgets, underscoring its superior adaptability. These findings position Ferret as a premier solution for efficient and adaptive OCL framework in real-time environments.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Ferret_An_Efficient_Online_Continual_Learning_Framework_under_Varying_Memory_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Ferret_An_Efficient_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Yuhao and Tian, Yuxin and Lv, Jindi and Shi, Mingjia and Li, Yuanxi and Ye, Qing and Zhang, Shuhao and Lv, Jiancheng},
    title     = {Ferret: An Efficient Online Continual Learning Framework under Varying Memory Constraints},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4850-4861}
}

raw_affiliation:
[{"affiliation": "Sichuan University", "city": "Chengdu", "country": "China"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "University of Illinois Urbana-Champaign", "city": "Urbana", "country": "USA"}, {"affiliation": "Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}]

affiliations:
["Sichuan University", "National University of Singapore", "University of Illinois Urbana-Champaign", "Huazhong University of Science and Technology"]


title: Spatiotemporal Skip Guidance for Enhanced Video Diffusion Sampling
authors: Junha Hyung, Kinam Kim, Susung Hong, Min-Jung Kim, Jaegul Choo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11006-11015
affiliations: ["KAIST AI", "University of Washington"]
cities: ["Daejeon", "Seattle"]
countries: ["South Korea", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hyung_Spatiotemporal_Skip_Guidance_for_Enhanced_Video_Diffusion_Sampling_CVPR_2025_paper.html

abstract:
Diffusion models have emerged as a powerful tool for generating high-quality images, videos, and 3D content. While sampling guidance techniques like CFG improve quality, they reduce diversity and motion. Autoguidance mitigates these issues but demands extra weak model training, limiting its practicality for large-scale models. In this work, we introduce Spatiotemporal Skip Guidance (STG), a simple training-free sampling guidance method for enhancing transformer-based video diffusion models. STG employs an implicit weak model via self-perturbation, avoiding the need for external models or additional training. By selectively skipping spatiotemporal layers, STG produces an aligned, degraded version of the original model to boost sample quality without compromising diversity or dynamic degree. Our contributions include: (1) introducing STG as an efficient, high-performing guidance technique for video diffusion models, (2) eliminating the need for auxiliary models by simulating a weak model through layer skipping, and (3) ensuring quality-enhanced guidance without compromising sample diversity or dynamics unlike CFG. For additional results, visit https://junhahyung.github.io/STGuidance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hyung_Spatiotemporal_Skip_Guidance_for_Enhanced_Video_Diffusion_Sampling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hyung_Spatiotemporal_Skip_Guidance_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hyung_2025_CVPR,
    author    = {Hyung, Junha and Kim, Kinam and Hong, Susung and Kim, Min-Jung and Choo, Jaegul},
    title     = {Spatiotemporal Skip Guidance for Enhanced Video Diffusion Sampling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11006-11015}
}

raw_affiliation:
[{"affiliation": "KAIST AI", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}]


title: VidComposition: Can MLLMs Analyze Compositions in Compiled Videos?
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8490-8500
affiliations: ["University of Rochester", "Arizona State University"]
cities: ["Rochester", "Tempe"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_VidComposition_Can_MLLMs_Analyze_Compositions_in_Compiled_Videos_CVPR_2025_paper.html

authors:
Yunlong Tang, Junjia Guo, Hang Hua, Susan Liang, Mingqian Feng, Xinyang Li, Rui Mao, Chao Huang, Jing Bi, Zeliang Zhang, Pooyan Fazli, Chenliang Xu

abstract:
The advancement of Multimodal Large Language Models (MLLMs) has enabled significant progress in multimodal understanding, expanding their capacity to analyze video content. However, existing evaluation benchmarks for MLLMs primarily focus on abstract video comprehension, lacking a detailed assessment of their ability to understand video compositions, the nuanced interpretation of how visual elements combine and interact within highly compiled video contexts. We introduce VidComposition, a new benchmark specifically designed to evaluate the video composition understanding capabilities of MLLMs using carefully curated compiled videos and cinematic-level annotations.VidComposition includes 982 videos with 1706 multiple-choice questions, covering various compositional aspects such as camera movement, angle, shot size, narrative structure, character actions and emotions, etc. Our comprehensive evaluation of 33 open-source and proprietary MLLMs reveals a significant performance gap between human and model capabilities. This highlights the limitations of current MLLMs in understanding complex, compiled video compositions and offers insights into areas for further improvement. Our benchmark is publicly available at https://yunlong10.github.io/VidComposition/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_VidComposition_Can_MLLMs_Analyze_Compositions_in_Compiled_Videos_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tang_VidComposition_Can_MLLMs_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Yunlong and Guo, Junjia and Hua, Hang and Liang, Susan and Feng, Mingqian and Li, Xinyang and Mao, Rui and Huang, Chao and Bi, Jing and Zhang, Zeliang and Fazli, Pooyan and Xu, Chenliang},
    title     = {VidComposition: Can MLLMs Analyze Compositions in Compiled Videos?},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8490-8500}
}

raw_affiliation:
[{"affiliation": "University of Rochester", "city": "Rochester", "country": "USA"}, {"affiliation": "Arizona State University", "city": "Tempe", "country": "USA"}]


title: SAR3D: Autoregressive 3D Object Generation and Understanding via Multi-scale 3D VQVAE
authors: Yongwei Chen, Yushi Lan, Shangchen Zhou, Tengfei Wang, Xingang Pan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28371-28382
affiliations: ["S-Lab, Nanyang Technological University", "Shanghai Artificial Intelligence Laboratory"]
cities: ["Singapore", "Shanghai"]
countries: ["Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_SAR3D_Autoregressive_3D_Object_Generation_and_Understanding_via_Multi-scale_3D_CVPR_2025_paper.html

abstract:
Autoregressive models have demonstrated remarkable success across various fields, from large language models (LLMs) to large multimodal models (LMMs) and 2D content generation, moving closer to artificial general intelligence (AGI). Despite these advances, applying autoregressive approaches to 3D object generation and understanding remains largely unexplored. This paper introduces Scale AutoRegressive 3D (SAR3D), a novel framework that leverages a multi-scale 3D vector-quantized variational autoencoder (VQVAE) to tokenize 3D objects for efficient autoregressive generation and detailed understanding. By predicting the next scale in a multi-scale latent representation instead of the next single token, SAR3D reduces generation time significantly, achieving fast 3D object generation in just 0.82 seconds on an A6000 GPU. Additionally, given the tokens enriched with hierarchical 3D-aware information, we finetune a pretrained LLM on them, enabling multimodal comprehension of 3D content.Our experiments show that SAR3D surpasses current 3D generation methods in both speed and quality and allows LLMs to interpret and caption 3D models comprehensively.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_SAR3D_Autoregressive_3D_Object_Generation_and_Understanding_via_Multi-scale_3D_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_SAR3D_Autoregressive_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Yongwei and Lan, Yushi and Zhou, Shangchen and Wang, Tengfei and Pan, Xingang},
    title     = {SAR3D: Autoregressive 3D Object Generation and Understanding via Multi-scale 3D VQVAE},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28371-28382}
}

raw_affiliation:
[{"affiliation": "S-Lab, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}]


title: Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30420-30429
affiliations: ["Fudan University", "Youtu Lab, Tencent", "Shanghai Jiao Tong University", "Zhejiang University"]
cities: ["Shanghai", "Shenzhen", "Shanghai", "Hangzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jin_Dual-Interrelated_Diffusion_Model_for_Few-Shot_Anomaly_Image_Generation_CVPR_2025_paper.html

authors:
Ying Jin, Jinlong Peng, Qingdong He, Teng Hu, Jiafu Wu, Hao Chen, Haoxuan Wang, Wenbing Zhu, Mingmin Chi, Jun Liu, Yabiao Wang

abstract:
The performance of anomaly inspection in industrial manufacturing is constrained by the scarcity of anomaly data. To overcome this challenge, researchers have started employing anomaly generation approaches to augment the anomaly dataset. However, existing anomaly generation methods suffer from limited diversity in the generated anomalies and struggle to achieve a seamless blending of this anomaly with the original image. Moreover, the generated mask is usually not aligned with the generated anomaly. In this paper, we overcome these challenges from a new perspective, simultaneously generating a pair of the overall image and the corresponding anomaly part. We propose DualAnoDiff, a novel diffusion-based few-shot anomaly image generation model, which can generate diverse and realistic anomaly images by using a dual-interrelated diffusion model, where one of them is employed to generate the whole image while the other one generates the anomaly part. Moreover, we extract background and shape information to mitigate the distortion and blurriness phenomenon in few-shot image generation.  Extensive experiments demonstrate the superiority of our proposed model over state-of-the-art methods in terms of diversity, realism and the accuracy of mask. Overall, our approach significantly improves the performance of downstream anomaly inspection tasks, including anomaly detection, anomaly localization, and anomaly classification tasks. Code will be made available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jin_Dual-Interrelated_Diffusion_Model_for_Few-Shot_Anomaly_Image_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jin_Dual-Interrelated_Diffusion_Model_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jin_2025_CVPR,
    author    = {Jin, Ying and Peng, Jinlong and He, Qingdong and Hu, Teng and Wu, Jiafu and Chen, Hao and Wang, Haoxuan and Zhu, Wenbing and Chi, Mingmin and Liu, Jun and Wang, Yabiao},
    title     = {Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30420-30429}
}

raw_affiliation:
[{"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Youtu Lab, Tencent", "city": "Shenzhen", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]


title: ODE: Open-Set Evaluation of Hallucinations in Multimodal Large Language Models
authors: Yahan Tu, Rui Hu, Jitao Sang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19836-19845
raw_affiliation: [{"affiliation": "Beijing Jiaotong University", "city": "Beijing", "country": "China"}]
affiliations: ["Beijing Jiaotong University"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tu_ODE_Open-Set_Evaluation_of_Hallucinations_in_Multimodal_Large_Language_Models_CVPR_2025_paper.html

abstract:
Hallucination poses a persistent challenge for multimodal large language models (MLLMs). However, existing benchmarks for evaluating hallucinations are generally static, which may overlook the potential risk of data contamination. To address this issue, we propose ODE, an open-set, dynamic protocol designed to evaluate object hallucinations in MLLMs at both the existence and attribute levels. ODE employs a graph-based structure to represent real-world object concepts, their attributes, and the distributional associations between them. This structure facilitates the extraction of concept combinations based on diverse distributional criteria, generating varied samples for structured queries that evaluate hallucinations in both generative and discriminative tasks. Through the generation of new samples, dynamic concept combinations, and varied distribution frequencies, ODE mitigates the risk of data contamination and broadens the scope of evaluation. This protocol is applicable to both general and specialized scenarios, including those with limited data. Experimental results demonstrate the effectiveness of our protocol, revealing that MLLMs exhibit higher hallucination rates when evaluated with ODE-generated samples, which indicates potential data contamination. Furthermore, these generated samples aid in analyzing hallucination patterns and fine-tuning models, offering an effective approach to mitigating hallucinations in MLLMs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tu_ODE_Open-Set_Evaluation_of_Hallucinations_in_Multimodal_Large_Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tu_ODE_Open-Set_Evaluation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tu_2025_CVPR,
    author    = {Tu, Yahan and Hu, Rui and Sang, Jitao},
    title     = {ODE: Open-Set Evaluation of Hallucinations in Multimodal Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19836-19845}
}


title: Self-Supervised Learning for Color Spike Camera Reconstruction
authors: Yanchen Dong, Ruiqin Xiong, Xiaopeng Fan, Zhaofei Yu, Yonghong Tian, Tiejun Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6231-6240
cities: ["Beijing", "Harbin", "Beijing", "Beijing", "Shenzhen"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dong_Self-Supervised_Learning_for_Color_Spike_Camera_Reconstruction_CVPR_2025_paper.html

abstract:
Spike camera is a kind of neuromorphic camera with ultra-high temporal resolution, which can capture dynamic scenes by continuously firing spike signals. To capture color information, a color filter array (CFA) is employed on the sensor of the spike camera, resulting in Bayer-pattern spike streams. How to restore high-quality color images from the binary spike signals remains challenging. In this paper, we propose a motion-guided reconstruction method for spike cameras with CFA, utilizing color layout and estimated motion information. Specifically, we develop a joint motion estimation pipeline for the Bayer-pattern spike stream, exploiting the motion consistency of channels. We propose to estimate the missing pixels of each color channel according to temporally neighboring pixels of the corresponding color along the motion trajectory. As the spike signals are read out at discrete time points, there is quantization noise that impacts the image quality. Thus, we analyze the correlation of the noise in spatial and temporal domains and propose a self-supervised network utilizing a masked spike encoder to handle the noise. Experiments on real-world captured Bayer-pattern spike streams show that our method can restore color images with better visual quality, compared with state-of-the-art methods. The source codes are available at https://github.com/csycdong/SSL-CSC.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dong_Self-Supervised_Learning_for_Color_Spike_Camera_Reconstruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dong_Self-Supervised_Learning_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dong_2025_CVPR,
    author    = {Dong, Yanchen and Xiong, Ruiqin and Fan, Xiaopeng and Yu, Zhaofei and Tian, Yonghong and Huang, Tiejun},
    title     = {Self-Supervised Learning for Color Spike Camera Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6231-6240}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer Science and T echnology, Harbin Institute of T echnology", "city": "Harbin", "country": "China"}, {"affiliation": "Institute for Arti\ufb01cial Intelligence, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Electronic and Computer Engineering, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}]

affiliations:
["School of Computer Science, Peking University", "School of Computer Science and T echnology, Harbin Institute of T echnology", "Institute for Arti\ufb01cial Intelligence, Peking University", "School of Electronic and Computer Engineering, Peking University", "Peng Cheng Laboratory"]


title: Interactive Medical Image Analysis with Concept-based Similarity Reasoning
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30797-30806
affiliations: ["Australian Institute for Machine Learning, The University of Adelaide", "Flinders University"]
cities: ["Adelaide", "Adelaide"]
countries: ["Australia", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huy_Interactive_Medical_Image_Analysis_with_Concept-based_Similarity_Reasoning_CVPR_2025_paper.html

authors:
Ta Duc Huy, Sen Kim Tran, Phan Nguyen, Nguyen Hoang Tran, Tran Bao Sam, Anton van den Hengel, Zhibin Liao, Johan W. Verjans, Minh-Son To, Vu Minh Hieu Phan

abstract:
The ability to interpret and intervene model decisions is important for the adoption of computer-aided diagnosis methods in clinical workflows. Recent concept-based methods link the model predictions with interpretable concepts and modify their activation scores to interact with the model. However, these concepts are at the image level, which hinders the model from pinpointing the exact patches the concepts are activated. Alternatively, prototype-based methods learn representations from training image patches and compare these with test image patches, using the similarity scores for final class prediction. However, interpreting the underlying concepts of these patches can be challenging and often necessitates post-hoc guesswork. To address this issue, this paper introduces the novel Concept-based Similarity Reasoning network (CSR), which offers (i) patch-level prototype with intrinsic concept interpretation, and (ii) spatial interactivity. First, the proposed CSR provides localized explanation by grounding prototypes of each concept on image regions. Second, our model introduces novel spatial-level interaction, allowing doctors to engage directly with specific image areas, making it an intuitive and transparent tool for medical imaging. CSR improves upon prior state-of-the-art interpretable methods by up to 4.% across three biomedical datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huy_Interactive_Medical_Image_Analysis_with_Concept-based_Similarity_Reasoning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huy_Interactive_Medical_Image_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Huy_2025_CVPR,
    author    = {Huy, Ta Duc and Tran, Sen Kim and Nguyen, Phan and Tran, Nguyen Hoang and Sam, Tran Bao and van den Hengel, Anton and Liao, Zhibin and Verjans, Johan W. and To, Minh-Son and Phan, Vu Minh Hieu},
    title     = {Interactive Medical Image Analysis with Concept-based Similarity Reasoning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30797-30806}
}

raw_affiliation:
[{"affiliation": "Australian Institute for Machine Learning, The University of Adelaide", "city": "Adelaide", "country": "Australia"}, {"affiliation": "Flinders University", "city": "Adelaide", "country": "Australia"}]


title: From Elements to Design: A Layered Approach for Automatic Graphic Design Composition
authors: Jiawei Lin, Shizhao Sun, Danqing Huang, Ting Liu, Ji Li, Jiang Bian
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8128-8137
affiliations: ["Xi\u2019an Jiaotong University", "Microsoft Research"]
cities: ["Xi'an", "Redmond"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_From_Elements_to_Design_A_Layered_Approach_for_Automatic_Graphic_CVPR_2025_paper.html

abstract:
In this work, we investigate automatic design composition from multimodal graphic elements. Although recent studies have developed various generative models for graphic design, they usually face the following limitations: they only focus on certain subtasks and are far from achieving the design composition task; they do not consider the hierarchical information of graphic designs during the generation process. To tackle these issues, we introduce the layered design principle into Large Multimodal Models (LMMs) and propose a novel approach, called LaDeCo, to accomplish this challenging task. Specifically, LaDeCo first performs layer planning for a given element set, dividing the input elements into different semantic layers according to their contents. Based on the planning results, it subsequently predicts element attributes that control the design composition in a layer-wise manner, and includes the rendered image of previously generated layers into the context. With this insightful design, LaDeCo decomposes the difficult task into smaller manageable steps, making the generation process smoother and clearer. The experimental results demonstrate the effectiveness of LaDeCo in design composition. Furthermore, we show that LaDeCo enables some interesting applications in graphic design, such as resolution adjustment, design decoration, design variation, etc. In addition, it even outperforms the specialized models in some design subtasks without any task-specific training.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_From_Elements_to_Design_A_Layered_Approach_for_Automatic_Graphic_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_From_Elements_to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Jiawei and Sun, Shizhao and Huang, Danqing and Liu, Ting and Li, Ji and Bian, Jiang},
    title     = {From Elements to Design: A Layered Approach for Automatic Graphic Design Composition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8128-8137}
}

raw_affiliation:
[{"affiliation": "Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "Microsoft Research", "city": "Redmond", "country": "USA"}]


title: h-Edit: Effective and Flexible Diffusion-Based Editing via Doob's h-Transform
authors: Toan Nguyen, Kien Do, Duc Kieu, Thin Nguyen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28490-28501
affiliations: ["Applied Artificial Intelligence Institute (A2I2), Deakin University"]
cities: ["Geelong"]
countries: ["Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nguyen_h-Edit_Effective_and_Flexible_Diffusion-Based_Editing_via_Doobs_h-Transform_CVPR_2025_paper.html

abstract:
We introduce a theoretical framework for diffusion-based image editing by formulating it as a reverse-time bridge modeling problem. This approach modifies the backward process of a pretrained diffusion model to construct a bridge that converges to an implicit distribution associated with the editing target at time 0. Building on this framework, we propose h-Edit, a novel editing method that utilizes Doob's h-transform and Langevin Monte Carlo to decompose the update of an intermediate edited sample into two components: a "reconstruction" term and an "editing" term. This decomposition provides flexibility, allowing the reconstruction term to be computed via existing inversion techniques and enabling the combination of multiple editing terms to handle complex editing tasks. To our knowledge, h-Edit is the first training-free method capable of performing simultaneous text-guided and reward-model-based editing. Extensive experiments, both quantitative and qualitative, show that h-Edit outperforms state-of-the-art baselines in terms of editing effectiveness and faithfulness.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nguyen_h-Edit_Effective_and_Flexible_Diffusion-Based_Editing_via_Doobs_h-Transform_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nguyen_h-Edit_Effective_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nguyen_2025_CVPR,
    author    = {Nguyen, Toan and Do, Kien and Kieu, Duc and Nguyen, Thin},
    title     = {h-Edit: Effective and Flexible Diffusion-Based Editing via Doob's h-Transform},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28490-28501}
}

raw_affiliation:
[{"affiliation": "Applied Artificial Intelligence Institute (A2I2), Deakin University", "city": "Geelong", "country": "Australia"}]


title: Masking meets Supervision: A Strong Learning Alliance
authors: Byeongho Heo, Taekyung Kim, Sangdoo Yun, Dongyoon Han
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20447-20457
raw_affiliation: [{"affiliation": "NA VER AI Lab", "city": "Seoul", "country": "South Korea"}]
affiliations: ["NA VER AI Lab"]
cities: ["Seoul"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Heo_Masking_meets_Supervision_A_Strong_Learning_Alliance_CVPR_2025_paper.html

abstract:
Pre-training with random masked inputs has emerged as a novel trend in self-supervised training. However, supervised learning still faces a challenge in adopting masking augmentations, primarily due to unstable training. In this paper, we propose a novel way to involve masking augmentations dubbed Masked Sub-branch (MaskSub). MaskSub consists of the main-branch and sub-branch, the latter being a part of the former. The main-branch undergoes conventional training recipes, while the sub-branch merits intensive masking augmentations, during training. MaskSub tackles the challenge by mitigating adverse effects through a relaxed loss function similar to a self-distillation loss. Our analysis shows that MaskSub improves performance, with the training loss converging faster than in standard training, which suggests our method stabilizes the training process. We further validate MaskSub across diverse training scenarios and models, including DeiT-III training, MAE finetuning, CLIP finetuning, BERT training, and hierarchical architectures (ResNet and Swin Transformer). Our results show that MaskSub consistently achieves impressive performance gains across all the cases. MaskSub provides a practical and effective solution for introducing additional regularization under various training recipes. Code available at https://github.com/naver-ai/augsub

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Heo_Masking_meets_Supervision_A_Strong_Learning_Alliance_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Heo_Masking_meets_Supervision_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Heo_2025_CVPR,
    author    = {Heo, Byeongho and Kim, Taekyung and Yun, Sangdoo and Han, Dongyoon},
    title     = {Masking meets Supervision: A Strong Learning Alliance},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20447-20457}
}


authors: Wang Zhao, Yan-Pei Cao, Jiale Xu, Yuejiang Dong, Ying Shan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11061-11072
affiliations: ["ARC Lab, Tencent PCG", "V AST", "Tsinghua University"]
cities: ["Shenzhen", "Unknown", "Beijing"]
countries: ["China", "Unknown", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_DI-PCG_Diffusion-based_Efficient_Inverse_Procedural_Content_Generation_for_High-quality_3D_CVPR_2025_paper.html

title:
DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation

abstract:
Procedural Content Generation (PCG) is powerful in creating high-quality 3D contents, yet controlling it to produce desired shapes is difficult and often requires extensive parameter tuning. Inverse Procedural Content Generation aims to automatically find the best parameters under the input condition. However, existing sampling-based and neural network-based methods still suffer from numerous sample iterations or limited controllability. In this work, we present DI-PCG, a novel and efficient method for Inverse PCG from general image conditions. At its core is a lightweight diffusion transformer model, where PCG parameters are directly treated as the denoising target and the observed images as conditions to control parameter generation. DI-PCG is efficient and effective. With only 7.6M network parameters and 30 GPU hours to train, it demonstrates superior performance in recovering parameters accurately, and generalizing well to in-the-wild images. Quantitative and qualitative experiment results validate the effectiveness of DI-PCG in inverse PCG and image-to-3D generation tasks. DI-PCG offers a promising approach for efficient inverse PCG and represents a valuable exploration step towards a 3D generation path that models how to construct a 3D asset using parametric models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_DI-PCG_Diffusion-based_Efficient_Inverse_Procedural_Content_Generation_for_High-quality_3D_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_DI-PCG_Diffusion-based_Efficient_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Wang and Cao, Yan-Pei and Xu, Jiale and Dong, Yuejiang and Shan, Ying},
    title     = {DI-PCG: Diffusion-based Efficient Inverse Procedural Content Generation for High-quality 3D Asset Creation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11061-11072}
}

raw_affiliation:
[{"affiliation": "ARC Lab, Tencent PCG", "city": "Shenzhen", "country": "China"}, {"affiliation": "V AST", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]


