# papers

title: SoftShadow: Leveraging Soft Masks for Penumbra-Aware Shadow Removal
authors: Xinrui Wang, Lanqing Guo, Xiyu Wang, Siyu Huang, Bihan Wen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23217-23226
affiliations: ["Nanyang Technological University", "The University of Texas at Austin", "Clemson University"]
cities: ["Singapore", "Austin", "Clemson"]
countries: ["Singapore", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_SoftShadow_Leveraging_Soft_Masks_for_Penumbra-Aware_Shadow_Removal_CVPR_2025_paper.html

abstract:
Recent advancements in deep learning have yielded promising results for the image shadow removal task. However, most existing methods rely on binary pre-generated shadow masks. The binary nature of such masks could potentially lead to artifacts near the boundary between shadow and non-shadow areas. In view of this, inspired by the physical model of shadow formation, we introduce novel soft shadow masks specifically designed for shadow removal. To achieve such soft masks, we propose a SoftShadow framework by leveraging the prior knowledge of pretrained SAM and integrating physical constraints. Specifically, we jointly tune the SAM and the subsequent shadow removal network using penumbra formation constraint loss, mask reconstruction loss, and shadow removal loss. This framework enables accurate predictions of penumbra (partially shaded) and umbra (fully shaded) areas while simultaneously facilitating end-to-end shadow removal. Through extensive experiments on popular datasets, we found that our SoftShadow framework, which generates soft masks, can better restore boundary artifacts, achieve state-of-the-art performance, and demonstrate superior generalizability.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_SoftShadow_Leveraging_Soft_Masks_for_Penumbra-Aware_Shadow_Removal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_SoftShadow_Leveraging_Soft_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Xinrui and Guo, Lanqing and Wang, Xiyu and Huang, Siyu and Wen, Bihan},
    title     = {SoftShadow: Leveraging Soft Masks for Penumbra-Aware Shadow Removal},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23217-23226}
}

raw_affiliation:
[{"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "The University of Texas at Austin", "city": "Austin", "country": "USA"}, {"affiliation": "Clemson University", "city": "Clemson", "country": "USA"}]


authors: Ruiheng Liu, Haozhe Chen, Boyao Zhao, Kejiang Chen, Weiming Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20169-20178
cities: ["Hefei", "Hefei"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Graph-Embedded_Structure-Aware_Perceptual_Hashing_for_Neural_Network_Protection_and_Piracy_CVPR_2025_paper.html

title:
Graph-Embedded Structure-Aware Perceptual Hashing for Neural Network Protection and Piracy Detection

abstract:
The advancement of AI technology has significantly influenced production activities, increasing the focus on copyright protection for AI models. Model perceptual hashing offers an efficient solution for retrieving the pirated models. Existing methods, such as handcrafted feature-based and dual-branch network-based perceptual hashing, have proven effective in detecting pirated models. However, these approaches often struggle to differentiate non-pirated models, leading to frequent false positives in model authentication and protection. To address this challenge, this paper proposes a structurally-aware perceptual model hashing technique that achieved reduced false positives while maintaining high true positive rates. Specifically, we introduce a method for converting the diverse neural network structures into graph structures suitable for DNN processing, then utilize a graph neural network to learn their structural features representation. Our approach integrates perceptual parameter-based model hashing, achieving robust performance with higher detection accuracy and fewer false positives. Experimental results show that the proposed method has only 3% false positive rate when detecting the non-pirated model, and the detection accuracy of pirated model reaches more than 98%.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Graph-Embedded_Structure-Aware_Perceptual_Hashing_for_Neural_Network_Protection_and_Piracy_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Graph-Embedded_Structure-Aware_Perceptual_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Ruiheng and Chen, Haozhe and Zhao, Boyao and Chen, Kejiang and Zhang, Weiming},
    title     = {Graph-Embedded Structure-Aware Perceptual Hashing for Neural Network Protection and Piracy Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20169-20178}
}

raw_affiliation:
[{"affiliation": "Anhui Province Key Laboratory of Digital Security, University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Anhui University", "city": "Hefei", "country": "China"}]

affiliations:
["Anhui Province Key Laboratory of Digital Security, University of Science and Technology of China", "Anhui University"]


title: VTON-HandFit: Virtual Try-on for Arbitrary Hand Pose Guided by Hand Priors Embedding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22616-22626
cities: ["Xiamen", "Xiamen", "Unknown"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liang_VTON-HandFit_Virtual_Try-on_for_Arbitrary_Hand_Pose_Guided_by_Hand_CVPR_2025_paper.html

authors:
Yujie Liang, Xiaobin Hu, Boyuan Jiang, Donghao Luo, Xu Peng, Kai Wu, Chengming Xu, Wenhui Han, Taisong Jin, Chengjie Wang, Rongrong Ji

abstract:
Although diffusion-based image virtual try-on has made considerable progress, emerging approaches still struggle to effectively address the issue of hand occlusion (i.e., clothing regions occluded by the hand part), leading to a notable degradation of the try-on performance. To tackle this issue widely existing in real-world scenarios, we propose VTON-HandFit, leveraging the power of hand priors to reconstruct the appearance and structure for hand occlusion cases. Firstly, we tailor a Hand-Pose Aggregation Net using the ControlNet-based structure explicitly and adaptively encoding the global hand and pose priors. Besides, to fully exploit the hand-related structure and appearance information, we propose Hand-feature Disentanglement Embedding module to disentangle the hand priors into the hand structure-parametric and visual-appearance features, and customize a masked cross attention for further decoupled feature embedding. Lastly, we customize a hand-canny constraint loss to better learn the structure edge knowledge from the hand template of model image. VTON-HandFit outperforms the baselines in qualitative and quantitative evaluations on the public dataset and our self-collected hand-occlusion Handfit-3K dataset particularly for the arbitrary hand pose occlusion cases in real-world scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_VTON-HandFit_Virtual_Try-on_for_Arbitrary_Hand_Pose_Guided_by_Hand_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Liang_2025_CVPR,
    author    = {Liang, Yujie and Hu, Xiaobin and Jiang, Boyuan and Luo, Donghao and Peng, Xu and Wu, Kai and Xu, Chengming and Han, Wenhui and Jin, Taisong and Wang, Chengjie and Ji, Rongrong},
    title     = {VTON-HandFit: Virtual Try-on for Arbitrary Hand Pose Guided by Hand Priors Embedding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22616-22626}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, China", "city": "Xiamen", "country": "China"}, {"affiliation": "School of Informatics, Xiamen University, China", "city": "Xiamen", "country": "China"}, {"affiliation": "Tencent Youtu Lab", "city": "Unknown", "country": "China"}]

affiliations:
["Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, China", "School of Informatics, Xiamen University, China", "Tencent Youtu Lab"]


title: Interleaved-Modal Chain-of-Thought
authors: Jun Gao, Yongqi Li, Ziqiang Cao, Wenjie Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19520-19529
cities: ["Suzhou", "Hong Kong"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_Interleaved-Modal_Chain-of-Thought_CVPR_2025_paper.html

abstract:
Chain-of-Thought (CoT) prompting elicits large language models (LLMs) to produce a series of intermediate reasoning steps before arriving at the final answer.However, when transitioning to vision-language models (VLMs), their text-only rationales struggle to express the fine-grained associations with the original image.In this paper, we propose an image-incorporated multimodal Chain-of-Thought, named Interleaved-modal Chain-of-Thought (ICoT), which generates sequential reasoning steps consisting of paired visual and textual rationales to infer the final answer.Intuitively, the novel ICoT requires VLMs to enable the generation of fine-grained interleaved-modal content, which is hard for current VLMs to fulfill.Considering that the required visual information is usually part of the input image, we propose Attention-driven Selection (ADS) to realize ICoT over existing VLMs.ADS intelligently inserts regions of the input image to generate the interleaved-modal reasoning steps with ignorable additional latency.ADS relies solely on the attention map of VLMs without the need for parameterization, and therefore it is a plug-and-play strategy that can be generalized to a spectrum of VLMs.We apply ADS to realize ICoT on two popular VLMs of different architectures.Extensive evaluations of three benchmarks have shown that ICoT prompting achieves substantial performance (up to 14%) and interpretability improvements compared to existing multimodal CoT prompting methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_Interleaved-Modal_Chain-of-Thought_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_Interleaved-Modal_Chain-of-Thought_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gao_2025_CVPR,
    author    = {Gao, Jun and Li, Yongqi and Cao, Ziqiang and Li, Wenjie},
    title     = {Interleaved-Modal Chain-of-Thought},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19520-19529}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology, Soochow University", "city": "Suzhou", "country": "China"}, {"affiliation": "Department of Computer Science, The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "China"}]

affiliations:
["School of Computer Science and Technology, Soochow University", "Department of Computer Science, The Hong Kong Polytechnic University"]


title: Uni-Renderer: Unifying Rendering and Inverse Rendering Via Dual Stream Diffusion
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26504-26513
affiliations: ["HKUST(GZ)", "HKUST", "Quwan"]
cities: ["Guangzhou", "Hong Kong", "Unknown"]
countries: ["China", "China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Uni-Renderer_Unifying_Rendering_and_Inverse_Rendering_Via_Dual_Stream_Diffusion_CVPR_2025_paper.html

authors:
Zhifei Chen, Tianshuo Xu, Wenhang Ge, Leyi Wu, Dongyu Yan, Jing He, Luozhou Wang, Lu Zeng, Shunsi Zhang, Ying-Cong Chen

abstract:
Rendering and inverse rendering are pivotal tasks in both computer vision and graphics. The rendering equation is the core of the two tasks, as an ideal conditional distribution transfer function from intrinsic properties to RGB images. Despite achieving promising results of existing rendering methods, they merely approximate the ideal estimation for a specific scene and come with a high computational cost. Additionally, the inverse conditional distribution transfer is intractable due to the inherent ambiguity. To address these challenges, we propose a data-driven method that jointly models rendering and inverse rendering as two conditional generation tasks within a single diffusion framework. Inspired by UniDiffuser, we utilize two distinct time schedules to model both tasks, and with a tailored dual streaming module, we achieve cross-conditioning of two pre-trained diffusion models. This unified approach, named Uni-Renderer, allows the two processes to facilitate each other through a cycle-consistent constrain, mitigating ambiguity by enforcing consistency between intrinsic properties and rendered images.Combined with a meticulously prepared dataset, our method effectively decomposition of intrinsic properties and demonstrating a strong capability to recognize changes during rendering. We will open-source our training and inference code to the public, fostering further research and development in this area.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Uni-Renderer_Unifying_Rendering_and_Inverse_Rendering_Via_Dual_Stream_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Uni-Renderer_Unifying_Rendering_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Zhifei and Xu, Tianshuo and Ge, Wenhang and Wu, Leyi and Yan, Dongyu and He, Jing and Wang, Luozhou and Zeng, Lu and Zhang, Shunsi and Chen, Ying-Cong},
    title     = {Uni-Renderer: Unifying Rendering and Inverse Rendering Via Dual Stream Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26504-26513}
}

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


title: Enhancing Adversarial Transferability with Checkpoints of a Single Model's Training
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20685-20694
cities: ["Wuhan", "Shanghai", "Redmond", "Newcastle"]
countries: ["China", "China", "USA", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Enhancing_Adversarial_Transferability_with_Checkpoints_of_a_Single_Models_Training_CVPR_2025_paper.html

authors:
Shixin Li, Chaoxiang He, Xiaojing Ma, Bin Benjamin Zhu, Shuo Wang, Hongsheng Hu, Dongmei Zhang, Linchen Yu

abstract:
Adversarial attacks threaten the integrity of deep neural networks (DNNs), particularly in high-stakes applications. In this paper, we present a novel black-box adversarial attack that leverages the diverse checkpoints generated during a single model's training trajectory. Unlike conventional ensemble attacks that require multiple surrogate models with diverse architectures, our approach exploits the intrinsic diversity captured over different training stages of a single surrogate model. By decomposing the learned representations into task-intrinsic and task-irrelevant components, we employ an accuracy gap-based selection strategy to identify checkpoints that predominantly capture transferable, task-intrinsic knowledge. Extensive experiments on ImageNet and CIFAR-10 demonstrate that our method consistently outperforms traditional ensemble attacks in terms of transferability, even under resource-constrained and practical settings. This work offers a resource-efficient solution for crafting highly transferable adversarial examples and provides new insights into the dynamics of adversarial vulnerability.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Enhancing_Adversarial_Transferability_with_Checkpoints_of_a_Single_Models_Training_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Shixin and He, Chaoxiang and Ma, Xiaojing and Zhu, Bin Benjamin and Wang, Shuo and Hu, Hongsheng and Zhang, Dongmei and Yu, Linchen},
    title     = {Enhancing Adversarial Transferability with Checkpoints of a Single Model's Training},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20685-20694}
}

raw_affiliation:
[{"affiliation": "Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Microsoft Corporation", "city": "Redmond", "country": "USA"}, {"affiliation": "University of Newcastle, Australia", "city": "Newcastle", "country": "Australia"}]

affiliations:
["Huazhong University of Science and Technology", "Shanghai Jiao Tong University", "Microsoft Corporation", "University of Newcastle, Australia"]


title: POSTA: A Go-to Framework for Customized Artistic Poster Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28694-28704
cities: ["Guangzhou", "Hong Kong", "Singapore", "Hong Kong"]
countries: ["China", "China", "Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_POSTA_A_Go-to_Framework_for_Customized_Artistic_Poster_Generation_CVPR_2025_paper.html

authors:
Haoyu Chen, Xiaojie Xu, Wenbo Li, Jingjing Ren, Tian Ye, Songhua Liu, Ying-Cong Chen, Lei Zhu, Xinchao Wang

abstract:
Poster design is a critical medium for visual communication. Prior work has explored automatic poster design using deep learning techniques, but these approaches lack text accuracy, user customization, and aesthetic appeal, limiting their applicability in artistic domains such as movies and exhibitions, where both clear content delivery and visual impact are essential. To address these limitations, we present POSTA: a modular framework powered by diffusion models and multimodal large language models (MLLMs) for customized artistic poster generation. The framework consists of three modules. Background Diffusion creates a themed background based on user input. Design MLLM then generates layout and typography elements that align with and complement the background style. Finally, to enhance the poster's aesthetic appeal, ArtText Diffusion applies additional stylization to key text elements. The final result is a visually cohesive and appealing poster, with a fully modular process that allows for complete customization. To train our models, we develop the PosterArt dataset, comprising high-quality artistic posters annotated with layout, typography, and pixel-level stylized text segmentation. Our comprehensive experimental analysis demonstrates POSTA's exceptional controllability and design diversity, outperforming existing models in both text accuracy and aesthetic quality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_POSTA_A_Go-to_Framework_for_Customized_Artistic_Poster_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_POSTA_A_Go-to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Haoyu and Xu, Xiaojie and Li, Wenbo and Ren, Jingjing and Ye, Tian and Liu, Songhua and Chen, Ying-Cong and Zhu, Lei and Wang, Xinchao},
    title     = {POSTA: A Go-to Framework for Customized Artistic Poster Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28694-28704}
}

raw_affiliation:
[{"affiliation": "The Hong Kong University of Science and Technology (Guangzhou)", "city": "Guangzhou", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}]

affiliations:
["The Hong Kong University of Science and Technology (Guangzhou)", "The Chinese University of Hong Kong", "National University of Singapore", "The Hong Kong University of Science and Technology"]


title: NSD-Imagery: A Benchmark Dataset for Extending fMRI Vision Decoding Methods to Mental Imagery
authors: Reese Kneeland, Paul S. Scotti, Ghislain St-Yves, Jesse Breedlove, Kendrick Kay, Thomas Naselaris
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28852-28862
cities: ["Minneapolis", "Unknown", "Princeton", "Unknown"]
countries: ["USA", "Unknown", "USA", "Unknown"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kneeland_NSD-Imagery_A_Benchmark_Dataset_for_Extending_fMRI_Vision_Decoding_Methods_CVPR_2025_paper.html

abstract:
We release NSD-Imagery, a benchmark dataset of human fMRI activity paired with mental images, to complement the existing Natural Scenes Dataset (NSD), a large-scale dataset of fMRI activity paired with seen images that enabled unprecedented improvements in fMRI-to-image reconstruction efforts. Recent models trained on NSD have been evaluated only on seen image reconstruction. Using NSD-Imagery, it is possible to assess how well these models perform on mental image reconstruction. This is a challenging generalization requirement because mental images are encoded in human brain activity with relatively lower signal-to-noise and spatial resolution; however, generalization from seen to mental imagery is critical for real-world applications in medical domains and brain-computer interfaces, where the desired information is always internally generated. We provide benchmarks for a suite of recent NSD-trained open-source visual decoding models (MindEye1, MindEye2, Brain Diffuser, iCNN, Takagi et al.) on NSD-Imagery, and show that the performance of decoding methods on mental images is largely decoupled from performance on vision reconstruction. We further demonstrate that architectural choices significantly impact cross-decoding performance: models employing simple linear decoding architectures and multimodal feature decoding generalize better to mental imagery, while complex architectures tend to overfit visual training data. Our findings indicate that mental imagery datasets are critical for the development of practical applications, and establish NSD-Imagery as a useful resource for better aligning visual decoding methods with this goal.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kneeland_NSD-Imagery_A_Benchmark_Dataset_for_Extending_fMRI_Vision_Decoding_Methods_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kneeland_NSD-Imagery_A_Benchmark_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kneeland_2025_CVPR,
    author    = {Kneeland, Reese and Scotti, Paul S. and St-Yves, Ghislain and Breedlove, Jesse and Kay, Kendrick and Naselaris, Thomas},
    title     = {NSD-Imagery: A Benchmark Dataset for Extending fMRI Vision Decoding Methods to Mental Imagery},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28852-28862}
}

raw_affiliation:
[{"affiliation": "University of Minnesota", "city": "Minneapolis", "country": "USA"}, {"affiliation": "Alljoined", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Princeton Neuroscience Institute", "city": "Princeton", "country": "USA"}, {"affiliation": "Formerly Stability AI/Medical AI Research Center (MedARC)", "city": "Unknown", "country": "Unknown"}]

affiliations:
["University of Minnesota", "Alljoined", "Princeton Neuroscience Institute", "Formerly Stability AI/Medical AI Research Center (MedARC)"]


title: VLsI: Verbalized Layers-to-Interactions from Large to Small Vision Language Models
authors: Byung-Kwan Lee, Ryo Hachiuma, Yu-Chiang Frank Wang, Yong Man Ro, Yueh-Hua Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29545-29557
affiliations: ["NVIDIA", "KAIST", "National Taiwan University"]
cities: ["Santa Clara", "Daejeon", "Taipei"]
countries: ["USA", "South Korea", "Taiwan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_VLsI_Verbalized_Layers-to-Interactions_from_Large_to_Small_Vision_Language_Models_CVPR_2025_paper.html

abstract:
The recent surge in high-quality visual instruction tuning samples from closed-source vision-language models (VLMs) such as GPT-4V has accelerated the release of open-source VLMs across various model sizes. However, scaling VLMs to improve performance using larger models brings significant computational challenges, especially for deployment on resource-constrained devices like mobile platforms and robots. To address this, we propose VLsI: Verbalized Layers-to-Interactions, a new VLM family in 2B and 7B model sizes, which prioritizes efficiency without compromising accuracy. VLsI leverages a unique, layer-wise distillation process, introducing intermediate "verbalizers" that map features from each layer to natural language space, allowing smaller VLMs to flexibly align with the reasoning processes of larger VLMs. This approach mitigates the training instability often encountered in output imitation and goes beyond typical final-layer tuning by aligning the small VLMs' layer-wise progression with that of the large ones. We validate VLsI across ten challenging vision-language benchmarks, achieving notable performance gains (11.0% for 2B and 17.4% for 7B) over GPT-4V without the need for model scaling, merging, or architectural changes.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_VLsI_Verbalized_Layers-to-Interactions_from_Large_to_Small_Vision_Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_VLsI_Verbalized_Layers-to-Interactions_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Byung-Kwan and Hachiuma, Ryo and Wang, Yu-Chiang Frank and Ro, Yong Man and Wu, Yueh-Hua},
    title     = {VLsI: Verbalized Layers-to-Interactions from Large to Small Vision Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29545-29557}
}

raw_affiliation:
[{"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "National Taiwan University", "city": "Taipei", "country": "Taiwan"}]


title: O-TPT: Orthogonality Constraints for Calibrating Test-time Prompt Tuning in Vision-Language Models
authors: Ashshak Sharifdeen, Muhammad Akhtar Munir, Sanoojan Baliah, Salman Khan, Muhammad Haris Khan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19942-19951
affiliations: ["Mohamed bin Zayed University of AI", "University of Colombo", "Australian National University"]
cities: ["Abu Dhabi", "Colombo", "Canberra"]
countries: ["United Arab Emirates", "Sri Lanka", "Australia"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sharifdeen_O-TPT_Orthogonality_Constraints_for_Calibrating_Test-time_Prompt_Tuning_in_Vision-Language_CVPR_2025_paper.html

abstract:
Test-time prompt tuning for vision-language models (VLMs) is getting attention because of their ability to learn with unlabeled data without fine-tuning. Although test-time prompt tuning methods for VLMs can boost accuracy, the resulting models tend to demonstrate poor calibration, which casts doubts on the reliability and trustworthiness of these models. Notably, more attention needs to be devoted to calibrating the test-time prompt tuning in vision-language models. To this end, we propose a new approach, called O-TPT that introduces orthogonality constraints on the textual features corresponding to the learnable prompts for calibrating test-time prompt tuning in VLMs. Towards introducing orthogonality constraints, we make the following contributions. First, we uncover new insights behind the suboptimal calibration performance of existing methods relying on textual feature dispersion. Second, we show that imposing a simple orthogonalization of textual features is a more effective approach towards obtaining textual dispersion. We conduct extensive experiments on various datasets with different backbones and baselines. The results indicate that our method consistently outperforms the prior state of the art in significantly reducing the overall average calibration error. Also, our method surpasses the zero-shot calibration performance on fine-grained classification tasks. Our code is available at https://github.com/ashshaksharifdeen/O-TPT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sharifdeen_O-TPT_Orthogonality_Constraints_for_Calibrating_Test-time_Prompt_Tuning_in_Vision-Language_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sharifdeen_O-TPT_Orthogonality_Constraints_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sharifdeen_2025_CVPR,
    author    = {Sharifdeen, Ashshak and Munir, Muhammad Akhtar and Baliah, Sanoojan and Khan, Salman and Khan, Muhammad Haris},
    title     = {O-TPT: Orthogonality Constraints for Calibrating Test-time Prompt Tuning in Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19942-19951}
}

raw_affiliation:
[{"affiliation": "Mohamed bin Zayed University of AI", "city": "Abu Dhabi", "country": "United Arab Emirates"}, {"affiliation": "University of Colombo", "city": "Colombo", "country": "Sri Lanka"}, {"affiliation": "Australian National University", "city": "Canberra", "country": "Australia"}]


title: Just Dance with pi! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24265-24274
cities: ["Sophia Antipolis", "Nice", "Tokyo", "Brussels", "Eindhoven"]
countries: ["France", "France", "Japan", "Belgium", "Netherlands"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Majhi_Just_Dance_with_pi_A_Poly-modal_Inductor_for_Weakly-supervised_Video_CVPR_2025_paper.html

authors:
Snehashis Majhi, Giacomo D'Amicantonio, Antitza Dantcheva, Quan Kong, Lorenzo Garattoni, Gianpiero Francesca, Egor Bondarev, Francois Bremond

abstract:
Weakly-supervised methods for video anomaly detection (VAD) are conventionally based merely on RGB spatio-temporal features, which continues to limit their reliability in real-world scenarios. This is due to the fact that RGB-features are not sufficiently distinctive in setting apart categories such as shoplifting from visually similar events. Therefore, towards robust complex real-world VAD, it is essential to augment RGB spatio-temporal features by additional modalities. Motivated by this, we introduce the Poly-modal Induced framework for VAD: PI-VAD (or \pi-VAD), a novel approach that augments RGB representations by five additional modalities. Specifically, the modalities include sensitivity to fine-grained motion (Pose), three dimensional scene and entity representation (Depth), surrounding objects (Panoptic masks), global motion (optical flow), as well as language cues (VLM). Each modality represents an axis of a polygon, streamlined to add salient cues to RGB. \pi-VAD includes two plug-in modules, namely Pseudo-modality Generation module and Cross Modal Induction module, which generate modality-specific prototypical representation and, thereby, induce multi-modal information into RGB cues. These modules operate by performing anomaly-aware auxiliary tasks and necessitate five modality backbones -- only during training. Notably, \pi-VAD achieves state-of-the-art accuracy on three prominent VAD datasets encompassing real-world scenarios, without requiring the computational overhead of five modality backbones at inference.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Majhi_Just_Dance_with_pi_A_Poly-modal_Inductor_for_Weakly-supervised_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Majhi_Just_Dance_with_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Majhi_2025_CVPR,
    author    = {Majhi, Snehashis and D'Amicantonio, Giacomo and Dantcheva, Antitza and Kong, Quan and Garattoni, Lorenzo and Francesca, Gianpiero and Bondarev, Egor and Bremond, Francois},
    title     = {Just Dance with pi! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24265-24274}
}

raw_affiliation:
[{"affiliation": "INRIA", "city": "Sophia Antipolis", "country": "France"}, {"affiliation": "C\u02c6ote d\u2019Azur University", "city": "Nice", "country": "France"}, {"affiliation": "Woven by Toyota", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Toyota Motor Europe", "city": "Brussels", "country": "Belgium"}, {"affiliation": "Eindhoven University of Technology", "city": "Eindhoven", "country": "Netherlands"}]

affiliations:
["INRIA", "C\u02c6ote d\u2019Azur University", "Woven by Toyota", "Toyota Motor Europe", "Eindhoven University of Technology"]


title: Flash3D: Super-scaling Point Transformers through Joint Hardware-Geometry Locality
authors: Liyan Chen, Gregory P. Meyer, Zaiwei Zhang, Eric M. Wolff, Paul Vernaza
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6595-6604
affiliations: ["The University of Texas at Austin", "Cruise LLC"]
cities: ["Austin", "San Francisco"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Flash3D_Super-scaling_Point_Transformers_through_Joint_Hardware-Geometry_Locality_CVPR_2025_paper.html

abstract:
Recent efforts recognize the power of scale in 3D learning (e.g. PTv3) and attention mechanisms (e.g. FlashAttention).However, current point cloud backbones fail to holistically unify geometric locality, attention mechanisms, and GPU architectures in one view.In this paper, we introduce Flash3D Transformer, which aligns geometric locality and GPU tiling through a principled locality mechanism based on Perfect Spatial Hashing (PSH).The common alignment with GPU tiling naturally fuses our PSH locality mechanism with FlashAttention at negligible extra cost.This mechanism affords flexible design choices throughout the backbone that result in superior downstream task results.Flash3D outperforms state-of-the-art PTv3 results on benchmark datasets, delivering a 2.25x speed increase and 2.4x memory efficiency boost. This efficiency enables scaling to wider attention scopes and larger models without additional overhead.Such scaling allows Flash3D to achieve even higher task accuracies than PTv3 under the same compute budget.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Flash3D_Super-scaling_Point_Transformers_through_Joint_Hardware-Geometry_Locality_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Flash3D_Super-scaling_Point_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Liyan and Meyer, Gregory P. and Zhang, Zaiwei and Wolff, Eric M. and Vernaza, Paul},
    title     = {Flash3D: Super-scaling Point Transformers through Joint Hardware-Geometry Locality},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6595-6604}
}

raw_affiliation:
[{"affiliation": "The University of Texas at Austin", "city": "Austin", "country": "USA"}, {"affiliation": "Cruise LLC", "city": "San Francisco", "country": "USA"}]


title: Analyzing the Synthetic-to-Real Domain Gap in 3D Hand Pose Estimation
authors: Zhuoran Zhao, Linlin Yang, Pengzhan Sun, Pan Hui, Angela Yao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12255-12265
cities: ["Guangzhou", "Beijing", "Singapore"]
countries: ["China", "China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Analyzing_the_Synthetic-to-Real_Domain_Gap_in_3D_Hand_Pose_Estimation_CVPR_2025_paper.html

abstract:
Recent synthetic 3D human datasets for the face, body, and hands have pushed the limits on photorealism. Face recognition and body pose estimation have achieved state-of-the-art performance using synthetic training data alone, but for the hand, there is still a large synthetic-to-real gap. This paper presents the first systematic study of the synthetic-to-real gap of 3D hand pose estimation. We analyze the gap and identify key components such as the forearm, image frequency statistics, hand pose, and object occlusions. To facilitate our analysis, we propose a data synthesis pipeline to synthesize high-quality data. We demonstrate that synthetic hand data can achieve the same level of accuracy as real data when integrating our identified components, paving the path to use synthetic data alone for hand pose estimation. Code and data are available at: https://github.com/delaprada/HandSynthesis.git.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Analyzing_the_Synthetic-to-Real_Domain_Gap_in_3D_Hand_Pose_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_Analyzing_the_Synthetic-to-Real_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Zhuoran and Yang, Linlin and Sun, Pengzhan and Hui, Pan and Yao, Angela},
    title     = {Analyzing the Synthetic-to-Real Domain Gap in 3D Hand Pose Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12255-12265}
}

raw_affiliation:
[{"affiliation": "The Hong Kong University of Science and Technology (Guangzhou)", "city": "Guangzhou", "country": "China"}, {"affiliation": "Communication University of China", "city": "Beijing", "country": "China"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}]

affiliations:
["The Hong Kong University of Science and Technology (Guangzhou)", "Communication University of China", "National University of Singapore"]


title: Feature4X: Bridging Any Monocular Video to 4D Agentic AI with Versatile Gaussian Feature Fields
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14179-14190
affiliations: ["UCLA", "MIT", "Stanford", "UT Austin", "DEVCOM ARL"]
cities: ["Los Angeles", "Cambridge", "Stanford", "Austin", "Adelphi"]
countries: ["USA", "USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Feature4X_Bridging_Any_Monocular_Video_to_4D_Agentic_AI_with_CVPR_2025_paper.html

authors:
Shijie Zhou, Hui Ren, Yijia Weng, Shuwang Zhang, Zhen Wang, Dejia Xu, Zhiwen Fan, Suya You, Zhangyang Wang, Leonidas Guibas, Achuta Kadambi

abstract:
Recent advancements in 2D and multimodal models have achieved remarkable success by leveraging large-scale training on extensive datasets. However, extending these achievements to enable free-form interactions and high-level semantic operations with complex 3D/4D scenes remains challenging. This difficulty stems from the limited availability of large-scale, annotated 3D/4D or multi-view datasets, which are crucial for generalizable vision and language tasks such as open-vocabulary and prompt-based segmentation, language-guided editing, and visual question answering (VQA). In this paper, we introduce Feature4X, a universal framework designed to extend any functionality from 2D vision foundation model into the 4D realm, using only monocular video input, which is widely available from user-generated content. The "X" in Feature4X represents its versatility, enabling any task through adaptable, model-conditioned 4D feature field distillation. At the core of our framework is a dynamic optimization strategy that unifies multiple model capabilities into a single representation. Additionally, to the best of our knowledge, Feature4X is the first method to distill and lift the features of video foundation models (e.g. SAM2, InternVideo2) into an explicit 4D feature field using Gaussian Splatting. Our experiments showcase novel view segment anything, geometric and appearance scene editing, and free-form VQA across all time steps, empowered by LLMs in feedback loops. These advancements broaden the scope of agentic AI applications by providing a foundation for scalable, contextually and spatiotemporally aware systems capable of immersive dynamic 4D scene interaction.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Feature4X_Bridging_Any_Monocular_Video_to_4D_Agentic_AI_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Feature4X_Bridging_Any_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Shijie and Ren, Hui and Weng, Yijia and Zhang, Shuwang and Wang, Zhen and Xu, Dejia and Fan, Zhiwen and You, Suya and Wang, Zhangyang and Guibas, Leonidas and Kadambi, Achuta},
    title     = {Feature4X: Bridging Any Monocular Video to 4D Agentic AI with Versatile Gaussian Feature Fields},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14179-14190}
}

raw_affiliation:
[{"affiliation": "UCLA", "city": "Los Angeles", "country": "USA"}, {"affiliation": "MIT", "city": "Cambridge", "country": "USA"}, {"affiliation": "Stanford", "city": "Stanford", "country": "USA"}, {"affiliation": "UT Austin", "city": "Austin", "country": "USA"}, {"affiliation": "DEVCOM ARL", "city": "Adelphi", "country": "USA"}]


title: Comprehensive Relighting: Generalizable and Consistent Monocular Human Relighting and Harmonization
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 380-390
affiliations: ["University of Southern California", "Adobe Research", "Runway"]
cities: ["Los Angeles", "San Jose", "San Francisco"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Comprehensive_Relighting_Generalizable_and_Consistent_Monocular_Human_Relighting_and_Harmonization_CVPR_2025_paper.html

authors:
Junying Wang, Jingyuan Liu, Xin Sun, Krishna Kumar Singh, Zhixin Shu, He Zhang, Jimei Yang, Nanxuan Zhao, Tuanfeng Y. Wang, Simon S. Chen, Ulrich Neumann, Jae Shin Yoon

abstract:
This paper introduces Comprehensive Relighting, the first all-in-one approach that can both control and harmonize the lighting from an image or video of humans with arbitrary body parts from any scene. Building such a generalizable model is extremely challenging due to the lack of dataset, restricting existing image-based relighting models to a specific scenario (e.g., face or static human). To address this challenge, we repurpose a pre-trained diffusion model as a general image prior and jointly model the human relighting and background harmonization in the coarse-to-fine framework. To further enhance the temporal coherence of the relighting, we introduce an unsupervised temporal lighting model that learns the lighting cycle consistency from many real-world videos without any ground truth. In inference time, our temporal lighting module is combined with the diffusion models through the spatio-temporal feature blending algorithms without extra training; and we apply a new guided refinement as a post-processing to preserve the high-frequency details from the input image. In the experiments, Comprehensive Relighting shows a strong generalizability and lighting temporal coherence, outperforming existing image-based human relighting and harmonization methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Comprehensive_Relighting_Generalizable_and_Consistent_Monocular_Human_Relighting_and_Harmonization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Comprehensive_Relighting_Generalizable_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Junying and Liu, Jingyuan and Sun, Xin and Singh, Krishna Kumar and Shu, Zhixin and Zhang, He and Yang, Jimei and Zhao, Nanxuan and Wang, Tuanfeng Y. and Chen, Simon S. and Neumann, Ulrich and Yoon, Jae Shin},
    title     = {Comprehensive Relighting: Generalizable and Consistent Monocular Human Relighting and Harmonization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {380-390}
}

raw_affiliation:
[{"affiliation": "University of Southern California", "city": "Los Angeles", "country": "USA"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}, {"affiliation": "Runway", "city": "San Francisco", "country": "USA"}]


title: Hyperspectral Pansharpening via Diffusion Models with Iteratively Zero-Shot Guidance
authors: Jin-Liang Xiao, Ting-Zhu Huang, Liang-Jian Deng, Guang Lin, Zihan Cao, Chao Li, Qibin Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12669-12678
cities: ["Chengdu", "Tokyo", "Tokyo"]
countries: ["China", "Japan", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xiao_Hyperspectral_Pansharpening_via_Diffusion_Models_with_Iteratively_Zero-Shot_Guidance_CVPR_2025_paper.html

abstract:
Hyperspectral pansharpening refers to fusing a panchromatic image (PAN) and a low-resolution hyperspectral image (LR-HSI) to obtain a high-resolution hyperspectral image (HR-HSI). Recently, guiding pre-trained diffusion models (DMs) has demonstrated significant potential in this area, leveraging their powerful representational abilities while avoiding complex training processes. However, these DMs are often trained on RGB images, not well-suited for pansharpening tasks, limited in adapting to the hyperspectral images. In this work, we propose a novel guided diffusion scheme with zero-shot guidance and neural spatial-spectral decomposition (NSSD) to iteratively generate the RGB detail image and map the RGB detail image to target HR-HSI. Specifically, zero-shot guidance employs an auxiliary neural network that trained only with a PAN and LR-HSI to guide pre-trained DMs in generating the RGB detail image, informed by specific prior knowledge. Then, NSSD establishes a spectral mapping from the generated RGB detail image to the final HR-HSI. Extensive experiments are conducted on Pavia, Washington DC, Chukusei, and FR1 datasets to demonstrate that the proposed method significantly enhances the performance of DMs for hyperspectral pansharpening tasks, outperforming existing methods across multiple metrics and achieving improvements in visualization results. The code is available at \href https://github.com/Jin-liangXiao/DM-zs  https://github.com/Jin-liangXiao/DM-zs .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiao_Hyperspectral_Pansharpening_via_Diffusion_Models_with_Iteratively_Zero-Shot_Guidance_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiao_Hyperspectral_Pansharpening_via_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xiao_2025_CVPR,
    author    = {Xiao, Jin-Liang and Huang, Ting-Zhu and Deng, Liang-Jian and Lin, Guang and Cao, Zihan and Li, Chao and Zhao, Qibin},
    title     = {Hyperspectral Pansharpening via Diffusion Models with Iteratively Zero-Shot Guidance},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12669-12678}
}

raw_affiliation:
[{"affiliation": "University of Electronic Science and Technology of China", "city": "Chengdu", "country": "China"}, {"affiliation": "Tokyo University of Agriculture and Technology", "city": "Tokyo", "country": "Japan"}, {"affiliation": "RIKEN AIP", "city": "Tokyo", "country": "Japan"}]

affiliations:
["University of Electronic Science and Technology of China", "Tokyo University of Agriculture and Technology", "RIKEN AIP"]


title: EASEMVC:Efficient Dual Selection Mechanism for Deep Multi-View Clustering
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20716-20726
affiliations: ["National University of Defense Technology", "Intelligent Game and Decision Lab"]
cities: ["Changsha", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xiao_EASEMVCEfficient_Dual_Selection_Mechanism_for_Deep_Multi-View_Clustering_CVPR_2025_paper.html

authors:
Baili Xiao, Zhibin Dong, Ke Liang, Suyuan Liu, Siwei Wang, Tianrui Liu, Xingchen Hu, En Zhu, Xinwang Liu

abstract:
Multi-view clustering represents one of the most established paradigms within the field of unsupervised learning and has witnessed a surge in popularity in recent years. View-pair form contrastive learning allows for consistently representing multiple views by maximizing mutual information between each two views. This approach permits multi-view clustering to discern consistent latent representations across multiple views. However, two significant issues emerge when this approach is considered. i)it is challenging to ascertain which two views are most appropriate for contrastive learning when there are more than three views, particularly without prior knowledge. ii) when all views are included in contrastive learning, multi-view clustering performance is compromised by poor quality views. To tackle these issues, we present a novel Efficient Dual Selection Mechanism for deep Multi-View Clustering framework, termed EASEMVC. Specifically, EASEMVC first constructs a view graph based on the OT distance between the bipartite graph of each view. It then designs a view selection module to realize an efficient view-level selection process through the view topology relations in the view graph structure. Additionally, a cross-view sample graph structure is constructed at the sample level, with the sample topological relations in the cross-view sample graph structure being employed to generate reliable sample learning weights. Based on the view pairs and sample weights selected by the aforementioned method, we employ contrastive learning within the theoretical framework of information theory to obtain consistent representations between views. Extensive experiments on six benchmark datasets have demonstrated that the proposed EASEMVC outperforms the state-of-the-art methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiao_EASEMVCEfficient_Dual_Selection_Mechanism_for_Deep_Multi-View_Clustering_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiao_EASEMVCEfficient_Dual_Selection_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xiao_2025_CVPR,
    author    = {Xiao, Baili and Dong, Zhibin and Liang, Ke and Liu, Suyuan and Wang, Siwei and Liu, Tianrui and Hu, Xingchen and Zhu, En and Liu, Xinwang},
    title     = {EASEMVC:Efficient Dual Selection Mechanism for Deep Multi-View Clustering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20716-20726}
}

raw_affiliation:
[{"affiliation": "National University of Defense Technology", "city": "Changsha", "country": "China"}, {"affiliation": "Intelligent Game and Decision Lab", "city": "Beijing", "country": "China"}]


title: Efficient Motion-Aware Video MLLM
authors: Zijia Zhao, Yuqi Huo, Tongtian Yue, Longteng Guo, Haoyu Lu, Bingning Wang, Weipeng Chen, Jing Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24159-24168
cities: ["Beijing", "Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Efficient_Motion-Aware_Video_MLLM_CVPR_2025_paper.html

abstract:
Most current video MLLMs rely on uniform frame sampling and image-level encoders, resulting in inefficient data processing and limited motion awareness. To address these challenges, we introduce EMA, an Efficient Motion-Aware video MLLM that utilizes compressed video structures as inputs. We propose a motion-aware GOP (Group of Pictures) encoder that fuses spatial and motion information within a GOP unit in the compressed video stream, generating compact, informative visual tokens. By integrating fewer but denser RGB frames with more but sparser motion vectors in this native slow-fast input architecture, our approach reduces redundancy and enhances motion representation. Additionally, we introduce MotionBench, a benchmark for evaluating motion understanding across four motion types: linear, curved, rotational, and contact-based. Experimental results show that EMA achieves state-of-the-art performance on both MotionBench and popular video question answering benchmarks, while reducing inference costs. Moreover, EMA demonstrates strong scalability, as evidenced by its competitive performance on long video understanding benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Efficient_Motion-Aware_Video_MLLM_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_Efficient_Motion-Aware_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Zijia and Huo, Yuqi and Yue, Tongtian and Guo, Longteng and Lu, Haoyu and Wang, Bingning and Chen, Weipeng and Liu, Jing},
    title     = {Efficient Motion-Aware Video MLLM},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24159-24168}
}

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

affiliations:
["Institute of Automation, Chinese Academy of Sciences", "School of Artificial Intelligence, University of Chinese Academy of Sciences", "Baichuan Inc.", "Renmin University of China"]


title: DSPNet: Dual-vision Scene Perception for Robust 3D Question Answering
authors: Jingzhou Luo, Yang Liu, Weixing Chen, Zhen Li, Yaowei Wang, Guanbin Li, Liang Lin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14169-14178
cities: ["Guangzhou", "Shenzhen", "Shenzhen", "Shenzhen", "Guangzhou"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Luo_DSPNet_Dual-vision_Scene_Perception_for_Robust_3D_Question_Answering_CVPR_2025_paper.html

abstract:
3D Question Answering (3D QA) requires the model to comprehensively understand its situated 3D scene described by the text, then reason about its surrounding environment and answer a question under that situation. However, existing methods usually rely on global scene perception from pure 3D point clouds and overlook the importance of rich local texture details from multi-view images. Moreover, due to the inherent noise in camera poses and complex occlusions, there exists significant feature degradation and reduced feature robustness problems when aligning 3D point cloud with multi-view images. In this paper, we propose a  Dual-vision Scene Perception Network (DSPNet), to comprehensively integrate multi-view and point cloud features to improve robustness in 3D QA. Our Text-guided Multi-view Fusion (TGMF) module prioritizes image views that closely match the semantic content of the text.  To adaptively fuse back-projected multi-view images with point cloud features, we design the Adaptive Dual-vision Perception (ADVP) module, enhancing 3D scene comprehension. Additionally, our Multimodal Context-guided Reasoning (MCGR) module facilitates robust reasoning by integrating contextual information across visual and linguistic modalities. Experimental results on SQA3D and ScanQA datasets demonstrate the superiority of our DSPNet. Codes will be available at https://github.com/LZ-CH/DSPNet.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Luo_DSPNet_Dual-vision_Scene_Perception_for_Robust_3D_Question_Answering_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Luo_DSPNet_Dual-vision_Scene_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Luo_2025_CVPR,
    author    = {Luo, Jingzhou and Liu, Yang and Chen, Weixing and Li, Zhen and Wang, Yaowei and Li, Guanbin and Lin, Liang},
    title     = {DSPNet: Dual-vision Scene Perception for Robust 3D Question Answering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14169-14178}
}

raw_affiliation:
[{"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong, Shenzhen", "city": "Shenzhen", "country": "China"}, {"affiliation": "Harbin Institute of Technology, Shenzhen", "city": "Shenzhen", "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", "The Chinese University of Hong Kong, Shenzhen", "Harbin Institute of Technology, Shenzhen", "Peng Cheng Laboratory", "Guangdong Key Laboratory of Big Data Analysis and Processing"]


title: Zero-Shot 4D Lidar Panoptic Segmentation
authors: Yushan Zhang, Aljoša Ošep, Laura Leal-Taixé, Tim Meinhardt
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24506-24517
affiliations: ["NVIDIA", "Link\u00a8oping University"]
cities: ["Santa Clara", "Linkoping"]
countries: ["USA", "Sweden"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Zero-Shot_4D_Lidar_Panoptic_Segmentation_CVPR_2025_paper.html

abstract:
Zero-shot 4D segmentation and recognition of arbitrary objects in Lidar is crucial for embodied navigation, with applications ranging from streaming perception to semantic mapping and localization. However, the primary challenge in advancing research and developing generalized, versatile methods for spatio-temporal scene understanding in Lidar lies in the scarcity of datasets that provide the necessary diversity and scale of annotations. To overcome these challenges, we propose SAL-4D (Segment Anything in Lidar--4D), a method that utilizes multi-modal robotic sensor setups as a bridge to distill recent developments in Video Object Segmentation (VOS) in conjunction with off-the-shelf Vision-Language foundation models to Lidar.  We utilize VOS models to pseudo-label tracklets in short video sequences, annotate these tracklets with sequence-level CLIP tokens, and lift them to the 4D Lidar space using calibrated multi-modal sensory setups to distill them to our SAL-4D model. Due to temporal consistent predictions, we outperform prior art in 3D Zero-Shot Lidar Panoptic Segmentation (LPS) over 5 PQ, and unlock Zero-Shot 4D-LPS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Zero-Shot_4D_Lidar_Panoptic_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Zero-Shot_4D_Lidar_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Yushan and O\v{s}ep, Aljo\v{s}a and Leal-Taix\'e, Laura and Meinhardt, Tim},
    title     = {Zero-Shot 4D Lidar Panoptic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24506-24517}
}

raw_affiliation:
[{"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "Link\u00a8oping University", "city": "Linkoping", "country": "Sweden"}]


title: MAtCha Gaussians: Atlas of Charts for High-Quality Geometry and Photorealism From Sparse Views
authors: Antoine Guedon, Tomoki Ichikawa, Kohei Yamashita, Ko Nishino
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6001-6011
cities: ["Paris", "Kyoto"]
countries: ["France", "Japan"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guedon_MAtCha_Gaussians_Atlas_of_Charts_for_High-Quality_Geometry_and_Photorealism_CVPR_2025_paper.html

abstract:
We present a novel appearance model that simultaneously realizes explicit high-quality 3D surface mesh recovery and photorealistic novel view synthesis from sparse view samples. Our key idea is to model the underlying scene geometry Mesh as an Atlas of Charts which we render with 2D Gaussian surfels (MAtCha Gaussians). MAtCha distills high-frequency scene surface details from an off-the-shelf monocular depth estimator and refines it through Gaussian surfel rendering. The Gaussian surfels are attached to the charts on the fly, satisfying photorealism of neural volumetric rendering and crisp geometry of a mesh model, i.e., two seemingly contradicting goals in a single model. At the core of MAtCha lies a novel neural deformation model and a structure loss that preserve the fine surface details distilled from learned monocular depths while addressing their fundamental scale ambiguities. Results of extensive experimental validation demonstrate MAtCha's state-of-the-art quality of surface reconstruction and photorealism on-par with top contenders but with dramatic reduction in the number of input views and computational time. We believe MAtCha will serve as a foundational tool for any visual application in vision, graphics, and robotics that require explicit geometry in addition to photorealism.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guedon_MAtCha_Gaussians_Atlas_of_Charts_for_High-Quality_Geometry_and_Photorealism_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guedon_MAtCha_Gaussians_Atlas_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Guedon_2025_CVPR,
    author    = {Guedon, Antoine and Ichikawa, Tomoki and Yamashita, Kohei and Nishino, Ko},
    title     = {MAtCha Gaussians: Atlas of Charts for High-Quality Geometry and Photorealism From Sparse Views},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6001-6011}
}

raw_affiliation:
[{"affiliation": "LIGM, Ecole des Ponts, Univ Gustave Eiffel, CNRS, France", "city": "Paris", "country": "France"}, {"affiliation": "Graduate School of Informatics, Kyoto University, Japan", "city": "Kyoto", "country": "Japan"}]

affiliations:
["LIGM, Ecole des Ponts, Univ Gustave Eiffel, CNRS, France", "Graduate School of Informatics, Kyoto University, Japan"]


title: Extreme Rotation Estimation in the Wild
authors: Hana Bezalel, Dotan Ankri, Ruojin Cai, Hadar Averbach-Elor
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1061-1070
affiliations: ["Tel Aviv University", "Cornell University"]
cities: ["Tel Aviv", "Ithaca"]
countries: ["Israel", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bezalel_Extreme_Rotation_Estimation_in_the_Wild_CVPR_2025_paper.html

abstract:
We present a technique and benchmark dataset for estimating the relative 3D orientation between a pair of Internet images captured in an extreme setting, where the images have limited or non-overlapping field of views. Prior work targeting extreme rotation estimation assume constrained 3D environments and emulate perspective images by cropping regions from panoramic views. However, real images captured in the wild are highly diverse, exhibiting variation in both appearance and camera intrinsics. In this work, we propose a Transformer-based method for estimating relative rotations in extreme real-world settings, and contribute the ExtremeLandmarkPairs dataset, assembled from scene-level Internet photo collections. Our evaluation demonstrates that our approach succeeds in estimating the relative rotations in a wide variety of extreme-view Internet image pairs, outperforming various baselines, including dedicated rotation estimation techniques and contemporary 3D reconstruction methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bezalel_Extreme_Rotation_Estimation_in_the_Wild_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bezalel_Extreme_Rotation_Estimation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bezalel_2025_CVPR,
    author    = {Bezalel, Hana and Ankri, Dotan and Cai, Ruojin and Averbach-Elor, Hadar},
    title     = {Extreme Rotation Estimation in the Wild},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1061-1070}
}

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


title: ADU: Adaptive Detection of Unknown Categories in Black-Box Domain Adaptation
authors: Yushan Lai, Guowen Li, Haoyuan Liang, Juepeng Zheng, Zhiyu Ye
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30588-30598
affiliations: ["School of Arti\ufb01cial Intelligence, Sun Y at-Sen University"]
cities: ["Guangzhou"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lai_ADU_Adaptive_Detection_of_Unknown_Categories_in_Black-Box_Domain_Adaptation_CVPR_2025_paper.html

abstract:
Black-box Domain Adaptation (BDA) utilizes a black-box predictor of the source domain to label target domain data, addressing privacy concerns in Unsupervised Domain Adaptation (UDA).  However, BDA assumes identical label sets across domains, which is unrealistic. To overcome this limitation, we propose a study on BDA with unknown classes in the target domain. It uses a black-box predictor to label target data and identify "unknown" categories, without requiring access to source domain data or predictor parameters, thus addressing both data privacy and category shift issues in traditional UDA. Existing methods face two main challenges: (i) Noisy pseudo-labels in knowledge distillation (KD) accumulate prediction errors, and (ii) relying on a preset threshold fails to adapt to varying category shifts. To address these, we propose ADU, a framework that allows the target domain to autonomously learn pseudo-labels guided by quality and use an adaptive threshold to identify "unknown" categories. Specifically, ADU consists of Selective Amplification Knowledge Distillation (SAKD) and Entopy-Driven Label Differentiation (EDLD). SAKD improves KD by focusing on high-quality pseudo-labels, mitigating the impact of noisy labels. EDLD categorizes pseudo-labels by quality and applies tailored training strategies to distinguish "unknown" categories, improving detection accuracy and adaptability. Extensive experiments show that ADU achieves state-of-the-art results, outperforming the best existing method by 3.1% on VisDA in the OPBDA scenario.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lai_ADU_Adaptive_Detection_of_Unknown_Categories_in_Black-Box_Domain_Adaptation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lai_ADU_Adaptive_Detection_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lai_2025_CVPR,
    author    = {Lai, Yushan and Li, Guowen and Liang, Haoyuan and Zheng, Juepeng and Ye, Zhiyu},
    title     = {ADU: Adaptive Detection of Unknown Categories in Black-Box Domain Adaptation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30588-30598}
}

raw_affiliation:
[{"affiliation": "School of Arti\ufb01cial Intelligence, Sun Y at-Sen University", "city": "Guangzhou", "country": "China"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26212-26221
affiliations: ["University of Science and Technology of China", "Imperial College London", "iFLYTEK"]
cities: ["Hefei", "London", "Hefei"]
countries: ["China", "UK", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_EmotiveTalk_Expressive_Talking_Head_Generation_through_Audio_Information_Decoupling_and_CVPR_2025_paper.html

title:
EmotiveTalk: Expressive Talking Head Generation through Audio Information Decoupling and Emotional Video Diffusion

authors:
Haotian Wang, Yuzhe Weng, Yueyan Li, Zilu Guo, Jun Du, Shutong Niu, Jiefeng Ma, Shan He, Xiaoyan Wu, Qiming Hu, Bing Yin, Cong Liu, Qingfeng Liu

abstract:
Diffusion models have revolutionized the field of talking head generation, yet still face challenges in expressiveness, controllability, and stability in long-time generation. In this research, we propose an EmotiveTalk framework to address these issues. Firstly, to realize better control over the generation of lip movement and facial expression, a Vision-guided Audio Information Decoupling (V-AID) approach is designed to generate audio-based decoupled representations aligned with lip movements and expression. Specifically, to achieve alignment between audio and facial expression representation spaces, we present a Diffusion-based Co-speech Temporal Expansion (Di-CTE) module within V-AID to generate expression-related representations under multi-source emotion condition constraints. Then we propose a well-designed Emotional Talking Head Diffusion (ETHD) backbone to efficiently generate highly expressive talking head videos, which contains an Expression Decoupling Injection (EDI) module to automatically decouple the expressions from reference portraits while integrating the target expression information, achieving more expressive generation performance. Experimental results show that EmotiveTalk can generate expressive talking head videos, ensuring the promised controllability of emotions and metric stability during long-time generation, yielding state-of-the-art performance compared to existing methods. The main page of our paper can be found in https://emotivetalk.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_EmotiveTalk_Expressive_Talking_Head_Generation_through_Audio_Information_Decoupling_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_EmotiveTalk_Expressive_Talking_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Haotian and Weng, Yuzhe and Li, Yueyan and Guo, Zilu and Du, Jun and Niu, Shutong and Ma, Jiefeng and He, Shan and Wu, Xiaoyan and Hu, Qiming and Yin, Bing and Liu, Cong and Liu, Qingfeng},
    title     = {EmotiveTalk: Expressive Talking Head Generation through Audio Information Decoupling and Emotional Video Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26212-26221}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Imperial College London", "city": "London", "country": "UK"}, {"affiliation": "iFLYTEK", "city": "Hefei", "country": "China"}]


title: Traversing Distortion-Perception Tradeoff using a Single Score-Based Generative Model
authors: Yuhan Wang, Suzhi Bi, Ying-Jun Angela Zhang, Xiaojun Yuan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2377-2386
cities: ["Hong Kong", "Shenzhen", "Chengdu"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Traversing_Distortion-Perception_Tradeoff_using_a_Single_Score-Based_Generative_Model_CVPR_2025_paper.html

abstract:
The distortion-perception (DP) tradeoff reveals a fundamental conflict between distortion metrics (e.g., MSE and PSNR) and perceptual quality. Recent research has increasingly concentrated on evaluating denoising algorithms within the DP framework. However, existing algorithms either prioritize perceptual quality by sacrificing acceptable distortion, or focus on minimizing MSE for faithful restoration. When the goal shifts or noisy measurements vary, adapting to different points on the DP plane needs retraining or even re-designing the model. Inspired by recent advances in solving inverse problems using score-based generative models, we explore the potential of flexibly and optimally traversing DP tradeoffs using a single pre-trained score-based model. Specifically, we introduce a variance-scaled reverse diffusion process and theoretically characterize the marginal distribution. We then prove that the proposed sample process is an optimal solution to the DP tradeoff for conditional Gaussian distribution. Experimental results on two-dimensional and image datasets illustrate that a single score network can effectively and flexibly traverse the DP tradeoff for general denoising problems.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Traversing_Distortion-Perception_Tradeoff_using_a_Single_Score-Based_Generative_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Traversing_Distortion-Perception_Tradeoff_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yuhan and Bi, Suzhi and Zhang, Ying-Jun Angela and Yuan, Xiaojun},
    title     = {Traversing Distortion-Perception Tradeoff using a Single Score-Based Generative Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2377-2386}
}

raw_affiliation:
[{"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "University of Electronic Science and Technology of China", "city": "Chengdu", "country": "China"}]

affiliations:
["The Chinese University of Hong Kong", "Shenzhen University", "University of Electronic Science and Technology of China"]


authors: Jingyi Xu, Siwei Tu, Weidong Yang, Ben Fei, Shuhao Li, Keyi Liu, Yeqi Luo, Lipeng Ma, Lei Bai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10567-10576
cities: ["Shanghai", "Shanghai", "Hong Kong"]
countries: ["China", "China", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_IceDiff_High_Resolution_and_High-Quality_Arctic_Sea_Ice_Forecasting_with_CVPR_2025_paper.html

title:
IceDiff: High Resolution and High-Quality Arctic Sea Ice Forecasting with Generative Diffusion Prior

abstract:
Variation of Arctic sea ice has significant impacts on polar ecosystems, transporting routes, coastal communities, and global climate. Tracing the change of sea ice at a finer scale is paramount for both operational applications and scientific studies. Recent pan-Arctic sea ice forecasting methods that leverage advances in artificial intelligence have made promising progress over numerical models. However, forecasting sea ice at higher resolutions is still under-explored. To bridge the gap, we propose a two-module cooperative deep learning framework, IceDiff, to forecast sea ice concentration at finer scales. IceDiff first leverages a vision transformer to generate coarse yet superior forecasting results over previous methods at a regular 25km grid. This high-quality sea ice forecasting can be utilized as reliable guidance for the next module. Subsequently, an unconditional diffusion model pre-trained on low-resolution sea ice concentration maps is utilized for sampling down-scaled sea ice forecasting via a zero-shot guided sampling strategy and a patch-based method. For the first time, IceDiff demonstrates sea ice forecasting with a 6.25km resolution. IceDiff extends the boundary of existing sea ice forecasting models and more importantly, its capability to generate high-resolution sea ice concentration data is vital for pragmatic usages and research.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_IceDiff_High_Resolution_and_High-Quality_Arctic_Sea_Ice_Forecasting_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_IceDiff_High_Resolution_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Jingyi and Tu, Siwei and Yang, Weidong and Fei, Ben and Li, Shuhao and Liu, Keyi and Luo, Yeqi and Ma, Lipeng and Bai, Lei},
    title     = {IceDiff: High Resolution and High-Quality Arctic Sea Ice Forecasting with Generative Diffusion Prior},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10567-10576}
}

raw_affiliation:
[{"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["Fudan University", "Shanghai Artificial Intelligence Laboratory", "Chinese University of Hong Kong"]


title: DTOS: Dynamic Time Object Sensing with Large Multimodal Model
authors: Jirui Tian, Jinrong Zhang, Shenglan Liu, Luhao Xu, Zhixiong Huang, Gao Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13810-13820
affiliations: ["Dalian University of T echnology", "T singhua University"]
cities: ["Dalian", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tian_DTOS_Dynamic_Time_Object_Sensing_with_Large_Multimodal_Model_CVPR_2025_paper.html

abstract:
Existing multimodal large language models (MLLMs) face significant challenges in Referring Video Object Segmentation(RVOS). We identify three critical challenges: (C1) insufficient quantitative representation of textual numerical data, (C2) repetitive and degraded response templates for spatiotemporal referencing, and (C3) loss of visual information in video sampling queries lacking textual guidance. To address these, we propose a novel framework, Dynamic Time Object Sensing (DTOS), specifically designed for RVOS. To tackle (C1) and (C2), we introduce specialized tokens to construct multi-answer response templates, enabling regression of event boundaries and target localization. This approach improves the accuracy of numerical regression while mitigating the issue of repetitive degradation. To address (C3), we propose a Text-guided Clip Sampler (TCS) that selects video clips aligned with user instructions, preventing visual information loss and ensuring consistent temporal resolution. TCS is also applicable to Moment Retrieval tasks, with enhanced multimodal input sequences preserving spatial details and maximizing temporal resolution. DTOS demonstrates exceptional capability in flexibly localizing multiple spatiotemporal targets based on user-provided textual instructions. Extensive experiments validate the effectiveness of our approach, with DTOS achieving state-of-the-art performance in J&F scores: an improvement of +4.36 on MeViS, +4.48 on Ref-DAVIS17, and +3.02 on Ref-YT-VOS. Additionally, our TCS demonstrates exceptional performance in Moment Retrieval. The code is available at https://github.com/Maulog/OPEN-DTOS-LMM

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tian_DTOS_Dynamic_Time_Object_Sensing_with_Large_Multimodal_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tian_DTOS_Dynamic_Time_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tian_2025_CVPR,
    author    = {Tian, Jirui and Zhang, Jinrong and Liu, Shenglan and Xu, Luhao and Huang, Zhixiong and Huang, Gao},
    title     = {DTOS: Dynamic Time Object Sensing with Large Multimodal Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13810-13820}
}

raw_affiliation:
[{"affiliation": "Dalian University of T echnology", "city": "Dalian", "country": "China"}, {"affiliation": "T singhua University", "city": "Beijing", "country": "China"}]


title: How to Merge Your Multimodal Models Over Time?
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20479-20491
cities: ["T\u00fcbingen", "Cambridge", "Munich"]
countries: ["Germany", "United Kingdom", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dziadzio_How_to_Merge_Your_Multimodal_Models_Over_Time_CVPR_2025_paper.html

authors:
Sebastian Dziadzio, Vishaal Udandarao, Karsten Roth, Ameya Prabhu, Zeynep Akata, Samuel Albanie, Matthias Bethge

abstract:
Model merging combines expert models---each finetuned from a shared foundation model on diverse tasks and domains---into a single, more capable base model. However, existing model merging approaches assume all experts to be available simultaneously. In reality, new tasks and domains emerge continuously, prompting the need for a dynamic process of integrating these experts over time, which we call temporal model merging. The temporal dimension introduces unique challenges not addressed in prior work: At each task, should expert training start from merged previous experts or the original base model? Should all models be merged at every time step? Which merging techniques are best suited for temporal merging? Should different strategies be used for the training initialization and deployment phases? To tackle these questions, we propose a unified framework called TIME---Temporal Integration of Model Expertise---that defines temporal model merging across three axes: (1) Initialization Phase, (2) Deployment Phase, and (3) Merging Technique. Utilizing TIME, we study temporal model merging across model sizes, tasks, and compute budgets on the large-scale FoMo-in-Flux benchmark for continual multimodal pretraining. Systematic experiments across TIME and FoMo-in-Flux allow us to arrive at several crucial key insights for temporal model merging to better understand current limits and best practices for successful model merging across time.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dziadzio_How_to_Merge_Your_Multimodal_Models_Over_Time_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dziadzio_How_to_Merge_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dziadzio_2025_CVPR,
    author    = {Dziadzio, Sebastian and Udandarao, Vishaal and Roth, Karsten and Prabhu, Ameya and Akata, Zeynep and Albanie, Samuel and Bethge, Matthias},
    title     = {How to Merge Your Multimodal Models Over Time?},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20479-20491}
}

raw_affiliation:
[{"affiliation": "T\u00a8ubingen AI Center, University of T\u00a8ubingen", "city": "T\u00fcbingen", "country": "Germany"}, {"affiliation": "University of Cambridge", "city": "Cambridge", "country": "United Kingdom"}, {"affiliation": "Munich Center for ML, Technical University of Munich", "city": "Munich", "country": "Germany"}]

affiliations:
["T\u00a8ubingen AI Center, University of T\u00a8ubingen", "University of Cambridge", "Munich Center for ML, Technical University of Munich"]


title: Identifying and Mitigating Position Bias of Multi-image Vision-Language Models
authors: Xinyu Tian, Shu Zou, Zhaoyuan Yang, Jing Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10599-10609
affiliations: ["Australian National University", "GE Research"]
cities: ["Canberra", "Niskayuna"]
countries: ["Australia", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tian_Identifying_and_Mitigating_Position_Bias_of_Multi-image_Vision-Language_Models_CVPR_2025_paper.html

abstract:
The evolution of Large Vision-Language Models (LVLMs) has progressed from single-image understanding to multi-image reasoning. Despite this advancement, our findings indicate that LVLMs struggle to robustly utilize information across multiple images, with predictions significantly affected by the alteration of image positions. To further explore this issue, we introduce Position-wise Question Answering (PQA), a meticulously designed task to quantify reasoning capabilities at each position. Our analysis reveals a pronounced position bias in LVLMs: open-source models excel in reasoning with images positioned later but underperform with those in the middle or at the beginning, while proprietary models like GPT-4o show improved comprehension for images at the beginning and end but struggle with those in the middle. Motivated by these insights, we propose SoFt Attention (SoFA), a simple, training-free approach that mitigates this bias by employing linear interpolation between inter-image causal attention and bidirectional counterparts. Experimental results demonstrate that SoFA effectively reduces position bias and significantly enhances the reasoning performance of existing LVLMs.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tian_Identifying_and_Mitigating_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tian_2025_CVPR,
    author    = {Tian, Xinyu and Zou, Shu and Yang, Zhaoyuan and Zhang, Jing},
    title     = {Identifying and Mitigating Position Bias of Multi-image Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10599-10609}
}

raw_affiliation:
[{"affiliation": "Australian National University", "city": "Canberra", "country": "Australia"}, {"affiliation": "GE Research", "city": "Niskayuna", "country": "USA"}]


title: Unsupervised Foundation Model-Agnostic Slide-Level Representation Learning
authors: Tim Lenz, Peter Neidlinger, Marta Ligero, Georg Wölflein, Marko van Treeck, Jakob N. Kather
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30807-30817
cities: ["Dresden", "St Andrews", "Heidelberg", "Dresden"]
countries: ["Germany", "UK", "Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lenz_Unsupervised_Foundation_Model-Agnostic_Slide-Level_Representation_Learning_CVPR_2025_paper.html

abstract:
Representation learning of pathology whole-slide images (WSIs) has primarily relied on weak supervision with Multiple Instance Learning (MIL). This approach leads to slide representations highly tailored to a specific clinical task. Self-supervised learning (SSL) has been successfully applied to train histopathology foundation models (FMs) for patch embedding generation. However, generating patient or slide level embeddings remains challenging. Existing approaches for slide representation learning extend the principles of SSL from patch level learning to entire slides by aligning different augmentations of the slide or by utilizing multimodal data. By integrating tile embeddings from multiple FMs, we propose a new single modality SSL method in feature space that generates useful slide representations. Our contrastive pretraining strategy, called COBRA, employs multiple FMs and an architecture based on Mamba-2. COBRA exceeds performance of state-of-the-art slide encoders on four different public Clinical Protemic Tumor Analysis Consortium (CPTAC) cohorts on average by at least +4.4% AUC, despite only being pretrained on 3048 WSIs from The Cancer Genome Atlas (TCGA). Additionally, COBRA is readily compatible at inference time with previously unseen feature extractors. Code available at https://github.com/KatherLab/COBRA

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lenz_Unsupervised_Foundation_Model-Agnostic_Slide-Level_Representation_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lenz_Unsupervised_Foundation_Model-Agnostic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lenz_2025_CVPR,
    author    = {Lenz, Tim and Neidlinger, Peter and Ligero, Marta and W\"olflein, Georg and van Treeck, Marko and Kather, Jakob N.},
    title     = {Unsupervised Foundation Model-Agnostic Slide-Level Representation Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30807-30817}
}

raw_affiliation:
[{"affiliation": "EKFZ for Digital Health TU Dresden", "city": "Dresden", "country": "Germany"}, {"affiliation": "University of St Andrews", "city": "St Andrews", "country": "UK"}, {"affiliation": "Heidelberg University Hospital", "city": "Heidelberg", "country": "Germany"}, {"affiliation": "University Hospital Dresden", "city": "Dresden", "country": "Germany"}]

affiliations:
["EKFZ for Digital Health TU Dresden", "University of St Andrews", "Heidelberg University Hospital", "University Hospital Dresden"]


title: Exploring CLIP's Dense Knowledge for Weakly Supervised Semantic Segmentation
authors: Zhiwei Yang, Yucong Meng, Kexue Fu, Feilong Tang, Shuo Wang, Zhijian Song
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20223-20232
cities: ["Shanghai", "Shanghai", "Shanghai", "Jinan"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Exploring_CLIPs_Dense_Knowledge_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2025_paper.html

abstract:
Weakly Supervised Semantic Segmentation (WSSS) with image-level labels aims to achieve pixel-level predictions using Class Activation Maps (CAMs). Recently, Contrastive Language-Image Pre-training (CLIP) has been introduced in WSSS. However, recent methods primarily focus on image-text alignment for CAM generation, while CLIP's potential in patch-text alignment remains unexplored. In this work, we propose ExCEL to explore CLIP's dense knowledge via a novel patch-text alignment paradigm for WSSS. Specifically, we propose Text Semantic Enrichment (TSE) and Visual Calibration (VC) modules to improve the dense alignment across both text and vision modalities. To make text embeddings semantically informative, our TSE module applies Large Language Models (LLMs) to build a dataset-wide knowledge base and enriches the text representations with an implicit attribute-hunting process. To mine fine-grained knowledge from visual features, our VC module first proposes Static Visual Calibration (SVC) to propagate fine-grained knowledge in a non-parametric manner. Then Learnable Visual Calibration (LVC) is further proposed to dynamically shift the frozen features towards distributions with diverse semantics. With these enhancements, ExCEL not only retains CLIP's training-free advantages but also significantly outperforms other state-of-the-art methods with much less training cost on PASCAL VOC and MS COCO. Code is available at https://github.com/zwyang6/ExCEL.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Exploring_CLIPs_Dense_Knowledge_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Exploring_CLIPs_Dense_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Zhiwei and Meng, Yucong and Fu, Kexue and Tang, Feilong and Wang, Shuo and Song, Zhijian},
    title     = {Exploring CLIP's Dense Knowledge for Weakly Supervised Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20223-20232}
}

raw_affiliation:
[{"affiliation": "Academy for Engineering and Technology, Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention", "city": "Shanghai", "country": "China"}, {"affiliation": "Digital Medical Research Center, School of Basic Medical Sciences, Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shandong Computer Science Center (National Supercomputer Center in Jinan)", "city": "Jinan", "country": "China"}]

affiliations:
["Academy for Engineering and Technology, Fudan University", "Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention", "Digital Medical Research Center, School of Basic Medical Sciences, Fudan University", "Shandong Computer Science Center (National Supercomputer Center in Jinan)"]


title: UNIALIGN: Scaling Multimodal Alignment within One Unified Model
authors: Bo Zhou, Liulei Li, Yujia Wang, Huafeng Liu, Yazhou Yao, Wenguan Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29644-29655
cities: ["Nanjing", "Hangzhou", "Hangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_UNIALIGN_Scaling_Multimodal_Alignment_within_One_Unified_Model_CVPR_2025_paper.html

abstract:
We present UNIALIGN, a unified model to align an arbitrary number of modalities (\text e.g. , image, text, audio, 3D point cloud, etc.) through one  encoder and a single training phase. Existing solutions typically employ distinct encoders for each modality, resulting in increased parameters as the number of modalities grows. In contrast, UNIALIGN proposes a modality-aware adaptation of the powerful mixture-of-experts (MoE) schema and further integrates it with Low-Rank Adaptation (LoRA),  efficiently scaling the encoder to accommodate inputs in diverse modalities while maintaining a fixed computational overhead.Moreover, prior work often requires separate training for each extended modality. This leads to task-specific models and further hinders the communication between modalities.To address this, we propose a soft modality binding strategy that aligns all modalities using unpaired data samples across datasets. Two additional training objectives are introduced to distill knowledge from well-aligned anchor modalities and prior multimodal models, elevating UNIALIGN into a high performance multimodal foundation model.Experiments on 11 benchmarks across 6 different modalities demonstrate that UNIALIGN could achieve comparable performance to SOTA approaches, while using merely 7.8M trainable parameters and maintaining an identical model with the same weight across all tasks. Our code shall be released.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_UNIALIGN_Scaling_Multimodal_Alignment_within_One_Unified_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_UNIALIGN_Scaling_Multimodal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Bo and Li, Liulei and Wang, Yujia and Liu, Huafeng and Yao, Yazhou and Wang, Wenguan},
    title     = {UNIALIGN: Scaling Multimodal Alignment within One Unified Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29644-29655}
}

raw_affiliation:
[{"affiliation": "Nanjing University of Science and Technology", "city": "Nanjing", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Zhejiang Sci-Tech University", "city": "Hangzhou", "country": "China"}]

affiliations:
["Nanjing University of Science and Technology", "Zhejiang University", "Zhejiang Sci-Tech University"]


title: ShowHowTo: Generating Scene-Conditioned Step-by-Step Visual Instructions
authors: Tomáš Souček, Prajwal Gatti, Michael Wray, Ivan Laptev, Dima Damen, Josef Sivic
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27435-27445
affiliations: ["CIIRC CTU", "University of Bristol", "MBZUAI"]
cities: ["Prague", "Bristol", "Abu Dhabi"]
countries: ["Czech Republic", "United Kingdom", "United Arab Emirates"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Soucek_ShowHowTo_Generating_Scene-Conditioned_Step-by-Step_Visual_Instructions_CVPR_2025_paper.html

abstract:
The goal of this work is to generate step-by-step visual instructions in the form of a sequence of images, given an input image that provides the scene context and the sequence of textual instructions. This is a challenging problem as it requires generating multi-step image sequences to achieve a complex goal while being grounded in a specific environment. Part of the challenge stems from the lack of large-scale training data for this problem. The contribution of this work is thus three-fold. First, we introduce an automatic approach for collecting large step-by-step visual instruction training data from instructional videos. We apply this approach to one million videos and create a large-scale, high-quality dataset of 0.6M sequences of image-text pairs. Second, we develop and train ShowHowTo, a video diffusion model capable of generating step-by-step visual instructions consistent with the provided input image. Third, we evaluate the generated image sequences across three dimensions of accuracy (step, scene, and task) and show our model achieves state-of-the-art results on all of them. Our code, dataset, and trained models are publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Soucek_ShowHowTo_Generating_Scene-Conditioned_Step-by-Step_Visual_Instructions_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Soucek_ShowHowTo_Generating_Scene-Conditioned_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Soucek_2025_CVPR,
    author    = {Sou\v{c}ek, Tom\'a\v{s} and Gatti, Prajwal and Wray, Michael and Laptev, Ivan and Damen, Dima and Sivic, Josef},
    title     = {ShowHowTo: Generating Scene-Conditioned Step-by-Step Visual Instructions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27435-27445}
}

raw_affiliation:
[{"affiliation": "CIIRC CTU", "city": "Prague", "country": "Czech Republic"}, {"affiliation": "University of Bristol", "city": "Bristol", "country": "United Kingdom"}, {"affiliation": "MBZUAI", "city": "Abu Dhabi", "country": "United Arab Emirates"}]


title: Exploration-Driven Generative Interactive Environments
authors: Nedko Savov, Naser Kazemi, Mohammad Mahdi, Danda Pani Paudel, Xi Wang, Luc Van Gool
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27597-27607
affiliations: ["INSAIT, Sofia University \u201dSt. Kliment Ohridski\u201d", "ETH Zurich", "TU Munich"]
cities: ["Sofia", "Zurich", "Munich"]
countries: ["Bulgaria", "Switzerland", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Savov_Exploration-Driven_Generative_Interactive_Environments_CVPR_2025_paper.html

abstract:
Modern world models require costly and time-consuming collection of large video datasets with action demonstrations by people or by environment-specific agents. To simplify training, we focus on using many virtual environments for inexpensive, automatically collected interaction data. Genie, a recent multi-environment world model, demonstrates simulation abilities of many environments with shared behavior. Unfortunately, training their model requires expensive demonstrations. Therefore, we propose a training framework merely using a random agent in virtual environments. While the model trained in this manner exhibits good controls, it is limited by the random exploration possibilities. To address this limitation, we propose AutoExplore Agent - an exploration agent that entirely relies on the uncertainty of the world model, delivering diverse data from which it can learn the best. Our agent is fully independent of environment-specific rewards and thus adapts easily to new environments. With this approach, the pretrained multi-environment model can quickly adapt to new environments achieving video fidelity and controllability improvement. In order to obtain automatically large-scale interaction datasets for pretraining, we group environments with similar behavior and controls. To this end, we annotate the behavior and controls of 974 virtual environments - a dataset that we name RetroAct. For building our model, we first create an open implementation of Genie - GenieRedux and apply enhancements and adaptations in our version GenieRedux-G.  Our code and data are available at https://github.com/insait-institute/GenieRedux

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Savov_Exploration-Driven_Generative_Interactive_Environments_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Savov_Exploration-Driven_Generative_Interactive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Savov_2025_CVPR,
    author    = {Savov, Nedko and Kazemi, Naser and Mahdi, Mohammad and Paudel, Danda Pani and Wang, Xi and Van Gool, Luc},
    title     = {Exploration-Driven Generative Interactive Environments},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27597-27607}
}

raw_affiliation:
[{"affiliation": "INSAIT, Sofia University \u201dSt. Kliment Ohridski\u201d", "city": "Sofia", "country": "Bulgaria"}, {"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "TU Munich", "city": "Munich", "country": "Germany"}]


title: Task-Agnostic Guided Feature Expansion for Class-Incremental Learning
authors: Bowen Zheng, Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10099-10109
cities: ["Nanjing", "Nanjing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zheng_Task-Agnostic_Guided_Feature_Expansion_for_Class-Incremental_Learning_CVPR_2025_paper.html

abstract:
The ability to learn new concepts while preserve the learned knowledge is desirable for learning systems in Class-Incremental Learning (CIL). Recently, feature expansion of the model become a prevalent solution for CIL, where the old features are fixed during the training of the new task while new features are expanded for the new tasks. However, such task-specific features learned from the new task may collide with the old features, leading to misclassification between tasks. Therefore, the expanded model is often encouraged to capture diverse features from the new task, aiming to avoid such collision. However, the existing solution is largely restricted to the samples from the current task, because of the poor accessibility to previous samples. To promote the learning and transferring of diverse features across tasks, we propose a framework called Task-Agnostic Guided Feature Expansion (TagFex). Firstly, it captures task-agnostic features continually with a separate model, providing extra task-agnostic features for subsequent tasks. Secondly, to obtain useful features from the task-agnostic model for the current task, it aggregates the task-agnostic features with the task-specific feature using a merge attention. Then the aggregated feature is transferred back into the task-specific feature for inference, helping the task-specific model capture diverse features. Extensive experiments show the effectiveness and superiority of TagFex on various CIL settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zheng_Task-Agnostic_Guided_Feature_Expansion_for_Class-Incremental_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zheng_Task-Agnostic_Guided_Feature_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zheng_2025_CVPR,
    author    = {Zheng, Bowen and Zhou, Da-Wei and Ye, Han-Jia and Zhan, De-Chuan},
    title     = {Task-Agnostic Guided Feature Expansion for Class-Incremental Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10099-10109}
}

raw_affiliation:
[{"affiliation": "National Key Laboratory for Novel Software Technology, Nanjing University, China", "city": "Nanjing", "country": "China"}, {"affiliation": "School of Arti\ufb01cial Intelligence, Nanjing University, China", "city": "Nanjing", "country": "China"}]

affiliations:
["National Key Laboratory for Novel Software Technology, Nanjing University, China", "School of Arti\ufb01cial Intelligence, Nanjing University, China"]


title: ShowUI: One Vision-Language-Action Model for GUI Visual Agent
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19498-19508
affiliations: ["Show Lab, National University of Singapore", "Microsoft"]
cities: ["Singapore", "Redmond"]
countries: ["Singapore", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_ShowUI_One_Vision-Language-Action_Model_for_GUI_Visual_Agent_CVPR_2025_paper.html

authors:
Kevin Qinghong Lin, Linjie Li, Difei Gao, Zhengyuan Yang, Shiwei Wu, Zechen Bai, Stan Weixian Lei, Lijuan Wang, Mike Zheng Shou

abstract:
Building Graphical User Interface (GUI) assistants holds significant promise for enhancing human workflow productivity. While most agents are language-based, relying on closed-source API with text-rich meta-information (e.g., HTML or accessibility tree), they show limitations in perceiving UI visuals as humans do, highlighting the need for GUI visual agents.In this work, we develop a vision-language-action model in the digital world, namely "Our Model," which features the following innovations:1. **UI-Guided Visual Token Selection** to reduce computational costs by formulating screenshots as a UI-connected graph, adaptively identifying their redundant relationships and serving as the criteria for token selection during self-attention blocks.  2. **Interleaved Vision-Language-Action Streaming** that flexibly unifies diverse needs within GUI tasks, enabling effective management of visual-action history in navigation or pairing multi-turn query-action sequences per screenshot to enhance training efficiency.  3. **Small-Scale High-Quality GUI Instruction-Following Datasets** by careful data curation and employing a resampling strategy to address significant data type imbalances.  With the above components, our model, a lightweight 2B model using 256K data, achieves a strong 75.1% accuracy in zero-shot screenshot grounding. Its UI-guided token selection further reduces 33% of redundant visual tokens during training and speeds up performance by 1.4x. Navigation experiments across web, mobile, and online environments further underscore the effectiveness and potential of our model in advancing GUI visual agents.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_ShowUI_One_Vision-Language-Action_Model_for_GUI_Visual_Agent_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Kevin Qinghong and Li, Linjie and Gao, Difei and Yang, Zhengyuan and Wu, Shiwei and Bai, Zechen and Lei, Stan Weixian and Wang, Lijuan and Shou, Mike Zheng},
    title     = {ShowUI: One Vision-Language-Action Model for GUI Visual Agent},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19498-19508}
}

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


title: Let's Chorus: Partner-aware Hybrid Song-Driven 3D Head Animation
authors: Xiumei Xie, Zikai Huang, Wenhao Xu, Peng Xiao, Xuemiao Xu, Huaidong Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5467-5476
cities: ["Guangzhou", "Guangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_Lets_Chorus_Partner-aware_Hybrid_Song-Driven_3D_Head_Animation_CVPR_2025_paper.html

abstract:
Singing is a vital form of human emotional expression and social interaction, distinguished from speech by its richer emotional nuances and freer expressive style. Thus, investigating 3D facial animation driven by singing holds significant research value. Our work focuses on 3D singing facial animation driven by mixed singing audio, and to the best of our knowledge, no prior studies have explored this area. Additionally, the absence of existing 3D singing datasets poses a considerable challenge. To address this, we collect a novel audiovisual dataset, ChorusHead which features synchronized mixed vocal audio and pseudo-3D flame motions for chorus singing. In addition, We propose a partner-aware 3D chorus head generation framework driven by mixed audio inputs. The proposed framework extracts emotional features from the background music and dependence between singers and models the head movement in a latent space from the Variational Autoencoder (VAE), enabling diverse interactive head animation generation. Extensive experimental results demonstrate that our approach effectively generates 3D facial animations of interacting singers, achieving notable improvements in realism and handling background music interference with strong robustness.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_Lets_Chorus_Partner-aware_Hybrid_Song-Driven_3D_Head_Animation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_Lets_Chorus_Partner-aware_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Xiumei and Huang, Zikai and Xu, Wenhao and Xiao, Peng and Xu, Xuemiao and Zhang, Huaidong},
    title     = {Let's Chorus: Partner-aware Hybrid Song-Driven 3D Head Animation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5467-5476}
}

raw_affiliation:
[{"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Guangdong Engineering Center for Large Model and GenAI Technology", "city": "Guangzhou", "country": "China"}]

affiliations:
["South China University of Technology", "Guangdong Engineering Center for Large Model and GenAI Technology"]


title: Twinner: Shining Light on Digital Twins in a Few Snaps
authors: Jesus Zarzar, Tom Monnier, Roman Shapovalov, Andrea Vedaldi, David Novotny
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5859-5869
affiliations: ["VGG, University of Oxford", "KAUST", "Meta AI"]
cities: ["Oxford", "Thuwal", "Menlo Park"]
countries: ["United Kingdom", "Saudi Arabia", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zarzar_Twinner_Shining_Light_on_Digital_Twins_in_a_Few_Snaps_CVPR_2025_paper.html

abstract:
We present the first large reconstruction model, Twinner, capable of recovering a scene's illumination as well as an object's geometry and material properties from only a few posed images. Twinner is based on the Large Reconstruction Model and innovates in three key ways:1) We introduce a memory-efficient voxel-grid transformer whose memory scales only quadratically with the size of the voxel grid. 2) To deal with scarcity of high-quality ground-truth PBR-shaded models, we introduce a large fully-synthetic dataset of procedurally-generated PBR-textured objects lit with varied illumination.3) To narrow the synthetic-to-real gap, we finetune the model on real life datasets by means of a differentiable physically-based shading model, eschewing the need for ground-truth illumination or material properties which are challenging to obtain in real life. We demonstrate the efficacy of our model on the real life StanfordORB benchmark where, given few input views, we achieve reconstruction quality significantly superior to existing feed-forward reconstruction networks, and comparable to significantly slower per-scene optimization methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zarzar_Twinner_Shining_Light_on_Digital_Twins_in_a_Few_Snaps_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zarzar_Twinner_Shining_Light_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zarzar_2025_CVPR,
    author    = {Zarzar, Jesus and Monnier, Tom and Shapovalov, Roman and Vedaldi, Andrea and Novotny, David},
    title     = {Twinner: Shining Light on Digital Twins in a Few Snaps},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5859-5869}
}

raw_affiliation:
[{"affiliation": "VGG, University of Oxford", "city": "Oxford", "country": "United Kingdom"}, {"affiliation": "KAUST", "city": "Thuwal", "country": "Saudi Arabia"}, {"affiliation": "Meta AI", "city": "Menlo Park", "country": "USA"}]


title: Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis
authors: Jian Han, Jinlai Liu, Yi Jiang, Bin Yan, Yuqi Zhang, Zehuan Yuan, Bingyue Peng, Xiaobing Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15733-15744
raw_affiliation: [{"affiliation": "ByteDance", "city": "Beijing", "country": "China"}]
affiliations: ["ByteDance"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Han_Infinity_Scaling_Bitwise_AutoRegressive_Modeling_for_High-Resolution_Image_Synthesis_CVPR_2025_paper.html

abstract:
We present Infinity, a Bitwise Visual AutoRegressive Modeling capable of generating high-resolution, photorealistic images following language instruction. Infinity refactors visual autoregressive model under a bitwise token prediction framework with an infinite-vocabulary classifier and bitwise self-correction mechanism. By theoretically expanding the tokenizer vocabulary size to infinity in Transformer, our method significantly unleashes powerful scaling capabilities to infinity compared to vanilla VAR. Extensive experiments indicate Infinity outperforms AutoRegressive Text-to-Image models by large margins, matches or surpasses leading diffusion models. Without extra optimization, Infinity generates a 1024x1024 image in 0.8s, 2.6xfaster than SD3-Medium, making it the fastest Text-to-Image model. Models and codes will be released to promote the further exploration of Infinity for visual generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Han_Infinity_Scaling_Bitwise_AutoRegressive_Modeling_for_High-Resolution_Image_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Han_Infinity_Scaling_Bitwise_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Han_2025_CVPR,
    author    = {Han, Jian and Liu, Jinlai and Jiang, Yi and Yan, Bin and Zhang, Yuqi and Yuan, Zehuan and Peng, Bingyue and Liu, Xiaobing},
    title     = {Infinity: Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15733-15744}
}


title: DreamText: High Fidelity Scene Text Synthesis
authors: Yibin Wang, Weizhong Zhang, Honghui Xu, Cheng Jin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28555-28563
cities: ["Shanghai", "Shanghai", "Shanghai", "Shanghai", "Hangzhou"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_DreamText_High_Fidelity_Scene_Text_Synthesis_CVPR_2025_paper.html

abstract:
Scene text synthesis involves rendering specified texts onto arbitrary images. Current methods typically formulate this task in an end-to-end manner but lack effective character-level guidance during training.Besides, their text encoders, pre-trained on a single font type, struggle to adapt to the diverse font styles encountered in practical applications.Consequently, these methods suffer from character distortion, repetition, and absence, particularly in polystylistic scenarios.To this end, this paper proposes DreamText for high-fidelity scene text synthesis.Our key idea is to reconstruct the diffusion training process, introducing more refined guidance tailored to this task, to expose and rectify the model's attention at the character level and strengthen its learning of text regions.This transformation poses a hybrid optimization challenge, involving both discrete and continuous variables. To effectively tackle this challenge, we employ a heuristic alternate optimization strategy. Meanwhile, we jointly train the text encoder and generator to comprehensively learn and utilize the diverse font present in the training dataset. This joint training is seamlessly integrated into the alternate optimization process, fostering a synergistic relationship between learning character embedding and re-estimating character attention.Specifically, in each step, we first encode potential character-generated position information from cross-attention maps into latent character masks. These masks are then utilized to update the representation of specific characters in the current step, which, in turn, enables the generator to correct the character's attention in the subsequent steps.Both qualitative and quantitative results demonstrate the superiority of our method to the state of the art.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_DreamText_High_Fidelity_Scene_Text_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_DreamText_High_Fidelity_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yibin and Zhang, Weizhong and Xu, Honghui and Jin, Cheng},
    title     = {DreamText: High Fidelity Scene Text Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28555-28563}
}

raw_affiliation:
[{"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Innovation Institute", "city": "Shanghai", "country": "China"}, {"affiliation": "Innovation Center of Calligraphy and Painting Creation Technology, MCT", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Key Laboratory of Intelligent Information Processing", "city": "Shanghai", "country": "China"}, {"affiliation": "Zhejiang University of Technology", "city": "Hangzhou", "country": "China"}]

affiliations:
["Fudan University", "Shanghai Innovation Institute", "Innovation Center of Calligraphy and Painting Creation Technology, MCT", "Shanghai Key Laboratory of Intelligent Information Processing", "Zhejiang University of Technology"]


title: MonoPlace3D: Learning 3D-Aware Object Placement for 3D Monocular Detection
authors: Rishubh Parihar, Srinjay Sarkar, Sarthak Vora, Jogendra Nath Kundu, R. Venkatesh Babu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6531-6541
raw_affiliation: [{"affiliation": "IISc", "city": "Bangalore", "country": "India"}]
affiliations: ["IISc"]
cities: ["Bangalore"]
countries: ["India"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Parihar_MonoPlace3D_Learning_3D-Aware_Object_Placement_for_3D_Monocular_Detection_CVPR_2025_paper.html

abstract:
Current monocular 3D detectors are held back by the limited diversity and scale of real-world datasets. While data augmentation certainly helps, it's particularly difficult to generate realistic scene-aware augmented data for outdoor settings. Most current approaches to synthetic data generation focus on realistic object appearance through improved rendering techniques. However, we show that where and how objects are positioned is just as crucial for training effective 3D monocular detectors. The key obstacle lies in automatically determining realistic object placement parameters - including position, dimensions, and directional alignment when introducing synthetic objects into actual scenes. To address this, we introduce MonoPlace3D, a novel system that considers the 3D scene content to create realistic augmentations. Specifically, given a background scene, MonoPlace3D learns a distribution over plausible 3D bounding boxes. Subsequently, we render realistic objects and place them according to the locations sampled from the learned distribution. Our comprehensive evaluation on two standard datasets KITTI and NuScenes,  demonstrates that MonoPlace3D significantly improves the accuracy of multiple existing monocular 3D detectors while being highly data efficient.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Parihar_MonoPlace3D_Learning_3D-Aware_Object_Placement_for_3D_Monocular_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Parihar_MonoPlace3D_Learning_3D-Aware_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Parihar_2025_CVPR,
    author    = {Parihar, Rishubh and Sarkar, Srinjay and Vora, Sarthak and Kundu, Jogendra Nath and Babu, R. Venkatesh},
    title     = {MonoPlace3D: Learning 3D-Aware Object Placement for 3D Monocular Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6531-6541}
}


title: HumanDreamer: Generating Controllable Human-Motion Videos via Decoupled Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12391-12401
cities: ["Beijing", "Beijing", "Luoyang", "Beijing", "Beijing", "Hong Kong"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_HumanDreamer_Generating_Controllable_Human-Motion_Videos_via_Decoupled_Generation_CVPR_2025_paper.html

authors:
Boyuan Wang, Xiaofeng Wang, Chaojun Ni, Guosheng Zhao, Zhiqin Yang, Zheng Zhu, Muyang Zhang, Yukun Zhou, Xinze Chen, Guan Huang, Lihong Liu, Xingang Wang

abstract:
Human-motion video generation has been a challenging task, primarily due to the difficulty inherent in learning human body movements. While some approaches have attempted to drive human-centric video generation explicitly through pose control, these methods typically rely on poses derived from existing videos, thereby lacking flexibility. To address this, we propose HumanDreamer, a decoupled human video generation framework that first generates diverse poses from text prompts and then leverages these poses to generate human-motion videos. Specifically, we propose MotionVid, the largest dataset for human-motion pose generation. Based on the dataset, we present MotionDiT, which is trained to generate structured human-motion poses from text prompts. Besides, a novel LAMA loss is introduced, which together contribute to a significant improvement in FID by 62.4%, along with respective enhancements in R-precision for top1, top2, and top3 by 41.8%, 26.3%, and 18.3%, thereby advancing both the Text-to-Pose control accuracy and FID metrics. Our experiments across various Pose-to-Video baselines demonstrate that the poses generated by our method can produce diverse and high-quality human-motion videos. Furthermore, our model can facilitate other downstream tasks, such as pose sequence prediction and 2D-3D motion lifting.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_HumanDreamer_Generating_Controllable_Human-Motion_Videos_via_Decoupled_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_HumanDreamer_Generating_Controllable_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Boyuan and Wang, Xiaofeng and Ni, Chaojun and Zhao, Guosheng and Yang, Zhiqin and Zhu, Zheng and Zhang, Muyang and Zhou, Yukun and Chen, Xinze and Huang, Guan and Liu, Lihong and Wang, Xingang},
    title     = {HumanDreamer: Generating Controllable Human-Motion Videos via Decoupled Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12391-12401}
}

raw_affiliation:
[{"affiliation": "Institute of Automation, Chinese Academy of Sciences, China", "city": "Beijing", "country": "China"}, {"affiliation": "School of Artificial Intelligence, University of Chinese Academy of Sciences, China", "city": "Beijing", "country": "China"}, {"affiliation": "Luoyang Institute for Robot and Intelligent Equipment, China", "city": "Luoyang", "country": "China"}, {"affiliation": "GigaAI, China", "city": "Beijing", "country": "China"}, {"affiliation": "Peking University, China", "city": "Beijing", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["Institute of Automation, Chinese Academy of Sciences, China", "School of Artificial Intelligence, University of Chinese Academy of Sciences, China", "Luoyang Institute for Robot and Intelligent Equipment, China", "GigaAI, China", "Peking University, China", "The Chinese University of Hong Kong"]


title: ReVisionLLM: Recursive Vision-Language Model for Temporal Grounding in Hour-Long Videos
authors: Tanveer Hannan, Md Mohaiminul Islam, Jindong Gu, Thomas Seidl, Gedas Bertasius
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19012-19022
affiliations: ["LMU Munich", "MCML", "UNC Chapel Hill", "University of Oxford"]
cities: ["Munich", "Munich", "Chapel Hill", "Oxford"]
countries: ["Germany", "Germany", "USA", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hannan_ReVisionLLM_Recursive_Vision-Language_Model_for_Temporal_Grounding_in_Hour-Long_Videos_CVPR_2025_paper.html

abstract:
Large language models (LLMs) excel at retrieving information from lengthy text, but their vision-language counterparts (VLMs) face difficulties with hour-long videos, especially for temporal grounding. Specifically, these VLMs are constrained by frame limitations, often losing essential temporal details needed for accurate event localization in extended video content. We propose ReVisionLLM, a recursive vision-language model designed to locate events in hour-long videos. Inspired by human search strategies, our model initially targets broad segments of interest, progressively revising its focus to pinpoint exact temporal boundaries. Our model can seamlessly handle videos of vastly different lengths--from minutes to hours. We also introduce a hierarchical training strategy that starts with short clips to capture distinct events and progressively extends to longer videos. To our knowledge, ReVisionLLM is the first VLM capable of temporal grounding in hour-long videos, outperforming previous state-of-the-art methods across multiple datasets by a significant margin (e.g., +2.6% R1@0.1 on MAD). The code is available in the supplementary and will be released.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hannan_ReVisionLLM_Recursive_Vision-Language_Model_for_Temporal_Grounding_in_Hour-Long_Videos_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hannan_ReVisionLLM_Recursive_Vision-Language_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hannan_2025_CVPR,
    author    = {Hannan, Tanveer and Islam, Md Mohaiminul and Gu, Jindong and Seidl, Thomas and Bertasius, Gedas},
    title     = {ReVisionLLM: Recursive Vision-Language Model for Temporal Grounding in Hour-Long Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19012-19022}
}

raw_affiliation:
[{"affiliation": "LMU Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "MCML", "city": "Munich", "country": "Germany"}, {"affiliation": "UNC Chapel Hill", "city": "Chapel Hill", "country": "USA"}, {"affiliation": "University of Oxford", "city": "Oxford", "country": "UK"}]


title: ArtiFade: Learning to Generate High-quality Subject from Blemished Images
authors: Shuya Yang, Shaozhe Hao, Yukang Cao, Kwan-Yee K. Wong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13167-13177
raw_affiliation: [{"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}]
affiliations: ["The University of Hong Kong"]
cities: ["Hong Kong"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_ArtiFade_Learning_to_Generate_High-quality_Subject_from_Blemished_Images_CVPR_2025_paper.html

abstract:
Subject-driven text-to-image generation has demonstrated remarkable advancements in its ability to learn and capture characteristics of a subject using only a limited number of images. However, existing methods commonly rely on high-quality images for training and often struggle to generate reasonable images when the input images are blemished by artifacts. This is primarily attributed to the inadequate capability of current techniques in distinguishing subject-related features from disruptive artifacts. In this paper, we introduce ArtiFade to tackle this issue and successfully generate high-quality artifact-free images from blemished datasets. Specifically, ArtiFade exploits fine-tuning of a pre-trained text-to-image model, aiming to remove artifacts. The elimination of artifacts is achieved by utilizing a specialized dataset that encompasses both unblemished images and their corresponding blemished counterparts during fine-tuning. ArtiFade also ensures the preservation of the original generative capabilities inherent within the diffusion model, thereby enhancing the overall performance of subject-driven methods in generating high-quality and artifact-free images. We further devise evaluation benchmarks tailored for this task. Through extensive qualitative and quantitative experiments, we demonstrate the generalizability of ArtiFade in effective artifact removal under both in-distribution and out-of-distribution scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_ArtiFade_Learning_to_Generate_High-quality_Subject_from_Blemished_Images_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_ArtiFade_Learning_to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Shuya and Hao, Shaozhe and Cao, Yukang and Wong, Kwan-Yee K.},
    title     = {ArtiFade: Learning to Generate High-quality Subject from Blemished Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13167-13177}
}


title: SGCR: Spherical Gaussians for Efficient 3D Curve Reconstruction
authors: Xinran Yang, Donghao Ji, Yuanqi Li, Jie Guo, Yanwen Guo, Junyuan Xie
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5793-5803
raw_affiliation: [{"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}]
affiliations: ["Nanjing University"]
cities: ["Nanjing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_SGCR_Spherical_Gaussians_for_Efficient_3D_Curve_Reconstruction_CVPR_2025_paper.html

abstract:
Neural rendering techniques have made substantial progress in generating photo-realistic 3D scenes. The latest 3D Gaussian Splatting technique has achieved high quality novel view synthesis as well as fast rendering speed. However, 3D Gaussians lack proficiency in defining accurate 3D geometric structures despite their explicit primitive representations. This is due to the fact that Gaussian's attributes are primarily tailored and fine-tuned for rendering diverse 2D images by their anisotropic nature. To pave the way for efficient 3D reconstruction, we present Spherical Gaussians, a simple and effective representation for 3D geometric boundaries, from which we can directly reconstruct 3D feature curves from a set of calibrated multi-view images. Spherical Gaussians is optimized from grid initialization with a view-based rendering loss, where a 2D edge map is rendered at a specific view and then compared to the ground-truth edge map extracted from the corresponding image, without the need for any 3D guidance or supervision. Given Spherical Gaussians serve as intermedia for the robust edge representation, we further introduce a novel optimization-based algorithm called SGCR to directly extract accurate parametric curves from aligned Spherical Gaussians. We demonstrate that SGCR outperforms existing state-of-the-art methods in 3D edge reconstruction while enjoying great efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_SGCR_Spherical_Gaussians_for_Efficient_3D_Curve_Reconstruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_SGCR_Spherical_Gaussians_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Xinran and Ji, Donghao and Li, Yuanqi and Guo, Jie and Guo, Yanwen and Xie, Junyuan},
    title     = {SGCR: Spherical Gaussians for Efficient 3D Curve Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5793-5803}
}


title: Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17070-17080
affiliations: ["Zhejiang University", "ByteDance Seed", "Shanghai Jiao Tong University", "ETH Zurich"]
cities: ["Hangzhou", "Beijing", "Shanghai", "Zurich"]
countries: ["China", "China", "China", "Switzerland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_Prompting_Depth_Anything_for_4K_Resolution_Accurate_Metric_Depth_Estimation_CVPR_2025_paper.html

authors:
Haotong Lin, Sida Peng, Jingxiao Chen, Songyou Peng, Jiaming Sun, Minghuan Liu, Hujun Bao, Jiashi Feng, Xiaowei Zhou, Bingyi Kang

abstract:
Prompts play a critical role in unleashing the power of language and vision foundation models for specific tasks. For the first time, we introduce prompting into depth foundation models, creating a new paradigm for metric depth estimation termed Prompt Depth Anything. Specifically, we use a low-cost LiDAR as the prompt to guide the Depth Anything model for accurate metric depth output, achieving up to 4K resolution. Our approach centers on a concise prompt fusion design that integrates the LiDAR at multiple scales within the depth decoder. To address training challenges posed by limited datasets containing both LiDAR depth and precise GT depth, we propose a scalable data pipeline that includes synthetic data LiDAR simulation and real data pseudo GT depth generation. Our approach sets new state-of-the-arts on the ARKitScenes and ScanNet++ datasets. Furthermore, we demonstrate that it benefits several downstream applications, including 3D reconstruction and generalized robotic grasping. Code will be released.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_Prompting_Depth_Anything_for_4K_Resolution_Accurate_Metric_Depth_Estimation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Haotong and Peng, Sida and Chen, Jingxiao and Peng, Songyou and Sun, Jiaming and Liu, Minghuan and Bao, Hujun and Feng, Jiashi and Zhou, Xiaowei and Kang, Bingyi},
    title     = {Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17070-17080}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "ByteDance Seed", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}]


title: ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models
authors: Yassir Bendou, Amine Ouasfi, Vincent Gripon, Adnane Boukhayma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25092-25102
affiliations: ["IMT Atlantique", "Inria, University Rennes, IRISA, CNRS"]
cities: ["Brest", "Rennes"]
countries: ["France", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bendou_ProKeR_A_Kernel_Perspective_on_Few-Shot_Adaptation_of_Large_Vision-Language_CVPR_2025_paper.html

abstract:
The growing popularity of Contrastive Language-Image Pretraining (CLIP) has led to its widespread application in various visual downstream tasks. To enhance CLIP's effectiveness and versatility, efficient few-shot adaptation techniques have been widely adopted. Among these approaches, training-free methods, particularly caching methods exemplified by Tip-Adapter, have gained attention for their lightweight adaptation without the need for additional fine-tuning. In this paper, we revisit Tip-Adapter from a kernel perspective, showing that caching methods function as local adapters and are connected to a well-established kernel literature. Drawing on this insight, we offer a theoretical understanding of how these methods operate and suggest multiple avenues for enhancing the Tip-Adapter baseline. Notably, our analysis shows the importance of incorporating global information in local adapters. Therefore, we subsequently propose a global method that learns a proximal regularizer in a reproducing kernel Hilbert space (RKHS) using CLIP as a base learner. Our method, which we call ProKeR (Proximal Kernel ridge Regression), has a closed form solution and achieves state-of-the-art performances across 11 datasets in the standard few-shot adaptation benchmark. Code is available at https://ybendou.github.io/ProKeR/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bendou_ProKeR_A_Kernel_Perspective_on_Few-Shot_Adaptation_of_Large_Vision-Language_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bendou_ProKeR_A_Kernel_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bendou_2025_CVPR,
    author    = {Bendou, Yassir and Ouasfi, Amine and Gripon, Vincent and Boukhayma, Adnane},
    title     = {ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25092-25102}
}

raw_affiliation:
[{"affiliation": "IMT Atlantique", "city": "Brest", "country": "France"}, {"affiliation": "Inria, University Rennes, IRISA, CNRS", "city": "Rennes", "country": "France"}]


title: Advancing Semantic Future Prediction through Multimodal Visual Sequence Transformers
authors: Efstathios Karypidis, Ioannis Kakogeorgiou, Spyros Gidaris, Nikos Komodakis
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3793-3803
cities: ["Athens", "Paris", "Athens", "Heraklion", "Heraklion"]
countries: ["Greece", "France", "Greece", "Greece", "Greece"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Karypidis_Advancing_Semantic_Future_Prediction_through_Multimodal_Visual_Sequence_Transformers_CVPR_2025_paper.html

abstract:
Semantic future prediction is important for autonomous systems navigating dynamic environments. This paper introduces FUTURIST, a method for multimodal future semantic prediction that uses a unified and efficient visual sequence transformer architecture. Our approach incorporates a multimodal masked visual modeling objective and a novel masking mechanism designed for multimodal training. This allows the model to effectively integrate visible information from various modalities, improving prediction accuracy. Additionally, we propose a VAE-free hierarchical tokenization process, which reduces computational complexity, streamlines the training pipeline, and enables end-to-end training with high-resolution, multimodal inputs. We validate FUTURIST on the Cityscapes dataset, demonstrating state-of-the-art performance in future semantic segmentation for both short- and mid-term forecasting. We provide the implementation code and model weights at https://github.com/Sta8is/FUTURIST.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Karypidis_Advancing_Semantic_Future_Prediction_through_Multimodal_Visual_Sequence_Transformers_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Karypidis_Advancing_Semantic_Future_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Karypidis_2025_CVPR,
    author    = {Karypidis, Efstathios and Kakogeorgiou, Ioannis and Gidaris, Spyros and Komodakis, Nikos},
    title     = {Advancing Semantic Future Prediction through Multimodal Visual Sequence Transformers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3793-3803}
}

raw_affiliation:
[{"affiliation": "Archimedes, Athena Research Center", "city": "Athens", "country": "Greece"}, {"affiliation": "valeo.ai", "city": "Paris", "country": "France"}, {"affiliation": "National Technical University of Athens", "city": "Athens", "country": "Greece"}, {"affiliation": "University of Crete", "city": "Heraklion", "country": "Greece"}, {"affiliation": "IACM-Forth", "city": "Heraklion", "country": "Greece"}]

affiliations:
["Archimedes, Athena Research Center", "valeo.ai", "National Technical University of Athens", "University of Crete", "IACM-Forth"]


title: GET: Unlocking the Multi-modal Potential of CLIP for Generalized Category Discovery
authors: Enguang Wang, Zhimao Peng, Zhengyuan Xie, Fei Yang, Xialei Liu, Ming-Ming Cheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20296-20306
affiliations: ["VCIP, CS, Nankai University", "NKIARI, Shenzhen Futian"]
cities: ["Tianjin", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_GET_Unlocking_the_Multi-modal_Potential_of_CLIP_for_Generalized_Category_CVPR_2025_paper.html

abstract:
Given unlabelled datasets containing both old and new categories, generalized category discovery (GCD) aims to accurately discover new classes while correctly classifying old classes. Current GCD methods only use a single visual modality of information, resulting in poor classification of visually similar classes. As a different modality, text information can provide complementary discriminative information, which motivates us to introduce it into the GCD task. However, the lack of class names for unlabelled data makes it impractical to utilize text information. To tackle this challenging problem, in this paper, we propose a Text Embedding Synthesizer (TES) to generate pseudo text embeddings for unlabelled samples. Specifically, our TES leverages the property that CLIP can generate aligned vision-language features, converting visual embeddings into tokens of the CLIP's text encoder to generate pseudo text embeddings. Besides, we employ a dual-branch framework, through the joint learning and instance consistency of different modality branches, visual and semantic information mutually enhance each other, promoting the interaction and fusion of visual and text knowledge. Our method unlocks the multi-modal potentials of CLIP and outperforms the baseline methods by a large margin on all GCD benchmarks, achieving new state-of-the-art.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_GET_Unlocking_the_Multi-modal_Potential_of_CLIP_for_Generalized_Category_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_GET_Unlocking_the_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Enguang and Peng, Zhimao and Xie, Zhengyuan and Yang, Fei and Liu, Xialei and Cheng, Ming-Ming},
    title     = {GET: Unlocking the Multi-modal Potential of CLIP for Generalized Category Discovery},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20296-20306}
}

raw_affiliation:
[{"affiliation": "VCIP, CS, Nankai University", "city": "Tianjin", "country": "China"}, {"affiliation": "NKIARI, Shenzhen Futian", "city": "Shenzhen", "country": "China"}]


title: On the Out-Of-Distribution Generalization of Large Multimodal Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10315-10326
affiliations: ["Tsinghua University", "Beijing Jiaotong University"]
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_On_the_Out-Of-Distribution_Generalization_of_Large_Multimodal_Models_CVPR_2025_paper.html

authors:
Xingxuan Zhang, Jiansheng Li, Wenjing Chu, junjia hai, Renzhe Xu, Yuqing Yang, Shikai Guan, Jiazheng Xu, Liping Jing, Peng Cui

abstract:
We investigate the generalization boundaries of current Large Multimodal Models (LMMs) via comprehensive evaluation under out-of-distribution scenarios and domain-specific tasks. We evaluate their zero-shot generalization across synthetic images, real-world distributional shifts, and specialized datasets like medical and molecular imagery. Empirical results indicate that LMMs struggle with generalization beyond common training domains, limiting their direct application without adaptation. To understand the cause of unreliable performance, we analyze three hypotheses: semantic misinterpretation, visual feature extraction insufficiency, and mapping deficiency. Results identify mapping deficiency as the primary hurdle. To address this problem, we show that in-context learning (ICL) can significantly enhance LMMs' generalization, opening new avenues for overcoming generalization barriers. We further explore the robustness of ICL under distribution shifts and show its vulnerability to domain shifts, label shifts, and spurious correlation shifts between in-context examples and test data.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_On_the_Out-Of-Distribution_Generalization_of_Large_Multimodal_Models_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Xingxuan and Li, Jiansheng and Chu, Wenjing and hai, junjia and Xu, Renzhe and Yang, Yuqing and Guan, Shikai and Xu, Jiazheng and Jing, Liping and Cui, Peng},
    title     = {On the Out-Of-Distribution Generalization of Large Multimodal Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10315-10326}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing Jiaotong University", "city": "Beijing", "country": "China"}]


title: Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generation
authors: Kang Liu, Zhuoqi Ma, Xiaolu Kang, Yunan Li, Kun Xie, Zhicheng Jiao, Qiguang Miao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10348-10359
affiliations: ["Xidian University", "Brown University"]
cities: ["Xi'an", "Providence"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Enhanced_Contrastive_Learning_with_Multi-view_Longitudinal_Data_for_Chest_X-ray_CVPR_2025_paper.html

abstract:
Automated radiology report generation offers an effective solution to alleviate radiologists' workload. However, most existing methods focus primarily on single or fixed-view images to model current disease conditions, which limits diagnostic accuracy and overlooks disease progression. Although some approaches utilize longitudinal data to track disease progression, they still rely on single images to analyze current visits. To address these issues, we propose enhanced contrastive learning with Multi-view Longitudinal data to facilitate chest X-ray Report Generation, named MLRG. Specifically, we introduce a multi-view longitudinal contrastive learning method that integrates spatial information from current multi-view images and temporal information from longitudinal data. This method also utilizes the inherent spatiotemporal information of radiology reports to supervise the pre-training of visual and textual representations. Subsequently, we present a tokenized absence encoding technique to flexibly handle missing patient-specific prior knowledge, allowing the model to produce more accurate radiology reports based on available prior knowledge. Extensive experiments on MIMIC-CXR, MIMIC-ABN, and Two-view CXR datasets demonstrate that our MLRG outperforms recent state-of-the-art methods, achieving a 2.3% BLEU-4 improvement on MIMIC-CXR, a 5.5% F1 score improvement on MIMIC-ABN, and a 2.7% F1 RadGraph improvement on Two-view CXR.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Enhanced_Contrastive_Learning_with_Multi-view_Longitudinal_Data_for_Chest_X-ray_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Enhanced_Contrastive_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Kang and Ma, Zhuoqi and Kang, Xiaolu and Li, Yunan and Xie, Kun and Jiao, Zhicheng and Miao, Qiguang},
    title     = {Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10348-10359}
}

raw_affiliation:
[{"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "Brown University", "city": "Providence", "country": "USA"}]


title: MonoTAKD: Teaching Assistant Knowledge Distillation for Monocular 3D Object Detection
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22266-22275
cities: ["Hsinchu", "Seattle", "Los Angeles", "San Jose", "Austin", "Taipei", "Taipei"]
countries: ["Taiwan", "USA", "USA", "USA", "USA", "Taiwan", "Taiwan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_MonoTAKD_Teaching_Assistant_Knowledge_Distillation_for_Monocular_3D_Object_Detection_CVPR_2025_paper.html

authors:
Hou-I Liu, Christine Wu, Jen-Hao Cheng, Wenhao Chai, Shian-Yun Wang, Gaowen Liu, Hugo Latapie, Jhih-Ciang Wu, Jenq-Neng Hwang, Hong-Han Shuai, Wen-Huang Cheng

abstract:
Monocular 3D object detection (Mono3D) holds noteworthy promise for autonomous driving applications owing to the cost-effectiveness and rich visual context of monocular camera sensors. However, depth ambiguity poses a significant challenge, as it requires extracting precise 3D scene geometry from a single image, resulting in suboptimal performance when transferring knowledge from a LiDAR-based teacher model to a camera-based student model. To facilitate effective distillation, we introduce Monocular Teaching Assistant Knowledge Distillation (MonoTAKD), which proposes a camera-based teaching assistant (TA) model to transfer robust 3D visual knowledge to the student model, leveraging the smaller feature representation gap. Additionally, we define 3D spatial cues as residual features that capture the differences between the teacher and the TA models. We then leverage these cues to improve the student model's 3D perception capabilities. Experimental results show that our MonoTAKD achieves state-of-the-art performance on the KITTI3D dataset. Furthermore, we evaluate the performance on nuScenes and KITTI raw datasets to demonstrate the generalization of our model to multi-view 3D and unsupervised data settings. Our code is available at https://github.com/hoiliu-0801/MonoTAKD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_MonoTAKD_Teaching_Assistant_Knowledge_Distillation_for_Monocular_3D_Object_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_MonoTAKD_Teaching_Assistant_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Hou-I and Wu, Christine and Cheng, Jen-Hao and Chai, Wenhao and Wang, Shian-Yun and Liu, Gaowen and Latapie, Hugo and Wu, Jhih-Ciang and Hwang, Jenq-Neng and Shuai, Hong-Han and Cheng, Wen-Huang},
    title     = {MonoTAKD: Teaching Assistant Knowledge Distillation for Monocular 3D Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22266-22275}
}

raw_affiliation:
[{"affiliation": "National Yang Ming Chiao Tung University", "city": "Hsinchu", "country": "Taiwan"}, {"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}, {"affiliation": "University of Southern California", "city": "Los Angeles", "country": "USA"}, {"affiliation": "Cisco Systems, Inc.", "city": "San Jose", "country": "USA"}, {"affiliation": "Taijitu AI, Inc.", "city": "Austin", "country": "USA"}, {"affiliation": "National Taiwan Normal University", "city": "Taipei", "country": "Taiwan"}, {"affiliation": "National Taiwan University", "city": "Taipei", "country": "Taiwan"}]

affiliations:
["National Yang Ming Chiao Tung University", "University of Washington", "University of Southern California", "Cisco Systems, Inc.", "Taijitu AI, Inc.", "National Taiwan Normal University", "National Taiwan University"]


authors: Xingguo Lv, Xingbo Dong, Liwen Wang, Jiewen Yang, Lei Zhao, Bin Pu, Zhe Jin, Xuejun Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15621-15631
cities: ["Hefei", "Changsha", "Hong Kong"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lv_Test-Time_Domain_Generalization_via_Universe_Learning_A_Multi-Graph_Matching_Approach_CVPR_2025_paper.html

title:
Test-Time Domain Generalization via Universe Learning: A Multi-Graph Matching Approach for Medical Image Segmentation

abstract:
Despite domain generalization (DG) has significantly addressed the performance degradation of pre-trained models caused by domain shifts, it often falls short in real-world deployment. Test-time adaptation (TTA), which adjusts a learned model using unlabeled test data, presents a promising solution. However, most existing TTA methods struggle to deliver strong performance in medical image segmentation, primarily because they overlook the crucial prior knowledge inherent to medical images. To address this challenge, we incorporate morphological information and propose a framework based on multi-graph matching. Specifically, we introduce learnable universe embeddings that integrate morphological priors during multi-source training, along with novel unsupervised test-time paradigms for domain adaptation. This approach guarantees cycle-consistency in multi-matching while enabling the model to more effectively capture the invariant priors of unseen data, significantly mitigating the effects of domain shifts. Extensive experiments demonstrate that our method outperforms other state-of-the-art approaches on two medical image segmentation benchmarks for both multi-source and single-source domain generalization tasks. The source code is available at https://github.com/Yore0/TTDG-MGM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lv_Test-Time_Domain_Generalization_via_Universe_Learning_A_Multi-Graph_Matching_Approach_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lv_Test-Time_Domain_Generalization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lv_2025_CVPR,
    author    = {Lv, Xingguo and Dong, Xingbo and Wang, Liwen and Yang, Jiewen and Zhao, Lei and Pu, Bin and Jin, Zhe and Li, Xuejun},
    title     = {Test-Time Domain Generalization via Universe Learning: A Multi-Graph Matching Approach for Medical Image Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15621-15631}
}

raw_affiliation:
[{"affiliation": "Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University", "city": "Hefei", "country": "China"}, {"affiliation": "Hunan University", "city": "Changsha", "country": "China"}, {"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}]

affiliations:
["Anhui Provincial International Joint Research Center for Advanced Technology in Medical Imaging, Anhui University", "Hunan University", "The Hong Kong University of Science and Technology"]


title: Easy-editable Image Vectorization with Multi-layer Multi-scale Distributed Visual Feature Embedding
authors: Ye Chen, Zhangli Hu, Zhongyin Zhao, Yupeng Zhu, Yue Shi, Yuxuan Xiong, Bingbing Ni
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23345-23354
cities: ["Shanghai", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Easy-editable_Image_Vectorization_with_Multi-layer_Multi-scale_Distributed_Visual_Feature_Embedding_CVPR_2025_paper.html

abstract:
Current parameterized image representations embed visual information along the semantic boundaries and struggle to express the internal detailed texture structures of image components, leading to a lack of content consistency after image editing and driving. To address these challenges, this work proposes a novel parameterized representation based on hierarchical image proxy geometry,  utilizing multi-layer hierarchically interrelated proxy geometric control points to embed multi-scale long-range structures and fine-grained texture details. The proposed representation enables smoother and more continuous interpolation during image rendering and ensures high-quality consistency within image components during image editing. Additionally, under the layer-wise representation strategy based on semantic-aware image layer decomposition, we enable decoupled image shape/texture editing of the targets of interest within the image. Extensive experimental results on image vectorization and editing tasks demonstrate that our proposed method achieves high rendering accuracy of general images, including natural images, with a significantly higher image parameter compression ratio, facilitating user-friendly editing of image semantic components.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Easy-editable_Image_Vectorization_with_Multi-layer_Multi-scale_Distributed_Visual_Feature_Embedding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Easy-editable_Image_Vectorization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Ye and Hu, Zhangli and Zhao, Zhongyin and Zhu, Yupeng and Shi, Yue and Xiong, Yuxuan and Ni, Bingbing},
    title     = {Easy-editable Image Vectorization with Multi-layer Multi-scale Distributed Visual Feature Embedding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23345-23354}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University, Shanghai 200240, China", "city": "Shanghai", "country": "China"}, {"affiliation": "USC-SJTU Institute of Cultural and Creative Industry", "city": "Shanghai", "country": "China"}]

affiliations:
["Shanghai Jiao Tong University, Shanghai 200240, China", "USC-SJTU Institute of Cultural and Creative Industry"]


title: Acquire and then Adapt: Squeezing out Text-to-Image Model for Image Restoration
authors: Junyuan Deng, Xinyi Wu, Yongxing Yang, Congchao Zhu, Song Wang, Zhenyao Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23195-23206
affiliations: ["Honor Device Co., Ltd", "Shenzhen University of Advanced Technology"]
cities: ["Shenzhen", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Deng_Acquire_and_then_Adapt_Squeezing_out_Text-to-Image_Model_for_Image_CVPR_2025_paper.html

abstract:
Recently, pre-trained text-to-image (T2I) models have been extensively adopted for real-world image restoration because of their powerful generative prior. However, controlling these large models for image restoration usually requires a large number of high-quality images and immense computational resources for training, which is costly and not privacy-friendly. In this paper, we find that the well-trained large T2I model (i.e., Flux) is able to produce a variety of high-quality images aligned with real-world distributions, offering an unlimited supply of training samples to mitigate the above issue. Specifically, we proposed a training data construction pipeline for image restoration, namely FluxGen, which includes unconditional image generation, image selection, and degraded image simulation. A novel light-weighted adapter (FluxIR)  with squeeze-and-excitation layers is also carefully designed to control the large Diffusion Transformer (DiT)-based T2I model so that reasonable details can be restored. Experiments demonstrate that our proposed method enables the Flux model to adapt effectively to real-world image restoration tasks, achieving superior scores and visual quality on both synthetic and real-world degradation datasets - at only about 8.5% of the training cost compared to current approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Deng_Acquire_and_then_Adapt_Squeezing_out_Text-to-Image_Model_for_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Deng_Acquire_and_then_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Deng_2025_CVPR,
    author    = {Deng, Junyuan and Wu, Xinyi and Yang, Yongxing and Zhu, Congchao and Wang, Song and Wu, Zhenyao},
    title     = {Acquire and then Adapt: Squeezing out Text-to-Image Model for Image Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23195-23206}
}

raw_affiliation:
[{"affiliation": "Honor Device Co., Ltd", "city": "Shenzhen", "country": "China"}, {"affiliation": "Shenzhen University of Advanced Technology", "city": "Shenzhen", "country": "China"}]


title: DeDe: Detecting Backdoor Samples for SSL Encoders via Decoders
authors: Sizai Hou, Songze Li, Duanyi Yao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20675-20684
cities: ["Hong Kong", "Nanjing", "Nanjing"]
countries: ["Hong Kong", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hou_DeDe_Detecting_Backdoor_Samples_for_SSL_Encoders_via_Decoders_CVPR_2025_paper.html

abstract:
Self-supervised learning (SSL) is pervasively exploited in training high-quality upstream encoders with a large amount of unlabeled data. However, it is found to be susceptible to backdoor attacks merely via polluting a small portion of training data. The victim encoders associate triggered inputs with target embeddings, e.g., mapping a triggered cat image to an airplane embedding, such that the downstream tasks inherit unintended behaviors when the trigger is activated. Emerging backdoor attacks have shown great threats across different SSL paradigms such as contrastive learning and CLIP, yet limited research is devoted to defending against such attacks, and existing defenses fall short in detecting advanced stealthy backdoors. To address the limitations, we propose a novel detection mechanism, DeDe, which detects the activation of backdoor mappings caused by triggered inputs on victim encoders. Specifically, DeDe trains a decoder for any given SSL encoder using an auxiliary dataset (which can be out-of-distribution or even slightly poisoned), so that for any triggered input that misleads the encoder into the target embedding, the decoder generates an output image significantly different from the input. DeDe leverages the discrepancy between the input and the decoded output to identify potential backdoor misbehavior during inference. We empirically evaluate DeDe on both contrastive learning and CLIP models against various types of backdoor attacks. Our results demonstrate promising detection effectiveness over various advanced attacks and superior performance compared over state-of-the-art detection methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hou_DeDe_Detecting_Backdoor_Samples_for_SSL_Encoders_via_Decoders_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hou_DeDe_Detecting_Backdoor_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Hou_2025_CVPR,
    author    = {Hou, Sizai and Li, Songze and Yao, Duanyi},
    title     = {DeDe: Detecting Backdoor Samples for SSL Encoders via Decoders},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20675-20684}
}

raw_affiliation:
[{"affiliation": "Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "Hong Kong"}, {"affiliation": "Southeast University", "city": "Nanjing", "country": "China"}, {"affiliation": "Engineering Research Center of Blockchain Application, Supervision and Management (Southeast University), Ministry of Education", "city": "Nanjing", "country": "China"}]

affiliations:
["Hong Kong University of Science and Technology", "Southeast University", "Engineering Research Center of Blockchain Application, Supervision and Management (Southeast University), Ministry of Education"]


title: Towards Scalable Human-aligned Benchmark for Text-guided Image Editing
authors: Suho Ryu, Kihyun Kim, Eugene Baek, Dongsoo Shin, Joonseok Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18292-18301
affiliations: ["Graduate School of Data Science, Seoul National University"]
cities: ["Seoul"]
countries: ["South Korea"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ryu_Towards_Scalable_Human-aligned_Benchmark_for_Text-guided_Image_Editing_CVPR_2025_paper.html

abstract:
A variety of text-guided image editing models have been proposed recently. However, there is no widely-accepted standard evaluation method mainly due to the subjective nature of the task, letting researchers rely on manual user study. To address this, we introduce a novel Human-Aligned benchmark for Text-guided Image Editing (HATIE). Providing a large-scale benchmark set covering a wide range of editing tasks, it allows reliable evaluation, not limited to specific easy-to-evaluate cases. Also, HATIE provides a fully-automated and omnidirectional evaluation pipeline. Particularly, we combine multiple scores measuring various aspects of editing so as to align with human perception. We empirically verify that the evaluation of HATIE is indeed human-aligned in various aspects, and provide benchmark results on several state-of-the-art models to provide deeper insights on their performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ryu_Towards_Scalable_Human-aligned_Benchmark_for_Text-guided_Image_Editing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ryu_Towards_Scalable_Human-aligned_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ryu_2025_CVPR,
    author    = {Ryu, Suho and Kim, Kihyun and Baek, Eugene and Shin, Dongsoo and Lee, Joonseok},
    title     = {Towards Scalable Human-aligned Benchmark for Text-guided Image Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18292-18301}
}

raw_affiliation:
[{"affiliation": "Graduate School of Data Science, Seoul National University", "city": "Seoul", "country": "South Korea"}]


authors: Zhangqi Jiang, Junkai Chen, Beier Zhu, Tingjin Luo, Yankun Shen, Xu Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25004-25014
cities: ["Changsha", "Nanjing", "Nanjing", "Singapore"]
countries: ["China", "China", "China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_Devils_in_Middle_Layers_of_Large_Vision-Language_Models_Interpreting_Detecting_CVPR_2025_paper.html

title:
Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens

abstract:
Hallucinations in Large Vision-Language Models (LVLMs) significantly undermine their reliability, motivating researchers to explore the causes of hallucination. However, most studies primarily focus on the language aspect rather than the visual. In this paper, we address how LVLMs process visual information and whether this process causes hallucination. Firstly, we use the attention lens to identify the stages at which LVLMs handle visual data, discovering that the middle layers are crucial. Moreover, we find that these layers can be further divided into two stages: "visual information enrichment" and "semantic refinement" which respectively propagate visual data to object tokens and interpret it through text. By analyzing attention patterns during the visual information enrichment stage, we find that real tokens consistently receive higher attention weights than hallucinated ones, serving as a strong indicator of hallucination. Further examination of multi-head attention maps reveals that hallucination tokens often result from heads interacting with inconsistent objects. Based on these insights, we propose a simple inference-time method that adjusts visual attention by integrating information across various heads. Extensive experiments demonstrate that this approach effectively mitigates hallucinations in mainstream LVLMs without additional training costs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_Devils_in_Middle_Layers_of_Large_Vision-Language_Models_Interpreting_Detecting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_Devils_in_Middle_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Zhangqi and Chen, Junkai and Zhu, Beier and Luo, Tingjin and Shen, Yankun and Yang, Xu},
    title     = {Devils in Middle Layers of Large Vision-Language Models: Interpreting, Detecting and Mitigating Object Hallucinations via Attention Lens},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25004-25014}
}

raw_affiliation:
[{"affiliation": "National University of Defense Technology", "city": "Changsha", "country": "China"}, {"affiliation": "Southeast University", "city": "Nanjing", "country": "China"}, {"affiliation": "Key Laboratory of New Generation Artificial Intelligence Technology & Its Interdisciplinary Applications (Southeast University), Ministry of Education", "city": "Nanjing", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]

affiliations:
["National University of Defense Technology", "Southeast University", "Key Laboratory of New Generation Artificial Intelligence Technology & Its Interdisciplinary Applications (Southeast University), Ministry of Education", "Nanyang Technological University"]


title: SpectroMotion: Dynamic 3D Reconstruction of Specular Scenes
authors: Cheng-De Fan, Chen-Wei Chang, Yi-Ruei Liu, Jie-Ying Lee, Jiun-Long Huang, Yu-Chee Tseng, Yu-Lun Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21328-21338
affiliations: ["National Yang Ming Chiao Tung University", "University of Illinois Urbana-Champaign"]
cities: ["Hsinchu", "Urbana"]
countries: ["Taiwan", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fan_SpectroMotion_Dynamic_3D_Reconstruction_of_Specular_Scenes_CVPR_2025_paper.html

abstract:
We present SpectroMotion, a novel approach that combines 3D Gaussian Splatting (3DGS) with physically-based rendering (PBR) and deformation fields to reconstruct dynamic specular scenes. Previous methods extending 3DGS to model dynamic scenes have struggled to represent specular surfaces accurately. Our method addresses this limitation by introducing a residual correction technique for accurate surface normal computation during deformation, complemented by a deformable environment map that adapts to time-varying lighting conditions. We implement a coarse-to-fine training strategy significantly enhancing scene geometry and specular color prediction. It is the only existing 3DGS method capable of synthesizing photorealistic real-world dynamic specular scenes, outperforming state-of-the-art methods in rendering complex, dynamic, and specular scenes. Please see our project page at https://cdfan0627.github.io/spectromotion/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fan_SpectroMotion_Dynamic_3D_Reconstruction_of_Specular_Scenes_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fan_SpectroMotion_Dynamic_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fan_2025_CVPR,
    author    = {Fan, Cheng-De and Chang, Chen-Wei and Liu, Yi-Ruei and Lee, Jie-Ying and Huang, Jiun-Long and Tseng, Yu-Chee and Liu, Yu-Lun},
    title     = {SpectroMotion: Dynamic 3D Reconstruction of Specular Scenes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21328-21338}
}

raw_affiliation:
[{"affiliation": "National Yang Ming Chiao Tung University", "city": "Hsinchu", "country": "Taiwan"}, {"affiliation": "University of Illinois Urbana-Champaign", "city": "Urbana", "country": "USA"}]


title: Scaling Inference Time Compute for Diffusion Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2523-2534
affiliations: ["New York University", "MIT", "Google"]
cities: ["New York", "Cambridge", "Mountain View"]
countries: ["USA", "USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ma_Scaling_Inference_Time_Compute_for_Diffusion_Models_CVPR_2025_paper.html

authors:
Nanye Ma, Shangyuan Tong, Haolin Jia, Hexiang Hu, Yu-Chuan Su, Mingda Zhang, Xuan Yang, Yandong Li, Tommi Jaakkola, Xuhui Jia, Saining Xie

abstract:
Generative models have made significant impacts across various domains, largely due to their ability to scale during training by increasing data, computational resources, and model size, a phenomenon characterized by the scaling laws. Recent research has begun to explore inference-time scaling behavior in Large Language Models (LLMs), revealing how performance can further improve with additional computation during inference. Unlike LLMs, diffusion models inherently possess the flexibility to adjust inference-time computation via the number of denoising steps, although the performance gains typically flatten after a few dozen. In this work, we explore the inference-time scaling behavior of diffusion models beyond increasing denoising steps and investigate how the generation performance can further improve with increased computation. Specifically, we consider a search problem aimed at identifying better noises for the diffusion sampling process. We structure the design space along two axes: the verifiers used to provide feedback, and the algorithms used to find better noise candidates. Through extensive experiments on class-conditioned and text-conditioned image generation benchmarks, our findings reveal that increasing inference-time compute leads to substantial improvements in the quality of samples generated by diffusion models, and with the complicated nature of images, combinations of the components in the framework can be specifically chosen to conform with different application scenario.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_Scaling_Inference_Time_Compute_for_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ma_Scaling_Inference_Time_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ma_2025_CVPR,
    author    = {Ma, Nanye and Tong, Shangyuan and Jia, Haolin and Hu, Hexiang and Su, Yu-Chuan and Zhang, Mingda and Yang, Xuan and Li, Yandong and Jaakkola, Tommi and Jia, Xuhui and Xie, Saining},
    title     = {Scaling Inference Time Compute for Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2523-2534}
}

raw_affiliation:
[{"affiliation": "New York University", "city": "New York", "country": "USA"}, {"affiliation": "MIT", "city": "Cambridge", "country": "USA"}, {"affiliation": "Google", "city": "Mountain View", "country": "USA"}]


title: Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large Models
authors: Zichen Miao, Wei Chen, Qiang Qiu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20146-20157
raw_affiliation: [{"affiliation": "Purdue University", "city": "West Lafayette", "country": "USA"}]
affiliations: ["Purdue University"]
cities: ["West Lafayette"]
countries: ["USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Miao_Coeff-Tuning_A_Graph_Filter_Subspace_View_for_Tuning_Attention-Based_Large_CVPR_2025_paper.html

abstract:
Transformer-based large pre-trained models have shown remarkable generalization ability, and various parameter-efficient fine-tuning (PEFT) methods have been proposed to customize these models on downstream tasks with minimal computational and memory budgets. Previous PEFT methods are primarily designed from a tensor-decomposition perspective that tries to effectively tune the linear transformation by finding the smallest subset of parameters to train. Our study adopts an orthogonal view by representing the attention operation as a graph convolution and formulating the multi-head attention maps as a convolutional filter subspace, with each attention map as a subspace element. In this paper, we propose to tune the large pre-trained transformers by learning a small set of combination coefficients that construct a more expressive filter subspace from the original multi-head attention maps. We show analytically and experimentally that the tuned filter subspace can effectively expand the feature space of the multi-head attention and further enhance the capacity of transformers. We further stabilize the fine-tuning with a residual parameterization of the tunable subspace coefficients, and enhance the generalization with a regularization design by directly applying dropout on the tunable coefficient during training. The tunable coefficients take a tiny number of parameters and can be combined with previous PEFT methods in a plug-and-play manner. Extensive experiments show that our approach achieves superior performances than PEFT baselines with neglectable additional parameters.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Miao_Coeff-Tuning_A_Graph_Filter_Subspace_View_for_Tuning_Attention-Based_Large_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Miao_Coeff-Tuning_A_Graph_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Miao_2025_CVPR,
    author    = {Miao, Zichen and Chen, Wei and Qiu, Qiang},
    title     = {Coeff-Tuning: A Graph Filter Subspace View for Tuning Attention-Based Large Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20146-20157}
}


title: VTON 360: High-Fidelity Virtual Try-On from Any Viewing Direction
authors: Zijian He, Yuwei Ning, Yipeng Qin, Guangrun Wang, Sibei Yang, Liang Lin, Guanbin Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26388-26398
cities: ["Guangzhou", "Cardiff", "Shanghai", "Guangzhou", "Shenzhen"]
countries: ["China", "UK", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/He_VTON_360_High-Fidelity_Virtual_Try-On_from_Any_Viewing_Direction_CVPR_2025_paper.html

abstract:
Virtual Try-On (VTON) is a transformative technology in e-commerce and fashion design, enabling realistic digital visualization of clothing on individuals. In this work, we propose VTON 360, a novel 3D VTON method that addresses the open challenge of achieving high-fidelity VTON that supports any-view rendering. Specifically, we leverage the  equivalence  between a 3D model and its rendered multi-view 2D images, and reformulate 3D VTON as an extension of 2D VTON that ensures 3D consistent results across multiple views.To achieve this, we extend 2D VTON models to include multi-view garments and clothing-agnostic human body images as input, and propose several novel techniques to enhance them, including: i) a pseudo-3D pose representation using normal maps derived from the SMPL-X 3D human model, ii) a multi-view spatial attention mechanism that models the correlations between features from different viewing angles, and iii) a multi-view CLIP embedding that enhances the garment CLIP features used in 2D VTON with camera information. Extensive experiments on large-scale real datasets and clothing images from e-commerce platforms demonstrate the effectiveness of our approach.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/He_VTON_360_High-Fidelity_Virtual_Try-On_from_Any_Viewing_Direction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/He_VTON_360_High-Fidelity_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{He_2025_CVPR,
    author    = {He, Zijian and Ning, Yuwei and Qin, Yipeng and Wang, Guangrun and Yang, Sibei and Lin, Liang and Li, Guanbin},
    title     = {VTON 360: High-Fidelity Virtual Try-On from Any Viewing Direction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26388-26398}
}

raw_affiliation:
[{"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Cardiff University", "city": "Cardiff", "country": "UK"}, {"affiliation": "ShanghaiTech University", "city": "Shanghai", "country": "China"}, {"affiliation": "Guangdong Key Laboratory of Big Data Analysis and Processing", "city": "Guangzhou", "country": "China"}, {"affiliation": "Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}]

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


title: MVBoost: Boost 3D Reconstruction with Multi-View Refinement
authors: Xiangyu Liu, Xiaomei Zhang, Zhiyuan Ma, Xiangyu Zhu, Zhen Lei
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21664-21673
cities: ["Beijing", "Beijing", "Hong Kong", "Hong Kong", "Macau"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_MVBoost_Boost_3D_Reconstruction_with_Multi-View_Refinement_CVPR_2025_paper.html

abstract:
Recent advancements in 3D object reconstruction have been remarkable, yet most current 3D models rely heavily on existing 3D datasets. The scarcity of diverse 3D datasets results in limited generalization capabilities of 3D reconstruction models. In this paper, we propose a novel framework for boosting 3D reconstruction with multi-view refinement (MVBoost) by generating pseudo-GT data. The key of MVBoost is combining the advantages of the high accuracy of the multi-view generation model and the consistency of the 3D reconstruction model to create a reliable data source. Specifically, given a single-view input image, we employ a multi-view diffusion model to generate multiple views, followed by a large 3D reconstruction model to produce consistent 3D data. MVBoost then adaptively refines these multi-view images, rendered from the consistent 3D data, to build a large-scale multi-view dataset for training a feed-forward 3D reconstruction model. Additionally, the input view optimization is designed to optimize the corresponding viewpoints based on the user's input image, ensuring that the most important viewpoint is accurately tailored to the user's needs. Extensive evaluations demonstrate that our method achieves superior reconstruction results and robust generalization compared to prior works.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_MVBoost_Boost_3D_Reconstruction_with_Multi-View_Refinement_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_MVBoost_Boost_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Xiangyu and Zhang, Xiaomei and Ma, Zhiyuan and Zhu, Xiangyu and Lei, Zhen},
    title     = {MVBoost: Boost 3D Reconstruction with Multi-View Refinement},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21664-21673}
}

raw_affiliation:
[{"affiliation": "MAIS, Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Artificial Intelligence, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "China"}, {"affiliation": "CAIR, HKISI, CAS", "city": "Hong Kong", "country": "China"}, {"affiliation": "School of Computer Science and Engineering, FIE, M.U.S.T", "city": "Macau", "country": "China"}]

affiliations:
["MAIS, Institute of Automation, Chinese Academy of Sciences", "School of Artificial Intelligence, University of Chinese Academy of Sciences", "The Hong Kong Polytechnic University", "CAIR, HKISI, CAS", "School of Computer Science and Engineering, FIE, M.U.S.T"]


title: Chat-based Person Retrieval via Dialogue-Refined Cross-Modal Alignment
authors: Yang Bai, Yucheng Ji, Min Cao, Jinqiao Wang, Mang Ye
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3952-3962
cities: ["Wuhan", "Wuhan", "Suzhou", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bai_Chat-based_Person_Retrieval_via_Dialogue-Refined_Cross-Modal_Alignment_CVPR_2025_paper.html

abstract:
Traditional text-based person retrieval (TPR) relies on a single-shot text as query to retrieve the target person, assuming that the query completely captures the user's search intent. However, in real-world scenarios, it can be challenging to ensure the information completeness of such single-shot text. To address this limitation, we propose chat-based person retrieval (ChatPR), a new paradigm that takes an interactive dialogue as query to perform the person retrieval, engaging the user in conversational context to progressively refine the query for accurate person retrieval. The primary challenge in ChatPR is the lack of available dialogue-image paired data. To overcome this challenge, we establish ChatPedes, the first dataset designed for ChatPR, which is constructed by leveraging large language models to automate the question generation and simulate user responses. Additionally, to bridge the modality gap between dialogues and images, we propose a dialogue-refined cross-modal alignment (DiaNA) framework, which leverages two adaptive attribute refiners to bottleneck the conversational and visual information for fine-grained cross-modal alignment. Moreover, we propose a dialogue-specific data augmentation strategy, random round retaining, to further enhance the model's generalization ability across varying dialogue lengths. Extensive experiments demonstrate that DiaNA significantly outperforms existing TPR approaches, highlighting the effectiveness of conversational interactions for person retrieval.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bai_Chat-based_Person_Retrieval_via_Dialogue-Refined_Cross-Modal_Alignment_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bai_Chat-based_Person_Retrieval_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bai_2025_CVPR,
    author    = {Bai, Yang and Ji, Yucheng and Cao, Min and Wang, Jinqiao and Ye, Mang},
    title     = {Chat-based Person Retrieval via Dialogue-Refined Cross-Modal Alignment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3952-3962}
}

raw_affiliation:
[{"affiliation": "National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Wuhan AI Research", "city": "Wuhan", "country": "China"}, {"affiliation": "School of Computer Science and Technology, Soochow University", "city": "Suzhou", "country": "China"}, {"affiliation": "Institute of Automation, Chinese Academy of Science", "city": "Beijing", "country": "China"}]

affiliations:
["National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University", "Wuhan AI Research", "School of Computer Science and Technology, Soochow University", "Institute of Automation, Chinese Academy of Science"]


title: Category-Agnostic Neural Object Rigging
authors: Guangzhao He, Chen Geng, Shangzhe Wu, Jiajun Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22078-22088
affiliations: ["Stanford University", "University of Cambridge"]
cities: ["Stanford", "Cambridge"]
countries: ["USA", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/He_Category-Agnostic_Neural_Object_Rigging_CVPR_2025_paper.html

abstract:
The motion of deformable 4D objects lies in a low-dimensional manifold. To better capture the low dimensionality and enable better controllability, traditional methods have devised several heuristic-based methods, i.e., rigging, for manipulating dynamic objects in an intuitive fashion. However, such representations are not scalable due to the need for expert knowledge of specific categories. Instead, we study the automatic exploration of such low-dimensional structures in a purely data-driven manner. Specifically, we design a novel representation that encodes deformable 4D objects into a sparse set of spatially grounded blobs and an instance-aware feature volume to disentangle the pose and instance information of the 3D shape. With such a representation, we can manipulate the pose of 3D objects intuitively by modifying the parameters of the blobs, while preserving rich instance-specific information. We evaluate the proposed method on a variety of object categories and demonstrate the effectiveness of the proposed framework. Project page: https://guangzhaohe.com/canor.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/He_Category-Agnostic_Neural_Object_Rigging_CVPR_2025_paper.pdf

bibtex:
@InProceedings{He_2025_CVPR,
    author    = {He, Guangzhao and Geng, Chen and Wu, Shangzhe and Wu, Jiajun},
    title     = {Category-Agnostic Neural Object Rigging},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22078-22088}
}

raw_affiliation:
[{"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "University of Cambridge", "city": "Cambridge", "country": "UK"}]


authors: Xuecheng Wu, Heli Sun, Yifan Wang, Jiayu Nie, Jie Zhang, Yabing Wang, Junxiao Xue, Liang He
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9142-9153
cities: ["Xi'an", "Hefei", "Singapore", "Xi'an", "Hangzhou"]
countries: ["China", "China", "Singapore", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_AVF-MAE_Scaling_Affective_Video_Facial_Masked_Autoencoders_via_Efficient_Audio-Visual_CVPR_2025_paper.html

title:
AVF-MAE++: Scaling Affective Video Facial Masked Autoencoders via Efficient Audio-Visual Self-Supervised Learning

abstract:
Affective Video Facial Analysis (AVFA) is important for advancing emotion-aware AI, yet the persistent data scarcity in AVFA presents challenges. Recently, the self-supervised learning (SSL) technique of Masked Autoencoders (MAE) has gained significant attention, particularly in its audio-visual adaptation. Insights from general domains suggest that scaling is vital for unlocking impressive improvements, though its effects on AVFA remain largely unexplored. Additionally, capturing both intra- and inter-modal correlations through scalable representations is a crucial challenge in this field. To tackle these gaps, we introduce AVF-MAE++, a series audio-visual MAE designed to explore the impact of scaling on AVFA with a focus on advanced correlation modeling. Our method incorporates a novel audio-visual dual masking strategy and an improved modality encoder with a holistic view to better support scalable pre-training. Furthermore, we propose the Iteratively Audio-Visual Correlations Learning Module to improve correlations capture within the SSL framework, bridging the limitations of prior methods. To support smooth adaptation and mitigate overfitting, we also introduce a progressive semantics injection strategy, which structures training in three stages. Extensive experiments across 17 datasets, spanning three key AVFA tasks, demonstrate the superior performance of AVF-MAE++, establishing new state-of-the-art outcomes. Ablation studies provide further insights into the critical design choices driving these gains. Code is released at https://github.com/XuecWu/AVF-MAE.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_AVF-MAE_Scaling_Affective_Video_Facial_Masked_Autoencoders_via_Efficient_Audio-Visual_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_AVF-MAE_Scaling_Affective_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Xuecheng and Sun, Heli and Wang, Yifan and Nie, Jiayu and Zhang, Jie and Wang, Yabing and Xue, Junxiao and He, Liang},
    title     = {AVF-MAE++: Scaling Affective Video Facial Masked Autoencoders via Efficient Audio-Visual Self-Supervised Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9142-9153}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "CFAR and IHPC, A*STAR", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "Zhejiang Lab", "city": "Hangzhou", "country": "China"}]

affiliations:
["School of Computer Science and Technology, Xi\u2019an Jiaotong University", "University of Science and Technology of China", "CFAR and IHPC, A*STAR", "Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University", "Zhejiang Lab"]


title: POPEN: Preference-Based Optimization and Ensemble for LVLM-Based Reasoning Segmentation
authors: Lanyun Zhu, Tianrun Chen, Qianxiong Xu, Xuanyi Liu, Deyi Ji, Haiyang Wu, De Wen Soh, Jun Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30231-30240
cities: ["Singapore", "Shenzhen", "Hangzhou", "Singapore", "Beijing", "Lancaster"]
countries: ["Singapore", "China", "China", "Singapore", "China", "United Kingdom"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_POPEN_Preference-Based_Optimization_and_Ensemble_for_LVLM-Based_Reasoning_Segmentation_CVPR_2025_paper.html

abstract:
Existing LVLM-based reasoning segmentation methods often suffer from imprecise segmentation results and hallucinations in their text responses. This paper introduces POPEN, a novel framework designed to address these issues and achieve improved results. POPEN includes a preference-based optimization method to finetune the LVLM, aligning it more closely with human preferences and thereby generating better text responses and segmentation results. Additionally, POPEN introduces a preference-based ensemble method for inference, which integrates multiple outputs from the LVLM using a preference-score-based attention mechanism for refinement. To better adapt to the segmentation task, we incorporate several task-specific designs in our POPEN framework, including a new approach for collecting segmentation preference data with a curriculum learning mechanism, and a novel preference optimization loss to refine the segmentation capability of the LVLM. Experiments demonstrate that our method achieves state-of-the-art performance in reasoning segmentation, exhibiting minimal hallucination in text responses and the highest segmentation accuracy compared to previous advanced methods like LISA and PixelLM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_POPEN_Preference-Based_Optimization_and_Ensemble_for_LVLM-Based_Reasoning_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_POPEN_Preference-Based_Optimization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Lanyun and Chen, Tianrun and Xu, Qianxiong and Liu, Xuanyi and Ji, Deyi and Wu, Haiyang and Soh, De Wen and Liu, Jun},
    title     = {POPEN: Preference-Based Optimization and Ensemble for LVLM-Based Reasoning Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30231-30240}
}

raw_affiliation:
[{"affiliation": "Singapore University of Technology and Design", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Tencent", "city": "Shenzhen", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Lancaster University", "city": "Lancaster", "country": "United Kingdom"}]

affiliations:
["Singapore University of Technology and Design", "Tencent", "Zhejiang University", "Nanyang Technological University", "Peking University", "Lancaster University"]


title: DiffusionSfM: Predicting Structure and Motion via Ray Origin and Endpoint Diffusion
authors: Qitao Zhao, Amy Lin, Jeff Tan, Jason Y. Zhang, Deva Ramanan, Shubham Tulsiani
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6317-6326
raw_affiliation: [{"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}]
affiliations: ["Carnegie Mellon University"]
cities: ["Pittsburgh"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_DiffusionSfM_Predicting_Structure_and_Motion_via_Ray_Origin_and_Endpoint_CVPR_2025_paper.html

abstract:
Current Structure-from-Motion (SfM) methods typically follow a two-stage pipeline, combining learned or geometric pairwise reasoning with a subsequent global optimization step. In contrast, we propose a data-driven multi-view reasoning approach that directly infers 3D scene geometry and camera poses from multi-view images. Our framework, DiffusionSfM, parameterizes scene geometry and cameras as pixel-wise ray origins and endpoints in a global frame and employs a transformer-based denoising diffusion model to predict them from multi-view inputs. To address practical challenges in training diffusion models with missing data and unbounded scene coordinates, we introduce specialized mechanisms that ensure robust learning. We empirically validate DiffusionSfM on both synthetic and real datasets, demonstrating that it outperforms classical and learning-based approaches while naturally modeling uncertainty.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_DiffusionSfM_Predicting_Structure_and_Motion_via_Ray_Origin_and_Endpoint_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_DiffusionSfM_Predicting_Structure_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Qitao and Lin, Amy and Tan, Jeff and Zhang, Jason Y. and Ramanan, Deva and Tulsiani, Shubham},
    title     = {DiffusionSfM: Predicting Structure and Motion via Ray Origin and Endpoint Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6317-6326}
}


title: GroundingFace: Fine-grained Face Understanding via Pixel Grounding Multimodal Large Language Model
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3942-3951
cities: ["Hangzhou", "Shenzhen", "Beijing", "Hangzhou"]
countries: ["China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Han_GroundingFace_Fine-grained_Face_Understanding_via_Pixel_Grounding_Multimodal_Large_Language_CVPR_2025_paper.html

authors:
Yue Han, Jiangning Zhang, Junwei Zhu, Runze Hou, Xiaozhong Ji, Chuming Lin, Xiaobin Hu, Zhucun Xue, Yong Liu

abstract:
Multimodal Language Learning Models (MLLMs) have shown remarkable performance in image understanding, generation, and editing, with recent advancements achieving pixel-level grounding with reasoning. However, these models for common objects struggle with fine-grained face understanding. In this work, we introduce the FacePlayGround-240K dataset, the first pioneering large-scale, pixel-grounded face caption and question-answer (QA) dataset, meticulously curated for alignment pretraining and instruction-tuning. We present the GroundingFace framework, specifically designed to enhance fine-grained face understanding. This framework significantly augments the capabilities of existing grounding models in face part segmentation, face attribute comprehension, while preserving general scene understanding. Comprehensive experiments validate that our approach surpasses current state-of-the-art models in pixel-grounded face captioning/QA and various downstream tasks, including face captioning, referring segmentation, and zero-shot face attribute recognition.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Han_GroundingFace_Fine-grained_Face_Understanding_via_Pixel_Grounding_Multimodal_Large_Language_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Han_2025_CVPR,
    author    = {Han, Yue and Zhang, Jiangning and Zhu, Junwei and Hou, Runze and Ji, Xiaozhong and Lin, Chuming and Hu, Xiaobin and Xue, Zhucun and Liu, Yong},
    title     = {GroundingFace: Fine-grained Face Understanding via Pixel Grounding Multimodal Large Language Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3942-3951}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Youtu Lab, Tencent", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Laboratory of Industrial Control Technology", "city": "Hangzhou", "country": "China"}]

affiliations:
["Zhejiang University", "Youtu Lab, Tencent", "Tsinghua University", "State Key Laboratory of Industrial Control Technology"]


title: Self-Supervised Cross-View Correspondence with Predictive Cycle Consistency
authors: Alan Baade, Changan Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16753-16763
affiliations: ["The University of Texas at Austin", "Stanford University"]
cities: ["Austin", "Stanford"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Baade_Self-Supervised_Cross-View_Correspondence_with_Predictive_Cycle_Consistency_CVPR_2025_paper.html

abstract:
Learning self-supervised visual correspondence is a long-studied task fundamental to visual understanding and human perception. However, existing correspondence methods largely focus on small image transformations, such as object tracking in high-framerate videos or learning pixel-to-pixel mappings between images with high view overlap. This severely limits their application in dynamic multi-view settings such as robot imitation learning. In this work, we introduce Predictive Cycle Consistency for learning object correspondence between extremely disjoint views of a scene without paired segmentation data. Our technique bootstraps object correspondence pseudolabels from raw image segmentations using conditional grayscale colorization and a cycle-consistency refinement prior. We then train deep ViTs on these pseudolabels, which we use to generate higher-quality pseudolabels and iteratively train better correspondence models. We demonstrate the performance of our method under both extreme in-the-wild camera view changes and across large temporal gaps in video. Our approach beats all prior supervised and prior SoTA self-supervised correspondence models on the EgoExo4D correspondence benchmark (+6.7 IoU Exo Query) and the prior SoTA self-supervised methods SiamMAE and DINO V1&V2 on the DAVIS-2017 and LVOS datasets across large frame gaps.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Baade_Self-Supervised_Cross-View_Correspondence_with_Predictive_Cycle_Consistency_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Baade_Self-Supervised_Cross-View_Correspondence_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Baade_2025_CVPR,
    author    = {Baade, Alan and Chen, Changan},
    title     = {Self-Supervised Cross-View Correspondence with Predictive Cycle Consistency},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16753-16763}
}

raw_affiliation:
[{"affiliation": "The University of Texas at Austin", "city": "Austin", "country": "USA"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}]


title: MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis
authors: Ho Kei Cheng, Masato Ishii, Akio Hayakawa, Takashi Shibuya, Alexander Schwing, Yuki Mitsufuji
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28901-28911
affiliations: ["University of Illinois Urbana-Champaign", "Sony AI", "Sony Group Corporation"]
cities: ["Urbana-Champaign", "Tokyo", "Tokyo"]
countries: ["USA", "Japan", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cheng_MMAudio_Taming_Multimodal_Joint_Training_for_High-Quality_Video-to-Audio_Synthesis_CVPR_2025_paper.html

abstract:
We propose to synthesize high-quality and synchronized audio, given video and optional text conditions, using a novel multimodal joint training framework (MMAudio). In contrast to single-modality training conditioned on (limited) video data only, MMAudio is jointly trained with larger-scale, readily available text-audio data to learn to generate semantically aligned high-quality audio samples. Additionally, we improve audio-visual synchrony with a conditional synchronization module that aligns video conditions with audio latents at the frame level. Trained with a flow matching objective, MMAudio achieves new video-to-audio state-of-the-art among public models in terms of audio quality, semantic alignment, and audio-visual synchronization, while having a low inference time (1.23s to generate an 8s clip) and just 157M parameters. MMAudio also achieves surprisingly competitive performance in text-to-audio generation, showing that joint training does not hinder single-modality performance. Code, models, and demo are available at: hkchengrex.github.io/MMAudio

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cheng_MMAudio_Taming_Multimodal_Joint_Training_for_High-Quality_Video-to-Audio_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cheng_MMAudio_Taming_Multimodal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cheng_2025_CVPR,
    author    = {Cheng, Ho Kei and Ishii, Masato and Hayakawa, Akio and Shibuya, Takashi and Schwing, Alexander and Mitsufuji, Yuki},
    title     = {MMAudio: Taming Multimodal Joint Training for High-Quality Video-to-Audio Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28901-28911}
}

raw_affiliation:
[{"affiliation": "University of Illinois Urbana-Champaign", "city": "Urbana-Champaign", "country": "USA"}, {"affiliation": "Sony AI", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Sony Group Corporation", "city": "Tokyo", "country": "Japan"}]


title: CryptoFace: End-to-End Encrypted Face Recognition
authors: Wei Ao, Vishnu Naresh Boddeti
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19197-19206
raw_affiliation: [{"affiliation": "Michigan State University", "city": "East Lansing", "country": "USA"}]
affiliations: ["Michigan State University"]
cities: ["East Lansing"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ao_CryptoFace_End-to-End_Encrypted_Face_Recognition_CVPR_2025_paper.html

abstract:
Face recognition is central to many authentication, security, and personalized applications. Yet, it suffers from significant privacy risks, particularly arising from unauthorized access to sensitive biometric data. This paper introduces CryptoFace, the first end-to-end encrypted face recognition system with fully homomorphic encryption (FHE). It enables secure processing of facial data across all stages of a face-recognition process--feature extraction, storage, and matching--without exposing raw images or features. We introduce a mixture of shallow patch convolutional networks to support higher-dimensional tensors via patch-based processing while reducing the multiplicative depth and thus inference latency. Parallel FHE evaluation of these networks ensures near-resolution-independent latency. On standard face recognition benchmarks, CryptoFace significantly accelerates inference and increases verification accuracy compared to the state-of-the-art FHE neural networks adapted for face recognition. CryptoFace will facilitate secure face recognition systems requiring robust and provable security. The code is available at https://github.com/human-analysis/CryptoFace.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ao_CryptoFace_End-to-End_Encrypted_Face_Recognition_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Ao_2025_CVPR,
    author    = {Ao, Wei and Boddeti, Vishnu Naresh},
    title     = {CryptoFace: End-to-End Encrypted Face Recognition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19197-19206}
}


title: Relation-Rich Visual Document Generator for Visual Information Extraction
authors: Zi-Han Jiang, Chien-Wei Lin, Wei-Hua Li, Hsuan-Tung Liu, Yi-Ren Yeh, Chu-Song Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14449-14459
cities: ["Taipei", "Taipei", "Kaohsiung"]
countries: ["Taiwan", "Taiwan", "Taiwan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_Relation-Rich_Visual_Document_Generator_for_Visual_Information_Extraction_CVPR_2025_paper.html

abstract:
Despite advances in Large Language Models (LLMs) and Multimodal LLMs (MLLMs) for visual document understanding (VDU), visual information extraction (VIE) from relation-rich documents remains challenging due to the layout diversity and limited training data. While existing synthetic document generators attempt to address data scarcity, they either rely on manually designed layouts and templates, or adopt rule-based approaches that limit layout diversity. Besides, current layout generation methods focus solely on topological patterns without considering textual content, making them impractical for generating documents with complex associations between the contents and layouts. In this paper, we propose a Relation-rIch visual Document GEnerator (RIDGE) that addresses these limitations through a two-stage approach: (1) Content Generation, which leverages LLMs to generate document content using a carefully designed Hierarchical Structure Text format which captures entity categories and relationships, and (2) Content-driven Layout Generation, which learns to create diverse, plausible document layouts solely from easily available Optical Character Recognition (OCR) results, requiring no human labeling or annotations efforts. Experimental results have demonstrated that our method significantly enhances the performance of document understanding models on various VIE benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_Relation-Rich_Visual_Document_Generator_for_Visual_Information_Extraction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_Relation-Rich_Visual_Document_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Zi-Han and Lin, Chien-Wei and Li, Wei-Hua and Liu, Hsuan-Tung and Yeh, Yi-Ren and Chen, Chu-Song},
    title     = {Relation-Rich Visual Document Generator for Visual Information Extraction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14449-14459}
}

raw_affiliation:
[{"affiliation": "National Taiwan University", "city": "Taipei", "country": "Taiwan"}, {"affiliation": "E.SUN Financial Holding Co., Ltd.", "city": "Taipei", "country": "Taiwan"}, {"affiliation": "National Kaohsiung Normal University", "city": "Kaohsiung", "country": "Taiwan"}]

affiliations:
["National Taiwan University", "E.SUN Financial Holding Co., Ltd.", "National Kaohsiung Normal University"]


title: DynFocus: Dynamic Cooperative Network Empowers LLMs with Video Understanding
authors: Yudong Han, Qingpei Guo, Liyuan Pan, Liu Liu, Yu Guan, Ming Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8512-8522
affiliations: ["Beijing Institute of Technology", "Ant Group", "KooMap Dept., Huawei", "University of Warwick"]
cities: ["Beijing", "Hangzhou", "Shenzhen", "Coventry"]
countries: ["China", "China", "China", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Han_DynFocus_Dynamic_Cooperative_Network_Empowers_LLMs_with_Video_Understanding_CVPR_2025_paper.html

abstract:
The challenge in LLM-based video understanding lies in preserving visual and semantic information in long videos while maintaining a memory-affordable token count. However, redundancy and correspondence in videos have hindered the performance potential of existing methods. Through statistical learning on current datasets, we observe that redundancy occurs in both repeated and answer-irrelevant frames, and the corresponding frames vary with different questions. This suggests the possibility of adopting dynamic encoding to balance detailed video information preservation with token budget reduction. To this end, we propose a dynamic cooperative network, DynFocus, for memory-efficient video encoding in this paper. Specifically, i) a Dynamic Event Prototype Estimation (DPE) module to dynamically select meaningful frames for question answering; (ii) a Compact Cooperative Encoding (CCE) module that encodes meaningful frames with detailed visual appearance and the remaining frames with sketchy perception separately. We evaluate our method on five publicly available benchmarks, and experimental results consistently demonstrate that our method achieves competitive performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Han_DynFocus_Dynamic_Cooperative_Network_Empowers_LLMs_with_Video_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Han_DynFocus_Dynamic_Cooperative_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Han_2025_CVPR,
    author    = {Han, Yudong and Guo, Qingpei and Pan, Liyuan and Liu, Liu and Guan, Yu and Yang, Ming},
    title     = {DynFocus: Dynamic Cooperative Network Empowers LLMs with Video Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8512-8522}
}

raw_affiliation:
[{"affiliation": "Beijing Institute of Technology", "city": "Beijing", "country": "China"}, {"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "KooMap Dept., Huawei", "city": "Shenzhen", "country": "China"}, {"affiliation": "University of Warwick", "city": "Coventry", "country": "UK"}]


title: Mimic In-Context Learning for Multimodal Tasks
authors: Yuchu Jiang, Jiale Fu, Chenduo Hao, Xinting Hu, Yingzhe Peng, Xin Geng, Xu Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29825-29835
cities: ["Nanjing", "Nanjing", "Singapore"]
countries: ["China", "China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_Mimic_In-Context_Learning_for_Multimodal_Tasks_CVPR_2025_paper.html

abstract:
Recently, In-context Learning (ICL) has become a significant inference paradigm in Large Multimodal Models (LMMs), utilizing a few in-context demonstrations (ICDs) to prompt LMMs for new tasks. However, the synergistic effects in multimodal data increase the sensitivity of ICL performance to the configurations of ICDs, stimulating the need for a more stable and general mapping function. Mathematically, in Transformer-based models, ICDs act as "shift vectors" added to the hidden states of query tokens. Inspired by this, we introduce Mimic In-Context Learning (MimIC) to learn stable and generalizable shift effects from ICDs. Specifically, compared with some previous shift vector-based methods, MimIC more strictly approximates the shift effects by integrating lightweight learnable modules into LMMs with four key enhancements: 1) inserting shift vectors after attention layers, 2) assigning a shift vector to each attention head, 3) making shift magnitude query-dependent, and 4) employing a layer-wise alignment loss. Extensive experiments on two LMMs (Idefics-9b and Idefics2-8b-base) across three multimodal tasks (VQAv2, OK-VQA, Captioning) demonstrate that MimIC outperforms existing shift vector-based methods. The code is available at https://anonymous.4open.science/r/MimIC/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_Mimic_In-Context_Learning_for_Multimodal_Tasks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_Mimic_In-Context_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Yuchu and Fu, Jiale and Hao, Chenduo and Hu, Xinting and Peng, Yingzhe and Geng, Xin and Yang, Xu},
    title     = {Mimic In-Context Learning for Multimodal Tasks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29825-29835}
}

raw_affiliation:
[{"affiliation": "Southeast University", "city": "Nanjing", "country": "China"}, {"affiliation": "Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, (Southeast University), Ministry of Education", "city": "Nanjing", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]

affiliations:
["Southeast University", "Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, (Southeast University), Ministry of Education", "Nanyang Technological University"]


title: PromptHash:Affinity-Prompted Collaborative Cross-Modal Learning for Adaptive Hashing Retrieval
authors: Qiang Zou, Shuli Cheng, Jiayi Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19649-19658
affiliations: ["School of Computer Science and Technology, Xinjiang University, China"]
cities: ["Urumqi"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zou_PromptHashAffinity-Prompted_Collaborative_Cross-Modal_Learning_for_Adaptive_Hashing_Retrieval_CVPR_2025_paper.html

abstract:
Cross-modal hashing is a promising approach for efficient data retrieval and storage optimization. However, contemporary methods exhibit significant limitations in semantic preservation, contextual integrity, and information redundancy, which constrains retrieval efficacy. We present PromptHash, an innovative framework leveraging affinity prompt-aware collaborative learning for adaptive cross-modal hashing. We propose an end-to-end framework for affinity-prompted collaborative hashing, with the following fundamental technical contributions: (i) a text affinity prompt learning mechanism that preserves contextual information while maintaining parameter efficiency, (ii) an adaptive gated selection fusion architecture that synthesizes State Space Model with Transformer network for precise cross-modal feature integration, and (iii) a prompt affinity alignment strategy that bridges modal heterogeneity through hierarchical contrastive learning. To the best of our knowledge, this study presents the first investigation into affinity prompt awareness within collaborative cross-modal adaptive hash learning, establishing a paradigm for enhanced semantic consistency across modalities. Through comprehensive evaluation on three benchmark multi-label datasets, PromptHash demonstrates substantial performance improvements over existing approaches. Notably, on the NUS-WIDE dataset, our method achieves significant gains of 18.22% and 18.65% in image-to-text and text-to-image retrieval tasks, respectively. The code is publicly available at https://github.com/ShiShuMo/PromptHash.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zou_PromptHashAffinity-Prompted_Collaborative_Cross-Modal_Learning_for_Adaptive_Hashing_Retrieval_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zou_2025_CVPR,
    author    = {Zou, Qiang and Cheng, Shuli and Chen, Jiayi},
    title     = {PromptHash:Affinity-Prompted Collaborative Cross-Modal Learning for Adaptive Hashing Retrieval},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19649-19658}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology, Xinjiang University, China", "city": "Urumqi", "country": "China"}]


title: V-Stylist: Video Stylization via Collaboration and Reflection of MLLM Agents
authors: Zhengrong Yue, Shaobin Zhuang, Kunchang Li, Yanbo Ding, Yali Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3195-3205
cities: ["Shanghai", "Shanghai", "Shenzhen", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yue_V-Stylist_Video_Stylization_via_Collaboration_and_Reflection_of_MLLM_Agents_CVPR_2025_paper.html

abstract:
Despite the recent advancement in video stylization, most existing methods struggle to render any video with complex transitions,based on an open style description of user query.To fill this gap,we introduce a generic multi-agent system for video stylization, V-Stylist, by a novel collaboration and reflection paradigm of multi-modal large language models. Specifically, our V-Stylist is a systematical workflow with three key roles: (1) Video Parser decomposes the input video into a number of shots and generates their text prompts of key shot content.Via a concise video-to-shot prompting paradigm,it allows our V-Stylist to effectively handle videos with complex transitions. (2) Style Parser identifies the style in the user query and progressively search the matched style model from a style tree.Via a robust tree-of-thought searching paradigm,it allows our V-Stylist to precisely specify vague style preference in the open user query.(3) Style Artist leverages the matched model to render all the video shots into the required style.Via a novel multi-round self-reflection paradigm,it allows our V-Stylist to adaptively adjust detail control,according to the style requirement.With such a distinct design of mimicking human professionals, our V-Stylist achieves a major breakthrough over the primary challenges for effective and automatic video stylization. Moreover,  we further construct a new benchmark Text-driven Video Stylization Benchmark (TVSBench),which fills the gap to assess stylization of complex videos on open user queries. Extensive experiments show that, V-Stylist achieves the state-of-the-art,e.g.,V-Stylist surpasses FRESCO and ControlVideo by 6.05% and 4.51% respectively in overall average metrics, marking a significant advance in video stylization.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yue_V-Stylist_Video_Stylization_via_Collaboration_and_Reflection_of_MLLM_Agents_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yue_V-Stylist_Video_Stylization_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Yue_2025_CVPR,
    author    = {Yue, Zhengrong and Zhuang, Shaobin and Li, Kunchang and Ding, Yanbo and Wang, Yali},
    title     = {V-Stylist: Video Stylization via Collaboration and Reflection of MLLM Agents},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3195-3205}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao T ong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Shenzhen Institutes of Advanced T echnology , Chinese Academy of Sciences", "city": "Shenzhen", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["Shanghai Jiao T ong University", "Shanghai AI Laboratory", "Shenzhen Institutes of Advanced T echnology , Chinese Academy of Sciences", "University of Chinese Academy of Sciences"]


title: Vision-Language Models Do Not Understand Negation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29612-29622
affiliations: ["MIT", "OpenAI", "University of Oxford"]
cities: ["Cambridge", "San Francisco", "Oxford"]
countries: ["USA", "USA", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Alhamoud_Vision-Language_Models_Do_Not_Understand_Negation_CVPR_2025_paper.html

authors:
Kumail Alhamoud, Shaden Alshammari, Yonglong Tian, Guohao Li, Philip H.S. Torr, Yoon Kim, Marzyeh Ghassemi

abstract:
Many practical vision-language applications require models that understand negation, e.g., when using natural language to retrieve images which contain certain objects but not others. Despite advancements in vision-language models (VLMs) through large-scale training, their ability to comprehend negation remains underexplored. This study addresses the question: how well do current VLMs understand negation? We introduce \benchmark  , a new benchmark designed to evaluate negation understanding across 18 task variations and 79k examples spanning image, video, and medical datasets. The benchmark consists of two core tasks designed to evaluate negation understanding in diverse multimodal settings: Retrieval with Negation and Multiple Choice Questions with Negated Captions. Our evaluation reveals that modern VLMs struggle significantly with negation, often performing at chance level. To address these shortcomings, we explore a data-centric approach wherein we finetune CLIP models on large-scale synthetic datasets containing millions of negated captions. We show that this approach can result in  a 10% increase in recall on negated queries and a 28% boost in accuracy on multiple-choice questions with negated captions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Alhamoud_Vision-Language_Models_Do_Not_Understand_Negation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Alhamoud_Vision-Language_Models_Do_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Alhamoud_2025_CVPR,
    author    = {Alhamoud, Kumail and Alshammari, Shaden and Tian, Yonglong and Li, Guohao and Torr, Philip H.S. and Kim, Yoon and Ghassemi, Marzyeh},
    title     = {Vision-Language Models Do Not Understand Negation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29612-29622}
}

raw_affiliation:
[{"affiliation": "MIT", "city": "Cambridge", "country": "USA"}, {"affiliation": "OpenAI", "city": "San Francisco", "country": "USA"}, {"affiliation": "University of Oxford", "city": "Oxford", "country": "UK"}]


title: ID-Patch: Robust ID Association for Group Photo Personalization
authors: Yimeng Zhang, Tiancheng Zhi, Jing Liu, Shen Sang, Liming Jiang, Qing Yan, Sijia Liu, Linjie Luo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2986-2996
affiliations: ["ByteDance Inc.", "Michigan State University"]
cities: ["Beijing", "East Lansing"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_ID-Patch_Robust_ID_Association_for_Group_Photo_Personalization_CVPR_2025_paper.html

abstract:
The ability to synthesize personalized group photos and specify the positions of each identity offers immense creative potential. While such imagery can be visually appealing, it presents significant challenges for existing technologies. A persistent issue is identity (ID) leakage, where injected facial features interfere with one another, resulting in low face resemblance, incorrect positioning, and visual artifacts. Existing methods suffer from limitations such as the reliance on segmentation models, increased runtime, or a high probability of ID leakage. To address these challenges, we propose ID-Patch, a novel method that provides robust association between identities and 2D positions. Our approach generates an ID patch and ID embeddings from the same facial features: the ID patch is positioned on the conditional image for precise spatial control, while the ID embeddings integrate with text embeddings to ensure high resemblance. Experimental results demonstrate that ID-Patch surpasses baseline methods across metrics, such as face ID resemblance, ID-position association accuracy, and generation efficiency. Project Page is: https://byteaigc.github.io/ID-Patch/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_ID-Patch_Robust_ID_Association_for_Group_Photo_Personalization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_ID-Patch_Robust_ID_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Yimeng and Zhi, Tiancheng and Liu, Jing and Sang, Shen and Jiang, Liming and Yan, Qing and Liu, Sijia and Luo, Linjie},
    title     = {ID-Patch: Robust ID Association for Group Photo Personalization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2986-2996}
}

raw_affiliation:
[{"affiliation": "ByteDance Inc.", "city": "Beijing", "country": "China"}, {"affiliation": "Michigan State University", "city": "East Lansing", "country": "USA"}]


title: iG-6DoF: Model-free 6DoF Pose Estimation for Unseen Object via Iterative 3D Gaussian Splatting
authors: Tuo Cao, Fei Luo, Jiongming Qin, Yu Jiang, Yusen Wang, Chunxia Xiao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6436-6446
affiliations: ["School of Computer Science, Wuhan University, Wuhan, Hubei, China"]
cities: ["Wuhan"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cao_iG-6DoF_Model-free_6DoF_Pose_Estimation_for_Unseen_Object_via_Iterative_CVPR_2025_paper.html

abstract:
Traditional methods in pose estimation often rely on precise 3D models or additional data such as depth and normals, limiting their generalization, especially when objects undergo large translations or rotations. We propose iG-6DoF, a novel model-free 6D pose estimation method iterative 3D Gaussian Splatting to estimate the pose of unseen objects. We first estimates an initial pose by leveraging multi-scale data augmentation and the rotation-equivariant features to create a better pose hypothesis from a set of candidates. Then, we propose an iterative 3DGS approach through iteratively rendering and comparing the rendered image with the input image to further progressively improve pose estimation accuracy. The proposed end-to-end network consists of an object detector, a multi-scale rotation-equivariant feature based initial pose estimator, and a coarse-to-fine pose refiner. Such combination allows our method to focus on the target object in a complex scene and deal with large movement and weak textures.Such framework allows to deal with large movement and weak texture and complex scene. We conduct extensive experiments on benchmark and real datasets. Our method achieves state-of-the-art results on the LINEMOD, OnePose-LowTexture, and GenMOP datasets, demonstrating its strong generalization to unseen objects and robustness across various scenes.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cao_iG-6DoF_Model-free_6DoF_Pose_Estimation_for_Unseen_Object_via_Iterative_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cao_iG-6DoF_Model-free_6DoF_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cao_2025_CVPR,
    author    = {Cao, Tuo and Luo, Fei and Qin, Jiongming and Jiang, Yu and Wang, Yusen and Xiao, Chunxia},
    title     = {iG-6DoF: Model-free 6DoF Pose Estimation for Unseen Object via Iterative 3D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6436-6446}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Wuhan University, Wuhan, Hubei, China", "city": "Wuhan", "country": "China"}]


title: NexusGS: Sparse View Synthesis with Epipolar Depth Priors in 3D Gaussian Splatting
authors: Yulong Zheng, Zicheng Jiang, Shengfeng He, Yandu Sun, Junyu Dong, Huaidong Zhang, Yong Du
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26800-26809
cities: ["Qingdao", "Singapore", "Guangzhou"]
countries: ["China", "Singapore", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zheng_NexusGS_Sparse_View_Synthesis_with_Epipolar_Depth_Priors_in_3D_CVPR_2025_paper.html

abstract:
Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS) have noticeably advanced photo-realistic novel view synthesis using images from densely spaced camera viewpoints. However, these methods struggle in few-shot scenarios due to limited supervision. In this paper, we present NexusGS, a 3DGS-based approach that enhances novel view synthesis from sparse-view images by directly embedding depth information into point clouds, without relying on complex manual regularizations. Exploiting the inherent epipolar geometry of 3DGS, our method introduces a novel point cloud densification strategy that initializes 3DGS with a dense point cloud, reducing randomness in point placement while preventing over-smoothing and overfitting. Specifically, NexusGS comprises three key steps: Epipolar Depth Nexus, Flow-Resilient Depth Blending, and Flow-Filtered Depth Pruning. These steps leverage optical flow and camera poses to compute accurate depth maps, while mitigating the inaccuracies often associated with optical flow. By incorporating epipolar depth priors, NexusGS ensures reliable dense point cloud coverage and supports stable 3DGS training under sparse-view conditions. Experiments demonstrate that NexusGS significantly enhances depth accuracy and rendering quality, surpassing state-of-the-art methods by a considerable margin. Furthermore, we validate the superiority of our generated point clouds by substantially boosting the performance of competing methods. Project page: https://usmizuki.github.io/NexusGS/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zheng_NexusGS_Sparse_View_Synthesis_with_Epipolar_Depth_Priors_in_3D_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zheng_NexusGS_Sparse_View_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zheng_2025_CVPR,
    author    = {Zheng, Yulong and Jiang, Zicheng and He, Shengfeng and Sun, Yandu and Dong, Junyu and Zhang, Huaidong and Du, Yong},
    title     = {NexusGS: Sparse View Synthesis with Epipolar Depth Priors in 3D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26800-26809}
}

raw_affiliation:
[{"affiliation": "Ocean University of China", "city": "Qingdao", "country": "China"}, {"affiliation": "Singapore Management University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}]

affiliations:
["Ocean University of China", "Singapore Management University", "South China University of Technology"]


authors: Eric Hedlin, Munawar Hayat, Fatih Porikli, Kwang Moo Yi, Shweta Mahajan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22129-22138
affiliations: ["University of British Columbia", "Qualcomm AI Research"]
cities: ["Vancouver", "San Diego"]
countries: ["Canada", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hedlin_HyperNet_Fields_Efficiently_Training_Hypernetworks_without_Ground_Truth_by_Learning_CVPR_2025_paper.html

title:
HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories

abstract:
To efficiently adapt large models or to train generative models of neural representations, Hypernetworks have drawn interest. While hypernetworks work well, training them is cumbersome, and often requires ground truth optimized weights for each sample. However, obtaining each of these weights is a training problem of its own---one needs to train, e.g., adaptation weights or even an entire neural field for hypernetworks to regress to. In this work, we propose a method to train hypernetworks, without the need for any per-sample ground truth. Our key idea is to learn a Hypernetwork `Field' and estimate the entire trajectory of network weight training instead of simply its converged state. In other words, we introduce an additional input to the Hypernetwork, the convergence state, which then makes it act as a neural field that models the entire convergence pathway of a task network. A critical benefit in doing so is that the gradient of the estimated weights at any convergence state must then match the gradients of the original task---this constraint alone is sufficient to train the Hypernetwork Field. We demonstrate the effectiveness of our method through the task of personalized image generation and 3D shape reconstruction from images and point clouds, demonstrating competitive results without any per-sample ground truth.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hedlin_HyperNet_Fields_Efficiently_Training_Hypernetworks_without_Ground_Truth_by_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hedlin_HyperNet_Fields_Efficiently_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Hedlin_2025_CVPR,
    author    = {Hedlin, Eric and Hayat, Munawar and Porikli, Fatih and Yi, Kwang Moo and Mahajan, Shweta},
    title     = {HyperNet Fields: Efficiently Training Hypernetworks without Ground Truth by Learning Weight Trajectories},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22129-22138}
}

raw_affiliation:
[{"affiliation": "University of British Columbia", "city": "Vancouver", "country": "Canada"}, {"affiliation": "Qualcomm AI Research", "city": "San Diego", "country": "USA"}]


title: Universal Scene Graph Generation
authors: Shengqiong Wu, Hao Fei, Tat-seng Chua
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14158-14168
raw_affiliation: [{"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}]
affiliations: ["National University of Singapore"]
cities: ["Singapore"]
countries: ["Singapore"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Universal_Scene_Graph_Generation_CVPR_2025_paper.html

abstract:
Scene graph (SG) representations can neatly and efficiently describe scene semantics, which has driven sustained intensive research in SG generation. In the real world, multiple modalities often coexist, with different types, such as images, text, video, and 3D data, expressing distinct characteristics. Unfortunately, current SG research is largely confined to single-modality scene modeling, preventing the full utilization of the complementary strengths of different modality SG representations in depicting holistic scene semantics. To this end, we introduce Universal SG (USG), a novel representation capable of fully characterizing comprehensive semantic scenes from any given combination of modality inputs, encompassing modality-invariant and modality-specific scenes. Further, we tailor a niche-targeting USG parser, USG-Par, which effectively addresses two key bottlenecks of cross-modal object alignment and out-of-domain challenges.  We design the USG-Par with modular architecture for end-to-end USG generation, in which we devise an object associator to relieve the modality gap for cross-modal object alignment. Further, we propose a text-centric scene contrasting learning mechanism to mitigate domain imbalances by aligning multimodal objects and relations with textual SGs. Through extensive experiments, we demonstrate that USG offers a stronger capability for expressing scene semantics than standalone SGs, and also that our USG-Par achieves higher efficacy and performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Universal_Scene_Graph_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Universal_Scene_Graph_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Shengqiong and Fei, Hao and Chua, Tat-seng},
    title     = {Universal Scene Graph Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14158-14168}
}


title: RICCARDO: Radar Hit Prediction and Convolution for Camera-Radar 3D Object Detection
authors: Yunfei Long, Abhinav Kumar, Xiaoming Liu, Daniel Morris
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22276-22285
raw_affiliation: [{"affiliation": "Michigan State University", "city": "East Lansing", "country": "USA"}]
affiliations: ["Michigan State University"]
cities: ["East Lansing"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Long_RICCARDO_Radar_Hit_Prediction_and_Convolution_for_Camera-Radar_3D_Object_CVPR_2025_paper.html

abstract:
Radar hits reflect from points on both the boundary and internal to object outlines. This results in a complex distribution of radar hits that depends on factors including object category, size and orientation. Current radar-camera fusion methods implicitly account for this with a black-box neural network. In this paper, we explicitly utilize a radar hit distribution model to assist fusion. First, we build a model to predict radar hit distributions conditioned on object properties obtained from a monocular detector. Second, we use the predicted distribution as a kernel to match actual measured radar points in the neighborhood of the monocular detections, generating matching scores at nearby positions. Finally, a fusion stage combines context with the kernel detector to refine the matching scores. Our method achieves the state-of-the-art radar-camera detection performance on nuScenes. Our source code is available at https://github.com/longyunf/riccardo.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Long_RICCARDO_Radar_Hit_Prediction_and_Convolution_for_Camera-Radar_3D_Object_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Long_RICCARDO_Radar_Hit_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Long_2025_CVPR,
    author    = {Long, Yunfei and Kumar, Abhinav and Liu, Xiaoming and Morris, Daniel},
    title     = {RICCARDO: Radar Hit Prediction and Convolution for Camera-Radar 3D Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22276-22285}
}


title: ForestLPR: LiDAR Place Recognition in Forests Attentioning Multiple BEV Density Images
authors: Yanqing Shen, Turcan Tuna, Marco Hutter, Cesar Cadena, Nanning Zheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6659-6669
cities: ["Xi'an", "Zurich"]
countries: ["China", "Switzerland"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shen_ForestLPR_LiDAR_Place_Recognition_in_Forests_Attentioning_Multiple_BEV_Density_CVPR_2025_paper.html

abstract:
Place recognition is essential to maintain global consistency in large-scale localization systems. While research in urban environments has progressed significantly using LiDARs or cameras, applications in natural forest-like environments remain largely underexplored. Furthermore, forests present particular challenges due to high self-similarity and substantial variations in vegetation growth over time. In this work, we propose a robust LiDAR-based place recognition method for natural forests, ForestLPR. We hypothesize that a set of cross-sectional images of the forest's geometry at different heights contains the information needed to recognize revisiting a place. The cross-sectional images are represented by bird's-eye view (BEV) density images of horizontal slices of the point cloud at different heights. Our approach utilizes a visual transformer as the shared backbone to produce sets of local descriptors and introduces a multi-BEV interaction module to attend to information at different heights adaptively. It is followed by an aggregation layer that produces a rotation-invariant place descriptor. We evaluated the efficacy of our method extensively on real-world data from public benchmarks as well as robotic datasets and compared it against the state-of-the-art (SOTA) methods. The results indicate that ForestLPR has consistently good performance on all evaluations and achieves an average increase of 7.38% and 9.11% on Recall@1 over the closest competitor on intra-sequence loop closure detection and inter-sequence re-localization, respectively, validating our hypothesis.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_ForestLPR_LiDAR_Place_Recognition_in_Forests_Attentioning_Multiple_BEV_Density_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shen_ForestLPR_LiDAR_Place_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Shen_2025_CVPR,
    author    = {Shen, Yanqing and Tuna, Turcan and Hutter, Marco and Cadena, Cesar and Zheng, Nanning},
    title     = {ForestLPR: LiDAR Place Recognition in Forests Attentioning Multiple BEV Density Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6659-6669}
}

raw_affiliation:
[{"affiliation": "Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "Robotic Systems Lab, ETH Zurich", "city": "Zurich", "country": "Switzerland"}]

affiliations:
["Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University", "Robotic Systems Lab, ETH Zurich"]


title: BLADE: Single-view Body Mesh Estimation through Accurate Depth Estimation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21991-22000
affiliations: ["UNC Chapel Hill", "NVIDIA"]
cities: ["Chapel Hill", "Santa Clara"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_BLADE_Single-view_Body_Mesh_Estimation_through_Accurate_Depth_Estimation_CVPR_2025_paper.html

authors:
Shengze Wang, Jiefeng Li, Tianye Li, Ye Yuan, Henry Fuchs, Koki Nagano, Shalini De Mello, Michael Stengel

abstract:
Single-image human mesh recovery is a challenging task due to the ill-posed nature of simultaneous body shape, pose, and camera estimation. Existing estimators work well on images taken from afar, but they break down as the person moves close to the camera. Moreover, current methods fail to achieve both accurate 3D pose and 2D alignment at the same time. Error is mainly introduced by inaccurate perspective projection heuristically derived from orthographic parameters. To resolve this long-standing challenge, we present our method BLADE which accurately recovers perspective parameters from a single image without heuristic assumptions. We start from the inverse relationship between perspective distortion and the person's Z-translation T_z, and we show that T_z can be reliably estimated from the image. We then discuss the important role of T_z for accurate human mesh recovery estimated from close-range images. Finally, we show that, once T_z and the 3D human mesh are estimated, one can accurately recover the focal length and full 3D translation. Extensive experiments on standard benchmarks and real-world close-range images show that our method accurately recovers projection parameters from a single image, and consequently attains state-of-the-art accuracy on both 3D pose estimation and 2D alignment for a wide range of images.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_BLADE_Single-view_Body_Mesh_Estimation_through_Accurate_Depth_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_BLADE_Single-view_Body_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Shengze and Li, Jiefeng and Li, Tianye and Yuan, Ye and Fuchs, Henry and Nagano, Koki and De Mello, Shalini and Stengel, Michael},
    title     = {BLADE: Single-view Body Mesh Estimation through Accurate Depth Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21991-22000}
}

raw_affiliation:
[{"affiliation": "UNC Chapel Hill", "city": "Chapel Hill", "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. 9413-9422
cities: ["Beijing", "Beijing", "Beijing", "Beijing", "Unknown", "Shanghai", "Jiangsu", "Beijing"]
countries: ["China", "China", "China", "China", "Unknown", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Du_AdaMMS_Model_Merging_for_Heterogeneous_Multimodal_Large_Language_Models_with_CVPR_2025_paper.html

title:
AdaMMS: Model Merging for Heterogeneous Multimodal Large Language Models with Unsupervised Coefficient Optimization

authors:
Yiyang Du, Xiaochen Wang, Chi Chen, Jiabo Ye, Yiru Wang, Peng Li, Ming Yan, Ji Zhang, Fei Huang, Zhifang Sui, Maosong Sun, Yang Liu

abstract:
Recently, model merging methods have demonstrated powerful strengths in combining abilities on various tasks from multiple Large Language Models (LLMs). While previous model merging methods mainly focus on merging homogeneous models with identical architecture, they meet challenges when dealing with Multimodal Large Language Models (MLLMs) with inherent heterogeneous property, including differences in model architecture and the asymmetry in the parameter space. In this work, we propose AdaMMS, a novel model merging method tailored for heterogeneous MLLMs. Our method tackles the challenges in three steps: mapping, merging and searching. Specifically, we first design mapping function between models to apply model merging on MLLMs with different architecture. Then we apply linear interpolation on model weights to actively adapt the asymmetry in the heterogeneous MLLMs. Finally in the hyper-parameter searching step, we propose an unsupervised hyper-parameter selection method for model merging. As the first model merging method capable of merging heterogeneous MLLMs without labeled data, extensive experiments on various model combinations demonstrated that AdaMMS outperforms previous model merging methods on various vision-language benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Du_AdaMMS_Model_Merging_for_Heterogeneous_Multimodal_Large_Language_Models_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Du_AdaMMS_Model_Merging_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Du_2025_CVPR,
    author    = {Du, Yiyang and Wang, Xiaochen and Chen, Chi and Ye, Jiabo and Wang, Yiru and Li, Peng and Yan, Ming and Zhang, Ji and Huang, Fei and Sui, Zhifang and Sun, Maosong and Liu, Yang},
    title     = {AdaMMS: Model Merging for Heterogeneous Multimodal Large Language Models with Unsupervised Coefficient Optimization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9413-9422}
}

raw_affiliation:
[{"affiliation": "Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Institute for AI Industry Research (AIR), Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Laboratory of Multimedia Information Processing, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Software Microelectronics, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Institute of Intelligent Computing, Alibaba Group", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Jiangsu Collaborative Innovation Center for Language Competence", "city": "Jiangsu", "country": "China"}, {"affiliation": "ModelTC Open Source Organization", "city": "Beijing", "country": "China"}]

affiliations:
["Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua University", "Institute for AI Industry Research (AIR), Tsinghua University", "State Key Laboratory of Multimedia Information Processing, Peking University", "School of Software Microelectronics, Peking University", "Institute of Intelligent Computing, Alibaba Group", "Shanghai Artificial Intelligence Laboratory", "Jiangsu Collaborative Innovation Center for Language Competence", "ModelTC Open Source Organization"]


title: MoEE: Mixture of Emotion Experts for Audio-Driven Portrait Animation
authors: Huaize Liu, Wenzhang Sun, Donglin Di, Shibo Sun, Jiahui Yang, Changqing Zou, Hujun Bao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26222-26231
cities: ["Hangzhou", "Beijing", "Harbin", "Hangzhou", "Hangzhou"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_MoEE_Mixture_of_Emotion_Experts_for_Audio-Driven_Portrait_Animation_CVPR_2025_paper.html

abstract:
The generation of talking avatars has achieved significant advancements in precise audio synchronization. However, crafting lifelike talking head videos requires capturing a broad spectrum of emotions and subtle facial expressions. Current methods face fundamental challenges: a) the absence of frameworks for modeling single basic emotional expressions, which restricts the generation of complex emotions such as compound emotions; b) the lack of comprehensive datasets rich in human emotional expressions, which limits the potential of models. To address these challenges, we propose the following innovations: 1) the Mixture of Emotion Experts (MoEE) model, which decouples six fundamental emotions to enable the precise synthesis of both singular and compound emotional states; 2) the DH-FaceEmoVid-150 dataset, specifically curated to include six prevalent human emotional expressions as well as four types of compound emotions, thereby expanding the training potential of emotion-driven models; 3) an emotion-to-latents module that leverages multimodal inputs, aligning diverse control signals--such as audio, text, and labels--to enhance audio-driven emotion control. Through extensive quantitative and qualitative evaluations, we demonstrate that the MoEE framework, in conjunction with the DH-FaceEmoVid-150 dataset, excels in generating complex emotional expressions and nuanced facial details, setting a new benchmark in the field. These datasets will be publicly released.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_MoEE_Mixture_of_Emotion_Experts_for_Audio-Driven_Portrait_Animation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_MoEE_Mixture_of_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Huaize and Sun, Wenzhang and Di, Donglin and Sun, Shibo and Yang, Jiahui and Zou, Changqing and Bao, Hujun},
    title     = {MoEE: Mixture of Emotion Experts for Audio-Driven Portrait Animation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26222-26231}
}

raw_affiliation:
[{"affiliation": "Zhejiang Lab", "city": "Hangzhou", "country": "China"}, {"affiliation": "Li Auto", "city": "Beijing", "country": "China"}, {"affiliation": "Harbin Institute of Technology", "city": "Harbin", "country": "China"}, {"affiliation": "State Key Lab of CAD&CG, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences", "city": "Hangzhou", "country": "China"}]

affiliations:
["Zhejiang Lab", "Li Auto", "Harbin Institute of Technology", "State Key Lab of CAD&CG, Zhejiang University", "Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences"]


title: ReCap: Better Gaussian Relighting with Cross-Environment Captures
authors: Jingzhi Li, Zongwei Wu, Eduard Zamfir, Radu Timofte
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21307-21316
affiliations: ["Computer Vision Lab, CAIDAS & IFI, University of W\u00a8urzburg, Germany"]
cities: ["Wurzburg"]
countries: ["Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_ReCap_Better_Gaussian_Relighting_with_Cross-Environment_Captures_CVPR_2025_paper.html

abstract:
Accurate 3D objects relighting in diverse unseen environments is crucial for realistic virtual object placement. Due to the albedo-lighting ambiguity, existing methods often fall short in producing faithful relights. Without proper constraints, observed training views can be explained by numerous combinations of lighting and material attributes, lacking physical correspondence with the actual environment maps used for relighting. In this work, we present ReCap, treating cross-environment captures as multi-task target to provide the missing supervision that cuts through the entanglement. Specifically, ReCap jointly optimizes multiple lighting representations that share a common set of material attributes. This naturally harmonizes a coherent set of lighting representations around the mutual material attributes, exploiting commonalities and differences across varied object appearances. Such coherence enables physically sound lighting reconstruction and robust material estimation -- both essential for accurate relighting. Together with a streamlined shading function and effective post-processing, ReCap outperforms all leading competitors on an expanded relighting benchmark.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_ReCap_Better_Gaussian_Relighting_with_Cross-Environment_Captures_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_ReCap_Better_Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Jingzhi and Wu, Zongwei and Zamfir, Eduard and Timofte, Radu},
    title     = {ReCap: Better Gaussian Relighting with Cross-Environment Captures},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21307-21316}
}

raw_affiliation:
[{"affiliation": "Computer Vision Lab, CAIDAS & IFI, University of W\u00a8urzburg, Germany", "city": "Wurzburg", "country": "Germany"}]


title: Split Adaptation for Pre-trained Vision Transformers
authors: Lixu Wang, Bingqi Shang, Yi Li, Payal Mohapatra, Wei Dong, Xiao Wang, Qi Zhu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20092-20102
affiliations: ["Northwestern University", "Nanyang Technological University"]
cities: ["Evanston", "Singapore"]
countries: ["USA", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Split_Adaptation_for_Pre-trained_Vision_Transformers_CVPR_2025_paper.html

abstract:
Vision Transformers (ViTs), extensively pre-trained on large-scale datasets, have become fundamental to foundation models, enabling adaptation to diverse downstream tasks. Existing adaptation methods typically require direct data access, rendering them infeasible in privacy-sensitive domains where clients are often reluctant to share their data. A straightforward solution may be sending the pre-trained ViT to clients for local adaptation, which poses issues of model intellectual property and incurs heavy client computation overhead. To address these issues, we propose a novel split adaptation (SA) method that enables effective downstream adaptation while protecting data and models. SA, inspired by split learning (SL), segments the pre-trained ViT into a frontend and a backend, with only the frontend shared with the client for data representation extraction. But unlike regular SL, SA replaces frontend parameters with low-bit quantized values, preventing direct exposure of the model. SA allows the client to add bi-level noise to the frontend and the extracted data representations, ensuring data protection. Accordingly, SA incorporates data-level and model-level out-of-distribution enhancements to mitigate noise injection's impact. Our SA focuses on the challenging few-shot adaptation and adopts patch retrieval augmentation for overfitting alleviation. Extensive experiments on multiple datasets validate SA's superiority over state-of-the-art methods and demonstrate its defense against advanced data reconstruction attacks while preventing model leakage with minimal computation cost on the client side. The source codes can be found at https://github.com/conditionWang/Split_Adaptation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Split_Adaptation_for_Pre-trained_Vision_Transformers_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Split_Adaptation_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Lixu and Shang, Bingqi and Li, Yi and Mohapatra, Payal and Dong, Wei and Wang, Xiao and Zhu, Qi},
    title     = {Split Adaptation for Pre-trained Vision Transformers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20092-20102}
}

raw_affiliation:
[{"affiliation": "Northwestern University", "city": "Evanston", "country": "USA"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]


title: SpatialLLM: A Compound 3D-Informed Design towards Spatially-Intelligent Large Multimodal Models
authors: Wufei Ma, Luoxin Ye, Celso M de Melo, Alan Yuille, Jieneng Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17249-17260
affiliations: ["Johns Hopkins University", "DEVCOM Army Research Laboratory"]
cities: ["Baltimore", "Aberdeen"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ma_SpatialLLM_A_Compound_3D-Informed_Design_towards_Spatially-Intelligent_Large_Multimodal_Models_CVPR_2025_paper.html

abstract:
Humans naturally understand 3D spatial relationships, enabling complex reasoning like predicting collisions of vehicles from different directions. Current large multimodal models (LMMs), however, lack of this capability of 3D spatial reasoning. This limitation stems from the scarcity of 3D training data and the bias in current model designs toward 2D data. In this paper, we systematically study the impact of 3D-informed data, architecture, and training setups, introducing SpatialLLM, a large multi-modal model with advanced 3D spatial reasoning abilities. To address data limitations,  we develop two types of 3D-informed training datasets: (1) 3D-informed probing data focused on object's 3D location and orientation, and (2) 3D-informed conversation data for complex spatial relationships. Notably, we are the first to curate VQA data that incorporate 3D orientation relationships on real images. Furthermore, we systematically integrate these two types of training data with the architectural and training designs of LMMs, providing a roadmap for optimal design aimed at achieving superior 3D reasoning capabilities.  Our SpatialLLM advances machines toward highly capable 3D-informed reasoning, surpassing GPT-4o performance by 8.7%. Our systematic empirical design and the resulting findings offer valuable insights for future research in this direction.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_SpatialLLM_A_Compound_3D-Informed_Design_towards_Spatially-Intelligent_Large_Multimodal_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ma_SpatialLLM_A_Compound_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ma_2025_CVPR,
    author    = {Ma, Wufei and Ye, Luoxin and de Melo, Celso M and Yuille, Alan and Chen, Jieneng},
    title     = {SpatialLLM: A Compound 3D-Informed Design towards Spatially-Intelligent Large Multimodal Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17249-17260}
}

raw_affiliation:
[{"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}, {"affiliation": "DEVCOM Army Research Laboratory", "city": "Aberdeen", "country": "USA"}]


authors: Ning Ni, Libao Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 400-410
affiliations: ["School of Arti\ufb01cial Intelligence, Beijing Normal University"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ni_SLVR_Super-Light_Visual_Reconstruction_via_Blueprint_Controllable_Convolutions_and_Exploring_CVPR_2025_paper.html

title:
SLVR: Super-Light Visual Reconstruction via Blueprint Controllable Convolutions and Exploring Feature Diversity Representation

abstract:
Recently, improving the residual structure and designing efficient convolutions have become important branches of lightweight visual reconstruction model design. We have observed that the feature addition mode (FAM) in existing residual structure tends to lead to slow feature learning or stagnation in feature evolution, a phenomenon we define as network inertia. In addition, although blueprint separable convolutions (BSConv) have proved the dominance of intra-kernel correlation, BSConv forces the blueprint to perform scale transformation on all channels, which may lead to incorrect intra-kernel correlation and introduce useless or disruptive features on some channels and hinder the effective propagation of features. Therefore, in this paper, we rethink the FAM and BSConv for super-light visual reconstruction framework design. First, we design a novel linking mode, called feature diversity evolution link (FDEL), which aims to alleviate the phenomenon of network inertia by reducing the retention of previous low-level features, thereby promoting the evolution of feature diversity. Second, we propose blueprint controllable convolutions (B2Conv). The B2Conv can adaptively pick accurate intra-kernel correlation in the depth-axis, effectively preventing the introduction of useless or disruptive features. Based on FDEL and B2Conv, we develop a super-light super-resolution (SR) framework SLVR for visual reconstruction. Both FDEL and B2Conv can serve as efficient plugins. Extensive experimental results demonstrate the effectiveness of our proposed B2Conv, FDEL, and SLVR. Our code will be available at https://github.com/chongningni/SLVR.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ni_SLVR_Super-Light_Visual_Reconstruction_via_Blueprint_Controllable_Convolutions_and_Exploring_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Ni_2025_CVPR,
    author    = {Ni, Ning and Zhang, Libao},
    title     = {SLVR: Super-Light Visual Reconstruction via Blueprint Controllable Convolutions and Exploring Feature Diversity Representation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {400-410}
}

raw_affiliation:
[{"affiliation": "School of Arti\ufb01cial Intelligence, Beijing Normal University", "city": "Beijing", "country": "China"}]


title: Vision-Language Embodiment for Monocular Depth Estimation
authors: Jinchang Zhang, Guoyu Lu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29479-29489
affiliations: ["University of Georgia", "Binghamton University"]
cities: ["Athens", "Binghamton"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Vision-Language_Embodiment_for_Monocular_Depth_Estimation_CVPR_2025_paper.html

abstract:
Depth estimation is a core problem in robotic perception and vision tasks, but 3D reconstruction from a single image presents inherent uncertainties. Current depth estimation models primarily rely on inter-image relationships for supervised training, often overlooking the intrinsic information provided by the camera itself. We propose a method that embodies the camera model and its physical characteristics into a deep learning model, computing embodied scene depth through real-time interactions with road environments. The model can calculate embodied scene depth in real-time based on immediate environmental changes using only the intrinsic properties of the camera, without any additional equipment. By combining embodied scene depth with RGB image features, the model gains a comprehensive perspective on both geometric and visual details. Additionally, we incorporate text descriptions containing environmental content and depth information as priors for scene understanding, enriching the model's perception of objects. This integration of image and language -- two inherently ambiguous modalities -- leverages their complementary strengths for monocular depth estimation. The real-time nature of the embodied language and depth prior model ensures that the model can continuously adjust its perception and behavior in dynamic environments. Experimental results show that the embodied depth estimation method enhances model performance across different scenes.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Vision-Language_Embodiment_for_Monocular_Depth_Estimation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Jinchang and Lu, Guoyu},
    title     = {Vision-Language Embodiment for Monocular Depth Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29479-29489}
}

raw_affiliation:
[{"affiliation": "University of Georgia", "city": "Athens", "country": "USA"}, {"affiliation": "Binghamton University", "city": "Binghamton", "country": "USA"}]


title: Layered Image Vectorization via Semantic Simplification
authors: Zhenyu Wang, Jianxi Huang, Zhida Sun, Yuanhao Gong, Daniel Cohen-Or, Min Lu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7728-7738
affiliations: ["Shenzhen University", "Tel Aviv University"]
cities: ["Shenzhen", "Tel Aviv"]
countries: ["China", "Israel"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Layered_Image_Vectorization_via_Semantic_Simplification_CVPR_2025_paper.html

abstract:
This work presents a progressive image vectorization technique that reconstructs the raster image as layer-wise vectors from semantic-aligned macro structures to finer details. Our approach introduces a new image simplification method leveraging the feature-average effect in the Score Distillation Sampling mechanism, achieving effective visual abstraction from the detailed to coarse. Guided by the sequence of progressive simplified images, we propose a two-stage vectorization process of structural buildup and visual refinement, constructing the vectors in an organized and manageable manner. The resulting vectors are layered and well-aligned with the target image's explicit and implicit semantic structures. Our method demonstrates high performance across a wide range of images. Comparative analysis with existing vectorization methods highlights our technique's superiority in creating vectors with high visual fidelity, and more importantly, achieving higher semantic alignment and more compact layered representation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Layered_Image_Vectorization_via_Semantic_Simplification_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Layered_Image_Vectorization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zhenyu and Huang, Jianxi and Sun, Zhida and Gong, Yuanhao and Cohen-Or, Daniel and Lu, Min},
    title     = {Layered Image Vectorization via Semantic Simplification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7728-7738}
}

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


title: Learning Occlusion-Robust Vision Transformers for Real-Time UAV Tracking
authors: You Wu, Xucheng Wang, Xiangyang Yang, Mengyuan Liu, Dan Zeng, Hengzhou Ye, Shuiwang Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17103-17113
cities: ["Guilin", "Shanghai", "Zhuhai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Learning_Occlusion-Robust_Vision_Transformers_for_Real-Time_UAV_Tracking_CVPR_2025_paper.html

abstract:
Single-stream architectures using Vision Transformer (ViT) backbones show great potential for real-time UAV tracking recently. However, frequent occlusions from obstacles like buildings and trees expose a major drawback: these models often lack strategies to handle occlusions effectively. New methods are needed to enhance the occlusion resilience of single-stream ViT models in aerial tracking. In this work, we propose to learn Occlusion-Robust Representations (ORR) based on ViTs for UAV tracking by enforcing an invariance of the feature representation of a target with respect to random masking operations modeled by a spatial Cox process. Hopefully, this random masking approximately simulates target occlusions, thereby enabling us to learn ViTs that are robust to target occlusion for UAV tracking. This framework is termed ORTrack. Additionally, to facilitate real-time applications, we propose an Adaptive Feature-Based Knowledge Distillation (AFKD) method to create a more compact tracker, which adaptively mimics the behavior of the teacher model ORTrack according to the task's difficulty. This student model, dubbed ORTrack-D, retains much of ORTrack's performance while offering higher efficiency. Extensive experiments on multiple benchmarks validate the effectiveness of our method, demonstrating its state-of-the-art performance. Codes is available at https://github.com/wuyou3474/ORTrack.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Learning_Occlusion-Robust_Vision_Transformers_for_Real-Time_UAV_Tracking_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Learning_Occlusion-Robust_Vision_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, You and Wang, Xucheng and Yang, Xiangyang and Liu, Mengyuan and Zeng, Dan and Ye, Hengzhou and Li, Shuiwang},
    title     = {Learning Occlusion-Robust Vision Transformers for Real-Time UAV Tracking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17103-17113}
}

raw_affiliation:
[{"affiliation": "College of Computer Science and Engineering, Guilin University of Technology, China", "city": "Guilin", "country": "China"}, {"affiliation": "School of Computer Science, Fudan University, Shanghai, China", "city": "Shanghai", "country": "China"}, {"affiliation": "School of Artificial Intelligence, Sun Yat-sen University, Zhuhai, China", "city": "Zhuhai", "country": "China"}]

affiliations:
["College of Computer Science and Engineering, Guilin University of Technology, China", "School of Computer Science, Fudan University, Shanghai, China", "School of Artificial Intelligence, Sun Yat-sen University, Zhuhai, China"]


title: Plug-and-Play Versatile Compressed Video Enhancement
authors: Huimin Zeng, Jiacheng Li, Zhiwei Xiong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17767-17777
affiliations: ["University of Science and Technology of China"]
cities: ["Hefei"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zeng_Plug-and-Play_Versatile_Compressed_Video_Enhancement_CVPR_2025_paper.html

abstract:
As a widely adopted technique in data transmission, video compression effectively reduces the size of files, making it possible for real-time cloud computing.  However, it comes at the cost of visual quality, posing challenges to the robustness of downstream vision models.  In this work, we present a versatile codec-aware enhancement framework that reuses codec information to adaptively enhance videos under different compression settings, assisting various downstream vision tasks without introducing computation bottleneck.  Specifically, the proposed codec-aware framework consists of a compression-aware adaptation (CAA) network that employs a hierarchical adaptation mechanism to estimate parameters of the frame-wise enhancement network, namely the bitstream-aware enhancement  (BAE) network. The BAE network further leverages temporal and spatial priors embedded in the bitstream to effectively improve the quality of compressed input frames.  Extensive experimental results demonstrate the superior quality enhancement performance of our framework over existing enhancement methods, as well as its versatility in assisting multiple downstream tasks on compressed videos as a plug-and-play module.  Code and models are available at https://huimin-zeng.github.io/PnP-VCVE/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zeng_Plug-and-Play_Versatile_Compressed_Video_Enhancement_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zeng_Plug-and-Play_Versatile_Compressed_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zeng_2025_CVPR,
    author    = {Zeng, Huimin and Li, Jiacheng and Xiong, Zhiwei},
    title     = {Plug-and-Play Versatile Compressed Video Enhancement},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17767-17777}
}


title: UltraFusion: Ultra High Dynamic Imaging using Exposure Fusion
authors: Zixuan Chen, Yujin Wang, Xin Cai, Zhiyuan You, Zheming Lu, Fan Zhang, Shi Guo, Tianfan Xue
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16111-16121
affiliations: ["Shanghai AI Laboratory", "The Chinese University of Hong Kong", "Zhejiang University"]
cities: ["Shanghai", "Hong Kong", "Hangzhou"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_UltraFusion_Ultra_High_Dynamic_Imaging_using_Exposure_Fusion_CVPR_2025_paper.html

abstract:
Capturing high dynamic range (HDR) scenes is one of the most important issues in camera design. Majority of cameras use exposure fusion, which fuses images captured by different exposure levels, to increase dynamic range. However, this approach can only handle images with limited exposure difference, normally 3-4 stops. When applying to very high dynamic range scenes where a large exposure difference is required, this approach often fails due to incorrect alignment or inconsistent lighting between inputs, or tone mapping artifacts. In this work, we propose UltraFusion, the first exposure fusion technique that can merge inputs with 9 stops differences. The key idea is that we model exposure fusion as a guided inpainting problem, where the under-exposed image is used as a guidance to fill the missing information of over-exposed highlights in the over-exposed region. Using an under-exposed image as a soft guidance, instead of a hard constraint, our model is robust to potential alignment issue or lighting variations. Moreover, by utilizing the image prior of the generative model, our model also generates natural tone mapping, even for very high-dynamic range scenes. Our approach outperforms HDR-Transformer on latest HDR benchmarks. Moreover, to test its performance in ultra high dynamic range scenes, we capture a new real-world exposure fusion benchmark, UltraFusion dataset, with exposure differences up to 9 stops, and experiments show that UltraFusion can generate beautiful and high-quality fusion results under various scenarios. Code and data will be available at https://openimaginglab.github.io/UltraFusion.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_UltraFusion_Ultra_High_Dynamic_Imaging_using_Exposure_Fusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_UltraFusion_Ultra_High_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Zixuan and Wang, Yujin and Cai, Xin and You, Zhiyuan and Lu, Zheming and Zhang, Fan and Guo, Shi and Xue, Tianfan},
    title     = {UltraFusion: Ultra High Dynamic Imaging using Exposure Fusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16111-16121}
}

raw_affiliation:
[{"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]


title: Hearing Anywhere in Any Environment
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5732-5741
affiliations: ["University of Washington", "Meta", "University of Maryland, College Park"]
cities: ["Seattle", "Menlo Park", "College Park"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Hearing_Anywhere_in_Any_Environment_CVPR_2025_paper.html

authors:
Xiulong Liu, Anurag Kumar, Paul Calamia, Sebastia V. Amengual, Calvin Murdock, Ishwarya Ananthabhotla, Philip Robinson, Eli Shlizerman, Vamsi Krishna Ithapu, Ruohan Gao

abstract:
In mixed reality applications, a realistic acoustic experience in spatial environments is as crucial as the visual experience for achieving true immersion. Despite recent advances in neural approaches for Room Impulse Response (RIR) estimation, most existing methods are limited to the single environment on which they are trained, lacking the ability to generalize to new rooms with different geometries and surface materials. We aim to develop a unified model capable of reconstructing the spatial acoustic experience of any environment with minimum additional measurements. To this end, we present xRIR, a framework for cross-room RIR prediction. The core of our generalizable approach lies in combining a geometric feature extractor, which captures spatial context from panorama depth images, with a RIR encoder that extracts detailed acoustic features from only a few reference RIR samples. To evaluate our method, we introduce AcousticRooms, a new dataset featuring high-fidelity simulation of over 300,000 RIRs from 260 rooms. Experiments show that our method strongly outperforms a series of baselines. Furthermore, we successfully perform sim-to-real transfer by evaluating our model on four real-world environments, demonstrating the generalizability of our approach and the realism of our dataset.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Hearing_Anywhere_in_Any_Environment_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Hearing_Anywhere_in_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Xiulong and Kumar, Anurag and Calamia, Paul and Amengual, Sebastia V. and Murdock, Calvin and Ananthabhotla, Ishwarya and Robinson, Philip and Shlizerman, Eli and Ithapu, Vamsi Krishna and Gao, Ruohan},
    title     = {Hearing Anywhere in Any Environment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5732-5741}
}

raw_affiliation:
[{"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}, {"affiliation": "Meta", "city": "Menlo Park", "country": "USA"}, {"affiliation": "University of Maryland, College Park", "city": "College Park", "country": "USA"}]


title: Automated Proof of Polynomial Inequalities via Reinforcement Learning
authors: Banglong Liu, Niuniu Qi, Xia Zeng, Lydia Dehbi, Zhengfeng Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5052-5060
affiliations: ["East China Normal University", "Southwest University"]
cities: ["Shanghai", "Chongqing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Automated_Proof_of_Polynomial_Inequalities_via_Reinforcement_Learning_CVPR_2025_paper.html

abstract:
Polynomial inequality proving is fundamental to many mathematical disciplines and finds wide applications in diverse fields. Current traditional algebraic methods are based on searching for a polynomial positive definite representation over a set of basis. However, these methods are limited by truncation degree.To address this issue, this paper proposes an approach based on reinforcement learning to find a Krivine-basis representation for proving polynomial inequalities.Specifically, we formulate the inequality proving problem as a linear programming (LP) problem and encode it as a basis selection problem using reinforcement learning (RL), achieving a non-negative Krivine basis. Moreover, a fast multivariate polynomial multiplication method based on Fast Fourier Transform (FFT) is employed to enhance the efficiency of action space search. Furthermore, we have implemented a tool called APPIRL (Automated Proof of Polynomial Inequalities via Reinforcement Learning).Experimental evaluation on benchmark problems demonstrates the feasibility and effectiveness of our approach. In addition, APPIRL has been successfully applied to solve the maximum stable set problem.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Automated_Proof_of_Polynomial_Inequalities_via_Reinforcement_Learning_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Banglong and Qi, Niuniu and Zeng, Xia and Dehbi, Lydia and Yang, Zhengfeng},
    title     = {Automated Proof of Polynomial Inequalities via Reinforcement Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5052-5060}
}

raw_affiliation:
[{"affiliation": "East China Normal University", "city": "Shanghai", "country": "China"}, {"affiliation": "Southwest University", "city": "Chongqing", "country": "China"}]


authors: Han Hu, Wenli Du, Peng Liao, Bing Wang, Siyuan Fan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19109-19119
cities: ["Shanghai", "Huzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hu_Noise-Resistant_Video_Anomaly_Detection_via_RGB_Error-Guided_Multiscale_Predictive_Coding_CVPR_2025_paper.html

title:
Noise-Resistant Video Anomaly Detection via RGB Error-Guided Multiscale Predictive Coding and Dynamic Memory

abstract:
Due to the interference of background noise, existing video anomaly detection methods are prone to detect some normal events in complex scenes as anomalies. Meanwhile, we note that the diversity of normal patterns has not been adequately considered, i.e., the normal events that are worthy of reference in the test data have not been properly utilized, which raises the risk of missed and false detections. In this work, we combine the tasks of next-frame prediction and predicted-frame reconstruction to propose a noise-resistant video anomaly detection method. For the prediction task, we develop an RGB Error-Guided Multiscale Predictive Coding (EG-MPC) framework to overcome the interference of background noise on the learning of appearance and motion features of objects at various scales, thus achieving high-quality frame prediction. For the reconstruction task, we introduce the Dynamic Memory Modules (DMMs) into the reconstruction network, and design sparse aggregation and selective update strategies for the memory items in the DMMs to effectively represent diverse normal patterns while increasing the difficulty of reconstructing anomalies, thus making it easier to distinguish between normal and abnormal frames. Extensive experiments on four benchmark datasets demonstrate that our proposed method outperforms state-of-the-art approaches, especially in complex scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hu_Noise-Resistant_Video_Anomaly_Detection_via_RGB_Error-Guided_Multiscale_Predictive_Coding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hu_Noise-Resistant_Video_Anomaly_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hu_2025_CVPR,
    author    = {Hu, Han and Du, Wenli and Liao, Peng and Wang, Bing and Fan, Siyuan},
    title     = {Noise-Resistant Video Anomaly Detection via RGB Error-Guided Multiscale Predictive Coding and Dynamic Memory},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19109-19119}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Industrial Control Technology, ECUST, China", "city": "Shanghai", "country": "China"}, {"affiliation": "Huzhou Institute of Industrial Control Technology, China", "city": "Huzhou", "country": "China"}]

affiliations:
["State Key Laboratory of Industrial Control Technology, ECUST, China", "Huzhou Institute of Industrial Control Technology, China"]


title: Frequency Dynamic Convolution for Dense Image Prediction
authors: Linwei Chen, Lin Gu, Liang Li, Chenggang Yan, Ying Fu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30178-30188
cities: ["Beijing", "Tokyo", "Tokyo", "Beijing", "Hangzhou", "Beijing"]
countries: ["China", "Japan", "Japan", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Frequency_Dynamic_Convolution_for_Dense_Image_Prediction_CVPR_2025_paper.html

abstract:
While Dynamic Convolution (DY-Conv) has shown promising performance by enabling adaptive weight selection through multiple parallel weights combined with an attention mechanism, the frequency response of these weights tends to exhibit high similarity, resulting in high parameter costs but limited adaptability. In this work, we introduce Frequency Dynamic Convolution (FDConv), a novel approach that mitigates these limitations by learning a fixed parameter budget in the Fourier domain. FDConv divides this budget into frequency-based groups with disjoint Fourier indices, enabling the construction of frequency-diverse weights without increasing the parameter cost.  To further enhance adaptability, we propose Kernel Spatial Modulation (KSM) and Frequency Band Modulation (FBM). KSM dynamically adjusts the frequency response of each filter at the spatial level, while FBM decomposes weights into distinct frequency bands in the frequency domain and modulates them dynamically based on local content. Extensive experiments on object detection, segmentation, and classification validate the effectiveness of FDConv. We demonstrate that when applied to ResNet-50, FDConv achieves superior performance with a modest increase of +3.6M parameters, outperforming previous methods that require substantial increases in parameter budgets (e.g., CondConv +90M, KW +76.5M). Moreover, FDConv seamlessly integrates into a variety of architectures, including ConvNeXt, Swin-Transformer,  offering a flexible and efficient solution for modern vision tasks. The code is made publicly available at https://github.com/Linwei-Chen/FDConv.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Frequency_Dynamic_Convolution_for_Dense_Image_Prediction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Frequency_Dynamic_Convolution_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Linwei and Gu, Lin and Li, Liang and Yan, Chenggang and Fu, Ying},
    title     = {Frequency Dynamic Convolution for Dense Image Prediction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30178-30188}
}

raw_affiliation:
[{"affiliation": "Beijing Institute of Technology", "city": "Beijing", "country": "China"}, {"affiliation": "RIKEN", "city": "Tokyo", "country": "Japan"}, {"affiliation": "The University of Tokyo", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Hangzhou Dianzi University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["Beijing Institute of Technology", "RIKEN", "The University of Tokyo", "Chinese Academy of Sciences", "Hangzhou Dianzi University", "Tsinghua University"]


authors: Yuhao Wang, Yongfeng Lv, Pingping Zhang, Huchuan Lu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29701-29710
affiliations: ["School of Future Technology, School of Artificial Intelligence, Dalian University of Technology"]
cities: ["Dalian"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_IDEA_Inverted_Text_with_Cooperative_Deformable_Aggregation_for_Multi-modal_Object_CVPR_2025_paper.html

title:
IDEA: Inverted Text with Cooperative Deformable Aggregation for Multi-modal Object Re-Identification

abstract:
Multi-modal object Re-IDentification (ReID) aims to retrieve specific objects by utilizing complementary information from various modalities. However, existing methods focus on fusing heterogeneous visual features, neglecting the potential benefits of text-based semantic information. To address this issue, we first construct three text-enhanced multi-modal object ReID benchmarks. To be specific, we propose a standardized multi-modal caption generation pipeline for structured and concise text annotations with Multi-modal Large Language Models (MLLMs). Besides, current methods often directly aggregate multi-modal information without selecting representative local features, leading to redundancy and high complexity. To address the above issues, we introduce IDEA, a novel feature learning framework comprising the Inverted Multi-modal Feature Extractor (IMFE) and Cooperative Deformable Aggregation (CDA). The IMFE utilizes Modal Prefixes and an InverseNet to integrate multi-modal information with semantic guidance from inverted text. The CDA adaptively generates sampling positions, enabling the model to focus on the interplay between global features and discriminative local features. With the constructed benchmarks and the proposed modules, our framework can generate more robust multi-modal features under complex scenarios. Extensive experiments on three multi-modal object ReID benchmarks demonstrate the effectiveness of our proposed method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_IDEA_Inverted_Text_with_Cooperative_Deformable_Aggregation_for_Multi-modal_Object_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_IDEA_Inverted_Text_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yuhao and Lv, Yongfeng and Zhang, Pingping and Lu, Huchuan},
    title     = {IDEA: Inverted Text with Cooperative Deformable Aggregation for Multi-modal Object Re-Identification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29701-29710}
}

raw_affiliation:
[{"affiliation": "School of Future Technology, School of Artificial Intelligence, Dalian University of Technology", "city": "Dalian", "country": "China"}]


title: SAM-I2V: Upgrading SAM to Support Promptable Video Segmentation with Less than 0.2% Training Cost
authors: Haiyang Mei, Pengyu Zhang, Mike Zheng Shou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3417-3426
affiliations: ["Show Lab, National University of Singapore"]
cities: ["Singapore"]
countries: ["Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mei_SAM-I2V_Upgrading_SAM_to_Support_Promptable_Video_Segmentation_with_Less_CVPR_2025_paper.html

abstract:
Foundation models like the Segment Anything Model (SAM) have significantly advanced promptable image segmentation in computer vision. However, extending these capabilities to videos presents substantial challenges, particularly in ensuring precise and temporally consistent mask propagation in dynamic scenes. SAM 2 attempts to address this by training a model on massive image and video data from scratch to learn complex spatiotemporal associations, resulting in huge training costs that hinder research and practical deployment. In this paper, we introduce SAM-I2V, an effective image-to-video upgradation method for cultivating a promptable video segmentation (PVS) model. Our approach strategically upgrades the pre-trained SAM to support PVS, significantly reducing training complexity and resource requirements. To achieve this, we introduce three key innovations: (i) an image-to-video feature extraction upgrader built upon SAM's static image encoder to enable spatiotemporal video perception, (ii) a memory filtering strategy that selects the most relevant past frames for more effective utilization of historical information, and (iii) a memory-as-prompt mechanism leveraging object memory to ensure temporally consistent mask propagation in dynamic scenes. Comprehensive experiments demonstrate that our method achieves over 90% of SAM 2's performance while using only 0.2% of its training cost. Our work presents a resource-efficient pathway to PVS, lowering barriers for further research in PVS model design and enabling broader applications and advancements in the field. Code and model will be available at: https://github.com/showlab/SAM-I2V.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mei_SAM-I2V_Upgrading_SAM_to_Support_Promptable_Video_Segmentation_with_Less_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mei_SAM-I2V_Upgrading_SAM_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mei_2025_CVPR,
    author    = {Mei, Haiyang and Zhang, Pengyu and Shou, Mike Zheng},
    title     = {SAM-I2V: Upgrading SAM to Support Promptable Video Segmentation with Less than 0.2\% Training Cost},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3417-3426}
}


title: GroupMamba: Efficient Group-Based Visual State Space Model
authors: Abdelrahman Shaker, Syed Talal Wasim, Salman Khan, Juergen Gall, Fahad Shahbaz Khan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14912-14922
cities: ["Abu Dhabi", "Bonn", "Bonn", "Linkoping"]
countries: ["UAE", "Germany", "Germany", "Sweden"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shaker_GroupMamba_Efficient_Group-Based_Visual_State_Space_Model_CVPR_2025_paper.html

abstract:
State-space models (SSMs) have recently shown promise in capturing long-range dependencies with subquadratic computational complexity, making them attractive for various applications. However, purely SSM-based models face critical challenges related to stability and achieving state-of-the-art performance in computer vision tasks. Our paper addresses the challenges of scaling SSM-based models for computer vision, particularly the instability and inefficiency of large model sizes. We introduce a parameter-efficient modulated group mamba layer that divides the input channels into four groups and applies our proposed SSM-based efficient Visual Single Selective Scanning (VSSS) block independently to each group, with each VSSS block scanning in one of the four spatial directions. The Modulated Group Mamba layer also wraps the four VSSS blocks into a channel modulation operator to improve cross-channel communication. Furthermore, we introduce a distillation-based training objective to stabilize the training of large models, leading to consistent performance gains. Our comprehensive experiments demonstrate the merits of the proposed contributions, leading to superior performance over existing methods for image classification on ImageNet-1K, object detection, instance segmentation on MS-COCO, and semantic segmentation on ADE20K.  Our tiny variant with 23M parameters achieves state-of-the-art performance with a classification top-1 accuracy of 83.3% on ImageNet-1K, while being 26% efficient in terms of parameters, compared to the best existing Mamba design of same model size. Code and models are available at: https://github.com/Amshaker/GroupMamba

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shaker_GroupMamba_Efficient_Group-Based_Visual_State_Space_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shaker_GroupMamba_Efficient_Group-Based_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shaker_2025_CVPR,
    author    = {Shaker, Abdelrahman and Wasim, Syed Talal and Khan, Salman and Gall, Juergen and Khan, Fahad Shahbaz},
    title     = {GroupMamba: Efficient Group-Based Visual State Space Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14912-14922}
}

raw_affiliation:
[{"affiliation": "Mohamed Bin Zayed University of Artificial Intelligence", "city": "Abu Dhabi", "country": "UAE"}, {"affiliation": "University of Bonn", "city": "Bonn", "country": "Germany"}, {"affiliation": "Lamarr Institute for Machine Learning and Artificial Intelligence", "city": "Bonn", "country": "Germany"}, {"affiliation": "Link\u00a8oping University", "city": "Linkoping", "country": "Sweden"}]

affiliations:
["Mohamed Bin Zayed University of Artificial Intelligence", "University of Bonn", "Lamarr Institute for Machine Learning and Artificial Intelligence", "Link\u00a8oping University"]


title: How Do I Do That? Synthesizing 3D Hand Motion and Contacts for Everyday Interactions
authors: Aditya Prakash, Benjamin Lundell, Dmitry Andreychuk, David Forsyth, Saurabh Gupta, Harpreet Sawhney
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7026-7036
affiliations: ["University of Illinois Urbana-Champaign", "Microsoft"]
cities: ["Urbana", "Redmond"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Prakash_How_Do_I_Do_That_Synthesizing_3D_Hand_Motion_and_CVPR_2025_paper.html

abstract:
We tackle the novel problem of predicting 3D hand motion and contact maps (or Interaction Trajectories) given a single RGB view, action text, and a 3D contact point on the object as input. Our approach consists of (1) Interaction Codebook: a VQVAE model to learn a latent codebook of hand poses and contact points, effectively tokenizing interaction trajectories, (2) Interaction Predictor: a transformer-decoder module to predict the interaction trajectory from test time inputs by using an indexer module to retrieve a latent affordance from the learned codebook. To train our model, we develop a data engine that extracts 3D hand poses and contact trajectories from the diverse HoloAssist dataset. We evaluate our model on a benchmark that is 2.5-10X larger than existing works, in terms of diversity of objects and interactions observed, and test for generalization of the model across object categories, action categories, tasks, and scenes. Experimental results show the effectiveness of our approach over transformer & diffusion baselines across all settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Prakash_How_Do_I_Do_That_Synthesizing_3D_Hand_Motion_and_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Prakash_2025_CVPR,
    author    = {Prakash, Aditya and Lundell, Benjamin and Andreychuk, Dmitry and Forsyth, David and Gupta, Saurabh and Sawhney, Harpreet},
    title     = {How Do I Do That? Synthesizing 3D Hand Motion and Contacts for Everyday Interactions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7026-7036}
}

raw_affiliation:
[{"affiliation": "University of Illinois Urbana-Champaign", "city": "Urbana", "country": "USA"}, {"affiliation": "Microsoft", "city": "Redmond", "country": "USA"}]


title: Escaping Plato's Cave: Towards the Alignment of 3D and Text Latent Spaces
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19825-19835
cities: ["Paris", "Rome", "Milan", "Austin"]
countries: ["France", "Italy", "Italy", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hadgi_Escaping_Platos_Cave_Towards_the_Alignment_of_3D_and_Text_CVPR_2025_paper.html

authors:
Souhail Hadgi, Luca Moschella, Andrea Santilli, Diego Gomez, Qixing Huang, Emanuele Rodolà, Simone Melzi, Maks Ovsjanikov

abstract:
Recent works have shown that, when trained at scale, uni-modal 2D vision and text encoders converge to learned features that share remarkable structural properties, despite arising from different representations. However, the role of 3D encoders with respect to other modalities remains unexplored. Furthermore, existing 3D foundation models that leverage large datasets are typically trained with explicit alignment objectives with respect to frozen encoders from other representations. In this work, we investigate the possibility of a posteriori alignment of representations obtained from uni-modal 3D encoders compared to text-based feature spaces. We show that naive post-training feature alignment of uni-modal text and 3D encoders results in limited performance. We then focus on extracting subspaces of the corresponding feature spaces and discover that by projecting learned representations onto well-chosen lower-dimensional subspaces the quality of alignment becomes significantly higher, leading to improved accuracy on matching and retrieval tasks. Our analysis further sheds light on the nature of these shared subspaces, which roughly separate between semantic and geometric data representations. Overall, ours is the first work that helps to establish a baseline for post-training alignment of 3D uni-modal and text feature spaces, and helps to highlight both the shared and unique properties of 3D data compared to other representations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hadgi_Escaping_Platos_Cave_Towards_the_Alignment_of_3D_and_Text_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hadgi_Escaping_Platos_Cave_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hadgi_2025_CVPR,
    author    = {Hadgi, Souhail and Moschella, Luca and Santilli, Andrea and Gomez, Diego and Huang, Qixing and Rodol\`a, Emanuele and Melzi, Simone and Ovsjanikov, Maks},
    title     = {Escaping Plato's Cave: Towards the Alignment of 3D and Text Latent Spaces},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19825-19835}
}

raw_affiliation:
[{"affiliation": "\u00b4Ecole polytechnique", "city": "Paris", "country": "France"}, {"affiliation": "Sapienza University of Rome", "city": "Rome", "country": "Italy"}, {"affiliation": "University of Milano-Bicocca", "city": "Milan", "country": "Italy"}, {"affiliation": "The University of Texas at Austin", "city": "Austin", "country": "USA"}]

affiliations:
["\u00b4Ecole polytechnique", "Sapienza University of Rome", "University of Milano-Bicocca", "The University of Texas at Austin"]


title: Consistency Posterior Sampling for Diverse Image Synthesis
authors: Vishal Purohit, Matthew Repasky, Jianfeng Lu, Qiang Qiu, Yao Xie, Xiuyuan Cheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28327-28336
cities: ["West Lafayette", "Atlanta", "Durham", "Durham"]
countries: ["USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Purohit_Consistency_Posterior_Sampling_for_Diverse_Image_Synthesis_CVPR_2025_paper.html

abstract:
Posterior sampling in high-dimensional spaces using generative models holds significant promise for various applications, including but not limited to inverse problems and guided generation tasks. Generating diverse posterior samples remains expensive, as existing methods require restarting the entire generative process for each new sample. In this work, we propose a posterior sampling approach that simulates Langevin dynamics in the noise space of a pre-trained generative model. By exploiting the mapping between the noise and data spaces which can be provided by distilled flows or consistency models, our method enables seamless exploration of the posterior without the need to re-run the full sampling chain, drastically reducing computational overhead. Theoretically, we prove a guarantee for the proposed noise-space Langevin dynamics to approximate the posterior, assuming that the generative model sufficiently approximates the prior distribution. Our framework is experimentally validated on image restoration tasks involving noisy linear and nonlinear forward operators applied to LSUN-Bedroom (256 x 256) and ImageNet (64 x 64) datasets. The results demonstrate that our approach generates high-fidelity samples with enhanced semantic diversity even under a limited number of function evaluations, offering superior efficiency and performance compared to existing diffusion-based posterior sampling techniques.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Purohit_Consistency_Posterior_Sampling_for_Diverse_Image_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Purohit_Consistency_Posterior_Sampling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Purohit_2025_CVPR,
    author    = {Purohit, Vishal and Repasky, Matthew and Lu, Jianfeng and Qiu, Qiang and Xie, Yao and Cheng, Xiuyuan},
    title     = {Consistency Posterior Sampling for Diverse Image Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28327-28336}
}

raw_affiliation:
[{"affiliation": "Elmore Family School of Electrical and Computer Engineering, Purdue University, USA", "city": "West Lafayette", "country": "USA"}, {"affiliation": "H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, USA", "city": "Atlanta", "country": "USA"}, {"affiliation": "Department of Mathematics, Duke University, USA", "city": "Durham", "country": "USA"}, {"affiliation": "Department of Physics and Department of Chemistry, Duke University, USA", "city": "Durham", "country": "USA"}]

affiliations:
["Elmore Family School of Electrical and Computer Engineering, Purdue University, USA", "H. Milton Stewart School of Industrial and Systems Engineering, Georgia Institute of Technology, USA", "Department of Mathematics, Duke University, USA", "Department of Physics and Department of Chemistry, Duke University, USA"]


title: IMFine: 3D Inpainting via Geometry-guided Multi-view Refinement
authors: Zhihao Shi, Dong Huo, Yuhongze Zhou, Yan Min, Juwei Lu, Xinxin Zuo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26694-26703
cities: ["Ottawa", "Edmonton", "Hamilton", "Montreal"]
countries: ["Canada", "Canada", "Canada", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shi_IMFine_3D_Inpainting_via_Geometry-guided_Multi-view_Refinement_CVPR_2025_paper.html

abstract:
Current 3D inpainting and object removal methods are largely limited to front-facing scenes, facing substantial challenges when applied to diverse, "unconstrained" scenes where the camera orientation and trajectory are unrestricted. To bridge this gap, we introduce a novel approach that produces inpainted 3D scenes with consistent visual quality and coherent underlying geometry across both front-facing and unconstrained scenes. Specifically, we propose a robust 3D inpainting pipeline that incorporates geometric priors and a multi-view refinement network trained via test-time adaptation, building on a pre-trained image inpainting model. Additionally, we develop a novel inpainting mask detection technique to derive targeted inpainting masks from object masks, boosting the performance in handling unconstrained scenes. To validate the efficacy of our approach, we create a challenging and diverse benchmark that spans a wide range of scenes. Comprehensive experiments demonstrate that our proposed method substantially outperforms existing state-of-the-art approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shi_IMFine_3D_Inpainting_via_Geometry-guided_Multi-view_Refinement_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shi_IMFine_3D_Inpainting_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Shi_2025_CVPR,
    author    = {Shi, Zhihao and Huo, Dong and Zhou, Yuhongze and Min, Yan and Lu, Juwei and Zuo, Xinxin},
    title     = {IMFine: 3D Inpainting via Geometry-guided Multi-view Refinement},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26694-26703}
}

raw_affiliation:
[{"affiliation": "Huawei Canada Research Institute", "city": "Ottawa", "country": "Canada"}, {"affiliation": "University of Alberta", "city": "Edmonton", "country": "Canada"}, {"affiliation": "McMaster University", "city": "Hamilton", "country": "Canada"}, {"affiliation": "Concordia University", "city": "Montreal", "country": "Canada"}]

affiliations:
["Huawei Canada Research Institute", "University of Alberta", "McMaster University", "Concordia University"]


title: ActiveGAMER: Active GAussian Mapping through Efficient Rendering
authors: Liyan Chen, Huangying Zhan, Kevin Chen, Xiangyu Xu, Qingan Yan, Changjiang Cai, Yi Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16486-16497
affiliations: ["OPPO US Research Center", "Stevens Institute of Technology"]
cities: ["San Francisco", "Hoboken"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_ActiveGAMER_Active_GAussian_Mapping_through_Efficient_Rendering_CVPR_2025_paper.html

abstract:
We introduce ActiveGAMER, an active mapping system that utilizes 3D Gaussian Splatting (3DGS) to achieve high-quality, real-time scene mapping and exploration. Unlike traditional NeRF-based methods, which are computationally demanding and restrict active mapping performance, our approach leverages the efficient rendering capabilities of 3DGS, allowing effective and efficient exploration in complex environments.The core of our system is a rendering-based information gain module that dynamically identifies the most informative viewpoints for next-best-view planning, enhancing both geometric and photometric reconstruction accuracy. ActiveGAMER also integrates a carefully balanced framework, combining coarse-to-fine exploration, post-refinement, and a global-local keyframe selection strategy to maximize reconstruction completeness and fidelity.Our system autonomously explores and reconstructs environments with state-of-the-art geometric and photometric accuracy and completeness, significantly surpassing existing approaches in both aspects. Extensive evaluations on benchmark datasets such as Replica and MP3D highlight ActiveGAMER's effectiveness in active mapping tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_ActiveGAMER_Active_GAussian_Mapping_through_Efficient_Rendering_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_ActiveGAMER_Active_GAussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Liyan and Zhan, Huangying and Chen, Kevin and Xu, Xiangyu and Yan, Qingan and Cai, Changjiang and Xu, Yi},
    title     = {ActiveGAMER: Active GAussian Mapping through Efficient Rendering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16486-16497}
}

raw_affiliation:
[{"affiliation": "OPPO US Research Center", "city": "San Francisco", "country": "USA"}, {"affiliation": "Stevens Institute of Technology", "city": "Hoboken", "country": "USA"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30147-30156
cities: ["Binghamton", "Raleigh", "Newark", "Atlanta", "San Jose"]
countries: ["USA", "USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ahmed_DeepCompress-ViT_Rethinking_Model_Compression_to_Enhance_Efficiency_of_Vision_Transformers_CVPR_2025_paper.html

title:
DeepCompress-ViT: Rethinking Model Compression to Enhance Efficiency of Vision Transformers at the Edge

authors:
Sabbir Ahmed, Abdullah Al Arafat, Deniz Najafi, Akhlak Mahmood, Mamshad Nayeem Rizve, Mohaiminul Al Nahian, Ranyang Zhou, Shaahin Angizi, Adnan Siraj Rakin

abstract:
Vision Transformers (ViTs) excel in tackling complex vision tasks, yet their substantial size poses significant challenges for applications on resource-constrained edge devices. The increased size of these models leads to higher overhead (e.g., energy, latency) when transmitting model weights between the edge device and the server. Hence, ViTs are not ideal for edge devices where the entire model may not fit on the device. Current model compression techniques often achieve high compression ratios at the expense of performance degradation, particularly for ViTs. To overcome the limitations of existing works, we rethink model compression strategy for ViTs from first principle approach and develop an orthogonal strategy called \method. The objective of the \method is to encode the model weights to a highly compressed encoded representation using a novel training method, denoted as Unified Compression Training (UCT). Proposed UCT is accompanied by a decoding mechanism during inference, which helps to gain any loss of accuracy due to high compression ratio. We further optimize this decoding step by re-ordering the decoding operation using associative property of matrix multiplication, ensuring that the compressed weights can be decoded during inference without incurring any computational overhead. Our extensive experiments across multiple ViT models on modern edge devices show that \method can successfully compress ViTs at high compression ratios  (>14x). \method enables the entire model to be stored on edge device, resulting in unprecedented reductions in energy consumption (>1470x) and latency (>68x) for edge ViT inference. Our code is available at \href https://github.com/ML-Security-Research-LAB/DeepCompress-ViT  https://github.com/ML-Security-Research-LAB/DeepCompress-ViT .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ahmed_DeepCompress-ViT_Rethinking_Model_Compression_to_Enhance_Efficiency_of_Vision_Transformers_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Ahmed_2025_CVPR,
    author    = {Ahmed, Sabbir and Al Arafat, Abdullah and Najafi, Deniz and Mahmood, Akhlak and Rizve, Mamshad Nayeem and Al Nahian, Mohaiminul and Zhou, Ranyang and Angizi, Shaahin and Rakin, Adnan Siraj},
    title     = {DeepCompress-ViT: Rethinking Model Compression to Enhance Efficiency of Vision Transformers at the Edge},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30147-30156}
}

raw_affiliation:
[{"affiliation": "Binghamton University (SUNY)", "city": "Binghamton", "country": "USA"}, {"affiliation": "North Carolina State University", "city": "Raleigh", "country": "USA"}, {"affiliation": "New Jersey Institute of Technology", "city": "Newark", "country": "USA"}, {"affiliation": "Georgia Institute of Technology", "city": "Atlanta", "country": "USA"}, {"affiliation": "Adobe Inc.", "city": "San Jose", "country": "USA"}]

affiliations:
["Binghamton University (SUNY)", "North Carolina State University", "New Jersey Institute of Technology", "Georgia Institute of Technology", "Adobe Inc."]


title: EvOcc: Accurate Semantic Occupancy for Automated Driving Using Evidence Theory
authors: Jonas Kälble, Sascha Wirges, Maxim Tatarchenko, Eddy Ilg
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27467-27476
affiliations: ["Bosch Center for Artificial Intelligence", "University of Technology Nuremberg"]
cities: ["Nuremberg", "Nuremberg"]
countries: ["Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kalble_EvOcc_Accurate_Semantic_Occupancy_for_Automated_Driving_Using_Evidence_Theory_CVPR_2025_paper.html

abstract:
We present EvOcc, a novel evidential semantic occupancy mapping framework. It consists of two parts: (1) an evidential approach for calculating the ground-truth 3D semantic occupancy maps from noisy LiDAR measurements, and (2) a method for training image-based occupancy estimation models through a new loss formulation. In contrast to state-of-the-art semantic occupancy maps, our approach explicitly models the uncertainty introduced by unobserved spaces or contradicting measurements and we show that using it results in significantly stronger models. Evaluated as ray-based mIoU, our evidential semantic occupancy mapping approach improves over the baselines by at least 15.8\% for the ground truth and 5.5\% for the trained model. Overall, we make a significant contribution towards more detailed and uncertainty-aware 3D environment understanding and safe operation in autonomous driving.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kalble_EvOcc_Accurate_Semantic_Occupancy_for_Automated_Driving_Using_Evidence_Theory_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kalble_EvOcc_Accurate_Semantic_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Kalble_2025_CVPR,
    author    = {K\"alble, Jonas and Wirges, Sascha and Tatarchenko, Maxim and Ilg, Eddy},
    title     = {EvOcc: Accurate Semantic Occupancy for Automated Driving Using Evidence Theory},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27467-27476}
}

raw_affiliation:
[{"affiliation": "Bosch Center for Artificial Intelligence", "city": "Nuremberg", "country": "Germany"}, {"affiliation": "University of Technology Nuremberg", "city": "Nuremberg", "country": "Germany"}]


title: Positive2Negative: Breaking the Information-Lossy Barrier in Self-Supervised Single Image Denoising
authors: Tong Li, Lizhi Wang, Zhiyuan Xu, Lin Zhu, Wanxuan Lu, Hua Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17924-17934
cities: ["Beijing", "Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Positive2Negative_Breaking_the_Information-Lossy_Barrier_in_Self-Supervised_Single_Image_Denoising_CVPR_2025_paper.html

abstract:
Image denoising enhances image quality, serving as a foundational technique across various computational photography applications. The obstacle to clean image acquisition in real scenarios necessitates the development of self-supervised image denoising methods only depending on noisy images, especially a single noisy image. Existing self-supervised image denoising paradigms (Noise2Noise and Noise2Void) rely heavily on information-lossy operations, such as downsampling and masking, culminating in low-quality denoising performance.In this paper, we propose a novel self-supervised single image denoising paradigm, Positive2Negative, to break the information-lossy barrier.Our paradigm involves two key steps: Renoised Data Construction (RDC) and Denoised Consistency Supervision (DCS). RDC renoises the predicted denoised image by the predicted noise to construct multiple noisy images, preserving all the information of the original image. DCS ensures consistency across the multiple denoised images, supervising the network to learn robust denoising. Our Positive2Negative paradigm achieves state-of-the-art performance in self-supervised single image denoising with significant speed improvements. The code is released to the public at https://github.com/Li-Tong-621/P2N.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Positive2Negative_Breaking_the_Information-Lossy_Barrier_in_Self-Supervised_Single_Image_Denoising_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Tong and Wang, Lizhi and Xu, Zhiyuan and Zhu, Lin and Lu, Wanxuan and Huang, Hua},
    title     = {Positive2Negative: Breaking the Information-Lossy Barrier in Self-Supervised Single Image Denoising},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17924-17934}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology, Beijing Institute of Technology", "city": "Beijing", "country": "China"}, {"affiliation": "School of Artificial Intelligence, Beijing Normal University", "city": "Beijing", "country": "China"}, {"affiliation": "Engineering Research Center of Intelligent Technology and Educational Application, Ministry of Education", "city": "Beijing", "country": "China"}, {"affiliation": "Aerospace Information Research Institute, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["School of Computer Science and Technology, Beijing Institute of Technology", "School of Artificial Intelligence, Beijing Normal University", "Engineering Research Center of Intelligent Technology and Educational Application, Ministry of Education", "Aerospace Information Research Institute, Chinese Academy of Sciences"]


title: Towards Continual Universal Segmentation
authors: Zihan Lin, Zilei Wang, Xu Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29417-29427
affiliations: ["University of Science and Technology of China"]
cities: ["Hefei"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_Towards_Continual_Universal_Segmentation_CVPR_2025_paper.html

abstract:
Despite the significant progress in continual image segmentation, existing arts still strive to balance between stability and plasticity. Additionally, they are specialist to specific tasks and models, which hinders the extension to more general situations. In this work, we present CUE, a novel Continual Universal sEgmentation pipeline that not only inherently tackles the stability-plasticity dilemma, but unifies any segmentation across tasks and models as well. Our key insight: any segmentation task can be reformulated as an understanding-then-refinement paradigm, which is inspired by humans' visual perception system to first perform high-level semantic understanding, then focus on low-level vision cues. We claim three desiderata for this design: Continuity by inherently avoiding the stability-plasticity dilemma via exploiting the natural differences between high-level and low-level knowledge. Generality by unifying and simplifying the landscape towards various segmentation tasks. Efficiency as an interesting by-product by significantly reducing the research effort. Our resulting model, built upon this pipeline by complementary expert models, shows significant improvements over previous state-of-the-arts across various segmentation tasks and datasets. We believe that our work is a significant step towards making continual segmentation more universal and practicable.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_Towards_Continual_Universal_Segmentation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Zihan and Wang, Zilei and Wang, Xu},
    title     = {Towards Continual Universal Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29417-29427}
}


title: PGC: Physics-Based Gaussian Cloth from a Single Pose
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21215-21225
affiliations: ["Stanford University", "Meta Reality Labs"]
cities: ["Stanford", "Menlo Park"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_PGC_Physics-Based_Gaussian_Cloth_from_a_Single_Pose_CVPR_2025_paper.html

authors:
Michelle Guo, Matt Jen-Yuan Chiang, Igor Santesteban, Nikolaos Sarafianos, Hsiao-yu Chen, Oshri Halimi, Aljaž Božič, Shunsuke Saito, Jiajun Wu, C. Karen Liu, Tuur Stuyck, Egor Larionov

abstract:
We introduce a novel approach to reconstruct simulation-ready garments with intricate appearance. Despite recent advancements, existing methods often struggle to balance the need for accurate garment reconstruction with the ability to generalize to new poses and body shapes or require large amounts of data to achieve this. In contrast, our method only requires a multi-view capture of a single static frame. We represent garments as hybrid mesh-embedded 3D Gaussian splats, where the Gaussians capture near-field shading and high-frequency details, while the mesh encodes far-field albedo and optimized reflectance parameters. We achieve novel pose generalization by exploiting the mesh from our hybrid approach, enabling physics-based simulation and surface rendering techniques, while also capturing fine details with Gaussians that accurately reconstruct garment details. Our optimized garments can be used for simulating garments on novel poses, and garment relighting. Project page: phys-gaussian-clothing.github.io.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_PGC_Physics-Based_Gaussian_Cloth_from_a_Single_Pose_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_PGC_Physics-Based_Gaussian_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Michelle and Chiang, Matt Jen-Yuan and Santesteban, Igor and Sarafianos, Nikolaos and Chen, Hsiao-yu and Halimi, Oshri and Bo\v{z}i\v{c}, Alja\v{z} and Saito, Shunsuke and Wu, Jiajun and Liu, C. Karen and Stuyck, Tuur and Larionov, Egor},
    title     = {PGC: Physics-Based Gaussian Cloth from a Single Pose},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21215-21225}
}

raw_affiliation:
[{"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "Meta Reality Labs", "city": "Menlo Park", "country": "USA"}]


title: Joint Vision-Language Social Bias Removal for CLIP
authors: Haoyu Zhang, Yangyang Guo, Mohan Kankanhalli
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4246-4255
raw_affiliation: [{"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}]
affiliations: ["National University of Singapore"]
cities: ["Singapore"]
countries: ["Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Joint_Vision-Language_Social_Bias_Removal_for_CLIP_CVPR_2025_paper.html

abstract:
Vision-Language (V-L) pre-trained models such as CLIP show prominent capabilities in various downstream tasks. Despite this promise, V-L models are notoriously limited by their inherent social biases. A typical demonstration is that V-L models often produce biased predictions against specific groups of people, significantly undermining their real-world applicability. Existing approaches endeavor to mitigate the social bias problem in V-L models by removing biased attribute information from model embeddings. However, after our revisiting of these methods, we find that their bias removal is frequently accompanied by greatly compromised V-L alignment capabilities. We then reveal that this performance degradation stems from the unbalanced debiasing in image and text embeddings. To address this issue, we propose a novel V-L debiasing framework to align image and text biases followed by removing them from both modalities. By doing so, our method achieves multi-modal bias mitigation while maintaining the V-L alignment in the debiased embeddings. Additionally, we advocate a new evaluation protocol that can 1) holistically quantify the model debiasing and V-L alignment ability, and 2) evaluate the generalization of social bias removal models. We believe this work will offer new insights and guidance for future studies addressing the social bias problem in CLIP.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Joint_Vision-Language_Social_Bias_Removal_for_CLIP_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Joint_Vision-Language_Social_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Haoyu and Guo, Yangyang and Kankanhalli, Mohan},
    title     = {Joint Vision-Language Social Bias Removal for CLIP},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4246-4255}
}


title: MV-DUSt3R+: Single-Stage Scene Reconstruction from Sparse Views In 2 Seconds
authors: Zhenggang Tang, Yuchen Fan, Dilin Wang, Hongyu Xu, Rakesh Ranjan, Alexander Schwing, Zhicheng Yan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5283-5293
cities: ["Menlo Park", "Urbana-Champaign"]
countries: ["USA", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_MV-DUSt3R_Single-Stage_Scene_Reconstruction_from_Sparse_Views_In_2_Seconds_CVPR_2025_paper.html

abstract:
Recent sparse multi-view scene reconstruction advances like DUSt3R and MASt3R no longer require camera calibration and camera pose estimation. However, they only process a pair of views at a time to infer pixel-aligned pointmaps. When dealing with more than two views, a combinatorial number of error prone pairwise reconstructions are usually followed by an expensive global optimization, which often fails to rectify the pairwise reconstruction errors. To handle more views, reduce errors, and improve inference time, we propose the fast single-stage feed-forward network MV-DUSt3R. At its core are multi-view decoder blocks which exchange information across any number of views while considering one reference view. To make our method  robust to reference view selection, we further propose MV-DUSt3R+, which employs cross-reference-view blocks to fuse information across different reference view choices. To further enable novel view synthesis, we extend both by adding and jointly training Gaussian splatting heads. Experiments on multi-view stereo reconstruction, multi-view pose estimation, and novel view synthesis confirm that our methods improve significantly upon prior art. Code released.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_MV-DUSt3R_Single-Stage_Scene_Reconstruction_from_Sparse_Views_In_2_Seconds_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tang_MV-DUSt3R_Single-Stage_Scene_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Zhenggang and Fan, Yuchen and Wang, Dilin and Xu, Hongyu and Ranjan, Rakesh and Schwing, Alexander and Yan, Zhicheng},
    title     = {MV-DUSt3R+: Single-Stage Scene Reconstruction from Sparse Views In 2 Seconds},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5283-5293}
}

raw_affiliation:
[{"affiliation": "Meta Reality Labs: {zgtang, zyan3}@meta.com", "city": "Menlo Park", "country": "USA"}, {"affiliation": "University of Illinois Urbana-Champaign: {zt15, aschwing}@illinois.edu", "city": "Urbana-Champaign", "country": "USA"}]

affiliations:
["Meta Reality Labs: {zgtang, zyan3}@meta.com", "University of Illinois Urbana-Champaign: {zt15, aschwing}@illinois.edu"]


title: Explicit Depth-Aware Blurry Video Frame Interpolation Guided by Differential Curves
authors: Zaoming Yan, Pengcheng Lei, Tingting Wang, Faming Fang, Junkang Zhang, Yaomin Huang, Haichuan Song
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1994-2004
cities: ["Shanghai"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yan_Explicit_Depth-Aware_Blurry_Video_Frame_Interpolation_Guided_by_Differential_Curves_CVPR_2025_paper.html

abstract:
Blurry video frame interpolation (BVFI), which aims to generate high-frame-rate clear videos from low-frame-rate blurry inputs, is a challenging yet significant task in computer vision. Current state-of-the-art approaches typically rely on linear or quadratic models to estimate intermediate motion. However, these methods often overlook depth variations that occur during fast object motion, leading to changes in object size and hindering interpolation performance. This paper proposes the Differential Curves-guided Blurry Video Frame Interpolation (DC-BVFI) framework, which leverages the differential curves theory to analyze and mitigate the effects of depth variations caused by object motion. Specifically, DC-BVFI consists of UBNet and MPNet. Unlike prior approaches that rely on optical flow for frame interpolation, MPNet is designed to estimate the 3D scene flow, which facilitates a more precise awareness of depth and velocity variations. Since scene flow cannot be directly inferred in the 2D frame space, UBNet is introduced to transform them into 3D point maps. Extensive experiments demonstrate that the proposed DC-BVFI framework surpasses state-of-the-art performance in simulated and real-world datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Explicit_Depth-Aware_Blurry_Video_Frame_Interpolation_Guided_by_Differential_Curves_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yan_Explicit_Depth-Aware_Blurry_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yan_2025_CVPR,
    author    = {Yan, Zaoming and Lei, Pengcheng and Wang, Tingting and Fang, Faming and Zhang, Junkang and Huang, Yaomin and Song, Haichuan},
    title     = {Explicit Depth-Aware Blurry Video Frame Interpolation Guided by Differential Curves},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1994-2004}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology, and the KLATASDS Shanghai Key Laboratory of MIP, East China Normal University", "city": "Shanghai", "country": "China"}]

affiliations:
["School of Computer Science and Technology, and the KLATASDS Shanghai Key Laboratory of MIP, East China Normal University"]


title: OFER: Occluded Face Expression Reconstruction
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26985-26995
cities: ["Amherst", "T\u00fcbingen", "Z\u00fcrich", "Redmond"]
countries: ["USA", "Germany", "Switzerland", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Selvaraju_OFER_Occluded_Face_Expression_Reconstruction_CVPR_2025_paper.html

authors:
Pratheba Selvaraju, Victoria Fernandez Abrevaya, Timo Bolkart, Rick Akkerman, Tianyu Ding, Faezeh Amjadi, Ilya Zharkov

abstract:
Reconstructing 3D face models from a single image is an inherently ill-posed problem, which becomes even more challenging in the presence of occlusions. In addition to fewer available observations, occlusions introduce an extra source of ambiguity where multiple reconstructions can be equally valid. Despite the ubiquity of the problem, very few methods address its multi-hypothesis nature. In this paper we introduce OFER, a novel approach for single-image 3D face reconstruction that can generate plausible, diverse, and expressive 3D faces, even under strong occlusions. Specifically, we train two diffusion models to generate a shape and expression coefficients of face parametric model, conditioned on the input image. This approach captures the multi-modal nature of the problem, generating a distribution of solutions as output. However, to maintain consistency across diverse expressions, the challenge is to select the best matching shape. To achieve this, we propose a novel ranking mechanism that sorts the outputs of the shape diffusion network based on predicted shape accuracy scores. We evaluate our method using standard benchmarks and introduce CO-545, a new protocol and dataset designed to assess the accuracy of expressive faces under occlusion. Our results show improved performance over occlusion-based methods, while also enabling the generation of diverse expressions for a given image.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Selvaraju_OFER_Occluded_Face_Expression_Reconstruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Selvaraju_OFER_Occluded_Face_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Selvaraju_2025_CVPR,
    author    = {Selvaraju, Pratheba and Abrevaya, Victoria Fernandez and Bolkart, Timo and Akkerman, Rick and Ding, Tianyu and Amjadi, Faezeh and Zharkov, Ilya},
    title     = {OFER: Occluded Face Expression Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26985-26995}
}

raw_affiliation:
[{"affiliation": "University of Massachusetts", "city": "Amherst", "country": "USA"}, {"affiliation": "Max Planck Institute for Intelligent Systems", "city": "T\u00fcbingen", "country": "Germany"}, {"affiliation": "Google Research", "city": "Z\u00fcrich", "country": "Switzerland"}, {"affiliation": "Microsoft Research", "city": "Redmond", "country": "USA"}]

affiliations:
["University of Massachusetts", "Max Planck Institute for Intelligent Systems", "Google Research", "Microsoft Research"]


title: SeaLion: Semantic Part-Aware Latent Point Diffusion Models for 3D Generation
authors: Dekai Zhu, Yan Di, Stefan Gavranovic, Slobodan Ilic
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11789-11798
affiliations: ["Technical University of Munich", "Siemens AG", "Munich Center for Machine Learning"]
cities: ["Munich", "Munich", "Munich"]
countries: ["Germany", "Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_SeaLion_Semantic_Part-Aware_Latent_Point_Diffusion_Models_for_3D_Generation_CVPR_2025_paper.html

abstract:
Denoising diffusion probabilistic models have achieved significant success in point cloud generation, enabling numerous downstream applications, such as generative data augmentation and 3D model editing. However, little attention has been given to generating point clouds with point-wise segmentation labels, as well as to developing evaluation metrics for this task. Therefore, in this paper, we present SeaLion, a novel diffusion model designed to generate high-quality and diverse point cloud with fine-grained segmentation labels. Specifically, we introduce the semantic part-aware latent point diffusion technique, which leverages the intermediate features of the generative models to jointly predict the noise for perturbed latent points and associated part segmentation labels during the denoising process, and subsequently decodes the latent points to point clouds conditioned on part segmentation labels. To effectively evaluate the quality of generated point clouds, we introduce a novel point cloud pairwise distance calculation method named part-aware Chamfer distance (p-CD). This method enables existing metrics, such as 1-NNA, to measure both the local structural quality and inter-part coherence of generated point clouds. Experiments on the large-scale synthetic dataset ShapeNet and real-world medical dataset IntrA, demonstrate that SeaLion achieves remarkable performance in generation quality and diversity, outperforming the existing state-of-the-art model, DiffFacto, by 13.33% and 6.52% on 1-NNA (p-CD) across the two datasets. Experimental analysis shows that SeaLion can be trained semi-supervised, thereby reducing the demand for labeling efforts. Lastly, we validate the applicability of SeaLion in generative data augmentation for training segmentation models and the capability of SeaLion to serve as a tool for part-aware 3D shape editing.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_SeaLion_Semantic_Part-Aware_Latent_Point_Diffusion_Models_for_3D_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_SeaLion_Semantic_Part-Aware_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Dekai and Di, Yan and Gavranovic, Stefan and Ilic, Slobodan},
    title     = {SeaLion: Semantic Part-Aware Latent Point Diffusion Models for 3D Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11789-11798}
}

raw_affiliation:
[{"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Siemens AG", "city": "Munich", "country": "Germany"}, {"affiliation": "Munich Center for Machine Learning", "city": "Munich", "country": "Germany"}]


title: MonSter: Marry Monodepth to Stereo Unleashes Power
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6273-6282
affiliations: ["Huazhong University of Science and Technology", "Autel Robotics", "Intel Labs"]
cities: ["Wuhan", "Shenzhen", "Santa Clara"]
countries: ["China", "China", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cheng_MonSter_Marry_Monodepth_to_Stereo_Unleashes_Power_CVPR_2025_paper.html

authors:
Junda Cheng, Longliang Liu, Gangwei Xu, Xianqi Wang, Zhaoxing Zhang, Yong Deng, Jinliang Zang, Yurui Chen, Zhipeng Cai, Xin Yang

abstract:
Stereo matching recovers depth from image correspondences. Existing methods struggle to handle ill-posed regions with limited matching cues, such as occlusions and textureless areas. To address this, we propose MonSter, a novel method that leverages the complementary strengths of monocular depth estimation and stereo matching. MonSter integrates monocular depth and stereo matching into a dual-branch architecture to iteratively improve each other. Confidence-based guidance adaptively selects reliable stereo cues for monodepth scale-shift recovery, and utilizes explicit monocular depth priors to enhance stereo matching at ill-posed regions. Such iterative mutual enhancement enables MonSter to evolve monodepth priors from coarse object-level structures to pixel-level geometry, fully unlocking the potential of stereo matching. As shown in Fig.2, MonSter ranks 1st across five most commonly used leaderboards --- SceneFlow, KITTI 2012, KITTI 2015, Middlebury, and ETH3D. Achieving up to 49.5% improvements over the previous best method  (Bad 1.0 on ETH3D). Comprehensive analysis verifies the effectiveness of MonSter in ill-posed regions. In terms of zero-shot generalization, MonSter significantly and consistently outperforms state-of-the-art methods across the board. Code will be released upon acceptance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cheng_MonSter_Marry_Monodepth_to_Stereo_Unleashes_Power_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Cheng_2025_CVPR,
    author    = {Cheng, Junda and Liu, Longliang and Xu, Gangwei and Wang, Xianqi and Zhang, Zhaoxing and Deng, Yong and Zang, Jinliang and Chen, Yurui and Cai, Zhipeng and Yang, Xin},
    title     = {MonSter: Marry Monodepth to Stereo Unleashes Power},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6273-6282}
}

raw_affiliation:
[{"affiliation": "Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "Autel Robotics", "city": "Shenzhen", "country": "China"}, {"affiliation": "Intel Labs", "city": "Santa Clara", "country": "USA"}]


title: Toward Real-world BEV Perception: Depth Uncertainty Estimation via Gaussian Splatting
authors: Shu-Wei Lu, Yi-Hsuan Tsai, Yi-Ting Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17124-17133
affiliations: ["National Yang Ming Chiao Tung University", "Atmanity Inc."]
cities: ["Hsinchu", "Taipei"]
countries: ["Taiwan", "Taiwan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lu_Toward_Real-world_BEV_Perception_Depth_Uncertainty_Estimation_via_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
Bird's-eye view (BEV) perception has gained significant attention because it provides a unified representation to fuse multiple view images and enables a wide range of downstream autonomous driving tasks, such as forecasting and planning. Recent state-of-the-art models utilize projection-based methods which formulate BEV perception as query learning to bypass explicit depth estimation. While we observe promising advancements in this paradigm, they still fall short of real-world applications because of the lack of uncertainty modeling and expensive computational requirement. In this work, we introduce GaussianLSS, an uncertainty-aware BEV perception framework that revisits the unprojection-based method, specifically the Lift-Splat-Shoot (LSS) paradigm, and enhances it with depth uncertainty modeling. Our GaussianLSS represents spatial dispersion by learning a soft depth mean and computing the variance of the depth distribution, which implicitly captures object extents. We then transform the depth distribution into 3D Gaussians and rasterize them to construct uncertainty-aware BEV features. We evaluate GaussianLSS on the nuScenes dataset, achieving state-of-the-art performance compared to unprojection-based methods. In particular, it provides significant advantages in speed, running 2x faster, and in memory efficiency, using 0.3x less memory compared to projection-based methods, while achieving competitive performance with only a 0.7% IoU difference.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_Toward_Real-world_BEV_Perception_Depth_Uncertainty_Estimation_via_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lu_Toward_Real-world_BEV_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lu_2025_CVPR,
    author    = {Lu, Shu-Wei and Tsai, Yi-Hsuan and Chen, Yi-Ting},
    title     = {Toward Real-world BEV Perception: Depth Uncertainty Estimation via Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17124-17133}
}

raw_affiliation:
[{"affiliation": "National Yang Ming Chiao Tung University", "city": "Hsinchu", "country": "Taiwan"}, {"affiliation": "Atmanity Inc.", "city": "Taipei", "country": "Taiwan"}]


title: Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models
authors: Reza Shirkavand, Peiran Yu, Shangqian Gao, Gowthami Somepalli, Tom Goldstein, Heng Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18619-18629
cities: ["College Park", "Arlington", "Tallahassee"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shirkavand_Efficient_Fine-Tuning_and_Concept_Suppression_for_Pruned_Diffusion_Models_CVPR_2025_paper.html

abstract:
Recent advances in diffusion generative models have yielded remarkable progress. While the quality of generated content continues to improve, these models have grown considerably in size and complexity. This increasing computational burden poses significant challenges, particularly in resource-constrained deployment scenarios such as mobile devices. The combination of model pruning and knowledge distillation has emerged as a promising solution to reduce computational demands while preserving generation quality. However, this technique inadvertently propagates undesirable behaviors, including the generation of copyrighted content and unsafe concepts, even when such instances are absent from the fine-tuning dataset. In this paper, we propose a novel bilevel optimization framework for pruned diffusion models that consolidates the fine-tuning and unlearning processes into a unified phase. Our approach maintains the principal advantages of distillation--namely, efficient convergence and style transfer capabilities--while selectively suppressing the generation of unwanted content. This plug-in framework is compatible with various pruning and concept unlearning methods, facilitating efficient, safe deployment of diffusion models in controlled environments.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shirkavand_Efficient_Fine-Tuning_and_Concept_Suppression_for_Pruned_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shirkavand_Efficient_Fine-Tuning_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shirkavand_2025_CVPR,
    author    = {Shirkavand, Reza and Yu, Peiran and Gao, Shangqian and Somepalli, Gowthami and Goldstein, Tom and Huang, Heng},
    title     = {Efficient Fine-Tuning and Concept Suppression for Pruned Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18619-18629}
}

raw_affiliation:
[{"affiliation": "University of Maryland, College Park", "city": "College Park", "country": "USA"}, {"affiliation": "University of Texas, Arlington", "city": "Arlington", "country": "USA"}, {"affiliation": "Florida State University", "city": "Tallahassee", "country": "USA"}]

affiliations:
["University of Maryland, College Park", "University of Texas, Arlington", "Florida State University"]


title: Cubify Anything: Scaling Indoor 3D Object Detection
authors: Justin Lazarow, David Griffiths, Gefen Kohavi, Francisco Crespo, Afshin Dehghan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22225-22233
raw_affiliation: [{"affiliation": "Apple", "city": "Cupertino", "country": "USA"}]
affiliations: ["Apple"]
cities: ["Cupertino"]
countries: ["USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lazarow_Cubify_Anything_Scaling_Indoor_3D_Object_Detection_CVPR_2025_paper.html

abstract:
We consider indoor 3D object detection with respect to a single RGB(-D) frame acquired from a commodity handheld device. We seek to significantly advance the status quo with respect to both data and modeling. First, we establish that existing datasets have significant limitations to scale, accuracy, and diversity of objects. As a result, we introduce the Cubify-Anything 1M (CA-1M) dataset, which exhaustively labels over 400K 3D objects on over 1K highly accurate laser-scanned scenes with near-perfect registration to over 3.5K handheld, egocentric captures. Next, we establish Cubify Transformer (CuTR), a fully Transformer 3D object detection baseline which rather than operating in 3D on point or voxel-based representations, predicts 3D boxes directly from 2D features derived from RGB(-D) inputs. While this approach lacks any 3D inductive biases, we show that paired with CA-1M, CuTR outperforms point-based methods, accurately recalling over 62% of objects in 3D, and is significantly more capable at handling noise and uncertainty present in commodity LiDAR-derived depth maps while also providing promising RGB only performance without architecture changes. Furthermore, by pre-training on CA-1M, CuTR can outperform point-based methods on a more diverse variant of SUN RGB-D, supporting the notion that while inductive biases in 3D are useful at the smaller sizes of existing datasets, they fail to scale to the data-rich regime of CA-1M. Overall, this dataset and baseline model provide strong evidence that we are moving towards models which can effectively Cubify Anything.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lazarow_Cubify_Anything_Scaling_Indoor_3D_Object_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lazarow_Cubify_Anything_Scaling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lazarow_2025_CVPR,
    author    = {Lazarow, Justin and Griffiths, David and Kohavi, Gefen and Crespo, Francisco and Dehghan, Afshin},
    title     = {Cubify Anything: Scaling Indoor 3D Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22225-22233}
}


title: WildGS-SLAM: Monocular Gaussian Splatting SLAM in Dynamic Environments
authors: Jianhao Zheng, Zihan Zhu, Valentin Bieri, Marc Pollefeys, Songyou Peng, Iro Armeni
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11461-11471
affiliations: ["Stanford University", "ETH Z\u00fcrich", "Microsoft"]
cities: ["Stanford", "Z\u00fcrich", "Redmond"]
countries: ["USA", "Switzerland", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zheng_WildGS-SLAM_Monocular_Gaussian_Splatting_SLAM_in_Dynamic_Environments_CVPR_2025_paper.html

abstract:
We present WildGS-SLAM, a robust and efficient monocular RGB SLAM system designed to handle dynamic environments by leveraging uncertainty-aware geometric mapping. Unlike traditional SLAM systems, which assume static scenes, our approach integrates depth and uncertainty information to enhance tracking, mapping, and rendering performance in the presence of moving objects. We introduce an uncertainty map, predicted by a shallow multi-layer perceptron and DINOv2 features, to guide dynamic object removal during both tracking and mapping.This uncertainty map enhances dense bundle adjustment and Gaussian map optimization, improving reconstruction accuracy. Our system is evaluated on multiple datasets and demonstrates artifact-free view synthesis. Results showcase WildGS-SLAM's superior performance in dynamic environments compared to state-of-the-art methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zheng_WildGS-SLAM_Monocular_Gaussian_Splatting_SLAM_in_Dynamic_Environments_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zheng_WildGS-SLAM_Monocular_Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zheng_2025_CVPR,
    author    = {Zheng, Jianhao and Zhu, Zihan and Bieri, Valentin and Pollefeys, Marc and Peng, Songyou and Armeni, Iro},
    title     = {WildGS-SLAM: Monocular Gaussian Splatting SLAM in Dynamic Environments},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11461-11471}
}

raw_affiliation:
[{"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "ETH Z\u00fcrich", "city": "Z\u00fcrich", "country": "Switzerland"}, {"affiliation": "Microsoft", "city": "Redmond", "country": "USA"}]


title: A Tale of Two Classes: Adapting Supervised Contrastive Learning to Binary Imbalanced Datasets
authors: David Mildenberger, Paul Hager, Daniel Rueckert, Martin J. Menten
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10305-10314
affiliations: ["Technical University of Munich", "Munich Center for Machine Learning", "Imperial College London"]
cities: ["Munich", "Munich", "London"]
countries: ["Germany", "Germany", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mildenberger_A_Tale_of_Two_Classes_Adapting_Supervised_Contrastive_Learning_to_CVPR_2025_paper.html

abstract:
Supervised contrastive learning (SupCon) has proven to be a powerful alternative to the standard cross-entropy loss for classification of multi-class balanced datasets. However, it struggles to learn well-conditioned representations of datasets with long-tailed class distributions. This problem is potentially exacerbated for binary imbalanced distributions, which are commonly encountered during many real-world problems such as medical diagnosis. In experiments on seven binary datasets of natural and medical images, we show that the performance of SupCon decreases with increasing class imbalance. To substantiate these findings, we introduce two novel metrics that evaluate the quality of the learned representation space. By measuring the class distribution in local neighborhoods, we are able to uncover structural deficiencies of the representation space that classical metrics cannot detect. Informed by these insights, we propose two new supervised contrastive learning strategies tailored to binary imbalanced datasets that improve the structure of the representation space and increase downstream classification accuracy over standard SupCon by up to 35%.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mildenberger_A_Tale_of_Two_Classes_Adapting_Supervised_Contrastive_Learning_to_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mildenberger_A_Tale_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mildenberger_2025_CVPR,
    author    = {Mildenberger, David and Hager, Paul and Rueckert, Daniel and Menten, Martin J.},
    title     = {A Tale of Two Classes: Adapting Supervised Contrastive Learning to Binary Imbalanced Datasets},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10305-10314}
}

raw_affiliation:
[{"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Munich Center for Machine Learning", "city": "Munich", "country": "Germany"}, {"affiliation": "Imperial College London", "city": "London", "country": "UK"}]


title: DEAL: Data-Efficient Adversarial Learning for High-Quality Infrared Imaging
authors: Zhu Liu, Zijun Wang, Jinyuan Liu, Fanqi Meng, Long Ma, Risheng Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28198-28207
cities: ["Dalian", "Dalian"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_DEAL_Data-Efficient_Adversarial_Learning_for_High-Quality_Infrared_Imaging_CVPR_2025_paper.html

abstract:
Thermal imaging is often compromised by dynamic, complex degradations caused by hardware limitations and unpredictable environmental factors. The scarcity of high-quality infrared data, coupled with the challenges of dynamic, intricate degradations, makes it difficult to recover   details using existing methods. In this paper, we introduce thermal degradation simulation integrated into the training process via a mini-max optimization, by modeling these degraded factors as adversarial attacks on thermal images. The simulation is dynamic to maximize objective functions, thus capturing a broad spectrum of degraded data distributions. This approach enables training with limited data, thereby improving model performance. Additionally, we introduce a dual-interaction network that combines the benefits of spiking neural networks with scale transformation to capture degraded features with sharp spike signal intensities. This architecture ensures compact model parameters while preserving efficient feature representation. Extensive experiments demonstrate that our method not only achieves superior visual quality under diverse single and composited degradation, but also delivers a significant reduction in processing when trained on only fifty clear images, outperforming existing techniques in efficiency and accuracy. The source code  will be available at https://github.com/LiuZhu-CV/DEAL.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_DEAL_Data-Efficient_Adversarial_Learning_for_High-Quality_Infrared_Imaging_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Zhu and Wang, Zijun and Liu, Jinyuan and Meng, Fanqi and Ma, Long and Liu, Risheng},
    title     = {DEAL: Data-Efficient Adversarial Learning for High-Quality Infrared Imaging},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28198-28207}
}

raw_affiliation:
[{"affiliation": "School of Software Technology, Dalian University of Technology", "city": "Dalian", "country": "China"}, {"affiliation": "School of Mechanical Engineering, Dalian University of Technology", "city": "Dalian", "country": "China"}]

affiliations:
["School of Software Technology, Dalian University of Technology", "School of Mechanical Engineering, Dalian University of Technology"]


title: RePerformer: Immersive Human-centric Volumetric Videos from Playback to Photoreal Reperformance
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11349-11360
cities: ["Shanghai", "Shanghai", "Beijing", "Unknown", "Saarbrucken"]
countries: ["China", "China", "China", "Unknown", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_RePerformer_Immersive_Human-centric_Volumetric_Videos_from_Playback_to_Photoreal_Reperformance_CVPR_2025_paper.html

authors:
Yuheng Jiang, Zhehao Shen, Chengcheng Guo, Yu Hong, Zhuo Su, Yingliang Zhang, Marc Habermann, Lan Xu

abstract:
Human-centric volumetric videos offer immersive free-viewpoint experiences, yet existing methods focus either on replaying general dynamic scenes or animating human avatars, limiting their ability to re-perform general dynamic scenes. In this paper, we present RePerformer, a novel Gaussian-based representation that unifies playback and re-performance for high-fidelity human-centric volumetric videos. Specifically, we hierarchically disentangle the dynamic scenes into motion Gaussians and appearance Gaussians which are associated in the canonical space. We further employ a Morton-based parameterization to efficiently encode the appearance Gaussians into 2D position and attribute maps. For enhanced generalization, we adopt 2D CNNs to map position maps to attribute maps, which can be assembled into appearance Gaussians for high-fidelity rendering of the dynamic scenes. For re-performance, we develop a semantic-aware alignment module and apply deformation transfer on motion Gaussians, enabling photo-real rendering under novel motions. Extensive experiments validate the robustness and effectiveness of RePerformer, setting a new benchmark for playback-then-reperformance paradigm in human-centric volumetric videos.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_RePerformer_Immersive_Human-centric_Volumetric_Videos_from_Playback_to_Photoreal_Reperformance_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_RePerformer_Immersive_Human-centric_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Yuheng and Shen, Zhehao and Guo, Chengcheng and Hong, Yu and Su, Zhuo and Zhang, Yingliang and Habermann, Marc and Xu, Lan},
    title     = {RePerformer: Immersive Human-centric Volumetric Videos from Playback to Photoreal Reperformance},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11349-11360}
}

raw_affiliation:
[{"affiliation": "ShanghaiTech University", "city": "Shanghai", "country": "China"}, {"affiliation": "NeuDim", "city": "Shanghai", "country": "China"}, {"affiliation": "ByteDance", "city": "Beijing", "country": "China"}, {"affiliation": "DGene", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Max Planck Institute for Informatics, Saarland Informatics Campus", "city": "Saarbrucken", "country": "Germany"}]

affiliations:
["ShanghaiTech University", "NeuDim", "ByteDance", "DGene", "Max Planck Institute for Informatics, Saarland Informatics Campus"]


title: CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning
authors: Yang Yue, Yulin Wang, Chenxin Tao, Pan Liu, Shiji Song, Gao Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20778-20788
affiliations: ["Tsinghua University", "PLA General Hospital"]
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yue_CheXWorld_Exploring_Image_World_Modeling_for_Radiograph_Representation_Learning_CVPR_2025_paper.html

abstract:
Humans can develop internal world models that encode common sense knowledge, telling them how the world works and predicting the consequences of their actions. This concept has emerged as a promising direction for establishing general-purpose machine-learning models in recent preliminary works, e.g., for visual representation learning. In this paper, we present CheXWorld, the first effort towards a self-supervised world model for radiographic images. Specifically, our work develops a unified framework that simultaneously models three aspects of medical knowledge essential for qualified radiologists, including 1) local anatomical structures describing the fine-grained characteristics of local tissues (e.g., architectures, shapes, and textures); 2) global anatomical layouts describing the global organization of the human body (e.g., layouts of organs and skeletons); and 3) domain variations that encourage CheXWorld to model the transitions across different appearance domains of radiographs (e.g., varying clarity, contrast, and exposure caused by collecting radiographs from different hospitals, devices, or patients). Empirically, we design tailored qualitative and quantitative analyses, revealing that CheXWorld successfully captures these three dimensions of medical knowledge. Furthermore, transfer learning experiments across eight medical image classification and segmentation benchmarks showcase that CheXWorld significantly outperforms existing SSL methods and large-scale medical foundation models.  Code & pre-trained models are available at https://github.com/LeapLabTHU/CheXWorld.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yue_CheXWorld_Exploring_Image_World_Modeling_for_Radiograph_Representation_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yue_CheXWorld_Exploring_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yue_2025_CVPR,
    author    = {Yue, Yang and Wang, Yulin and Tao, Chenxin and Liu, Pan and Song, Shiji and Huang, Gao},
    title     = {CheXWorld: Exploring Image World Modeling for Radiograph Representation Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20778-20788}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "PLA General Hospital", "city": "Beijing", "country": "China"}]


title: Towards Long-Horizon Vision-Language Navigation: Platform, Benchmark and Method
authors: Xinshuai Song, Weixing Chen, Yang Liu, Weikai Chen, Guanbin Li, Liang Lin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12078-12088
cities: ["Guangzhou", "Shenzhen", "Guangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Song_Towards_Long-Horizon_Vision-Language_Navigation_Platform_Benchmark_and_Method_CVPR_2025_paper.html

abstract:
Existing Vision-Language Navigation (VLN) methods primarily focus on single-stage navigation, limiting their effectiveness in multi-stage and long-horizon tasks within complex and dynamic environments. To address these limitations, we propose a novel VLN task, named Long-Horizon Vision-Language Navigation (LH-VLN), which emphasizes long-term planning and decision consistency across consecutive subtasks. Furthermore, to support LH-VLN, we develop an automated data generation platform NavGen, which constructs datasets with complex task structures and improves data utility through a bidirectional, multi-granularity generation approach. To accurately evaluate complex tasks, we construct the Long-Horizon Planning and Reasoning in VLN (LHPR-VLN) benchmark consisting of 3,260 tasks with an average of 150 task steps, serving as the first dataset specifically designed for the long-horizon vision-language navigation task. Furthermore, we propose Independent Success Rate (ISR), Conditional Success Rate (CSR), and CSR weight by Ground Truth (CGT) metrics, to provide fine-grained assessments of task completion. To improve model adaptability in complex tasks, we propose a novel Multi-Granularity Dynamic Memory (MGDM) module that integrates short-term memory blurring with long-term memory retrieval to enable flexible navigation in dynamic environments. Our platform, benchmark and method supply LH-VLN with a robust data generation pipeline, comprehensive model evaluation dataset, reasonable metrics, and a novel VLN model, establishing a foundational framework for advancing LH-VLN.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Song_Towards_Long-Horizon_Vision-Language_Navigation_Platform_Benchmark_and_Method_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Song_Towards_Long-Horizon_Vision-Language_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Song_2025_CVPR,
    author    = {Song, Xinshuai and Chen, Weixing and Liu, Yang and Chen, Weikai and Li, Guanbin and Lin, Liang},
    title     = {Towards Long-Horizon Vision-Language Navigation: Platform, Benchmark and Method},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12078-12088}
}

raw_affiliation:
[{"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "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", "Peng Cheng Laboratory", "Guangdong Key Laboratory of Big Data Analysis and Processing"]


title: Learning Class Prototypes for Unified Sparse-Supervised 3D Object Detection
authors: Yun Zhu, Le Hui, Hang Yang, Jianjun Qian, Jin Xie, Jian Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9911-9920
cities: ["Nanjing", "Xi'an", "Nanjing", "Suzhou"]
countries: ["China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_Learning_Class_Prototypes_for_Unified_Sparse-Supervised_3D_Object_Detection_CVPR_2025_paper.html

abstract:
Both indoor and outdoor scene perceptions are essential for embodied intelligence. However, current sparse supervised 3D object detection methods focus solely on outdoor scenes without considering indoor settings. To this end, we propose a unified sparse supervised 3D object detection method for both indoor and outdoor scenes through learning class prototypes to effectively utilize unlabeled objects. Specifically, we first propose a prototype-based object mining module that converts the  unlabeled object mining into a matching problem between class prototypes and unlabeled features. By using optimal transport matching results, we assign prototype labels to high-confidence features, thereby achieving the mining of unlabeled objects. We then present a multi-label cooperative refinement module to effectively recover missed detections in sparse supervised 3D object detection through pseudo label quality control and prototype label cooperation. Experiments show that our method achieves state-of-the-art performance under the one object per scene sparse supervised setting across indoor and outdoor datasets. With only one labeled object per scene, our method achieves about 78%,  90%, and  96% performance compared to the fully supervised detector on ScanNet V2, SUN RGB-D, and KITTI, respectively, highlighting the scalability of our method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_Learning_Class_Prototypes_for_Unified_Sparse-Supervised_3D_Object_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_Learning_Class_Prototypes_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Yun and Hui, Le and Yang, Hang and Qian, Jianjun and Xie, Jin and Yang, Jian},
    title     = {Learning Class Prototypes for Unified Sparse-Supervised 3D Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9911-9920}
}

raw_affiliation:
[{"affiliation": "PCA Lab, Nanjing University of Science and Technology", "city": "Nanjing", "country": "China"}, {"affiliation": "School of Electronics and Information, Northwestern Polytechnical University", "city": "Xi'an", "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 Electronics and Information, Northwestern Polytechnical University", "State Key Laboratory for Novel Software Technology, Nanjing University", "School of Intelligence Science and Technology, Nanjing University"]


title: BimArt: A Unified Approach for the Synthesis of 3D Bimanual Interaction with Articulated Objects
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27694-27705
affiliations: ["MPI for Informatics, SIC 2VIA Center", "Google"]
cities: ["Saarbr\u00fccken", "Mountain View"]
countries: ["Germany", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_BimArt_A_Unified_Approach_for_the_Synthesis_of_3D_Bimanual_CVPR_2025_paper.html

authors:
Wanyue Zhang, Rishabh Dabral, Vladislav Golyanik, Vasileios Choutas, Eduardo Alvarado, Thabo Beeler, Marc Habermann, Christian Theobalt

abstract:
We present BimArt, a novel generative approach for synthesizing 3D bimanual hand interactions with articulated objects. Unlike prior works, we do not rely on a reference grasp, a coarse hand trajectory, or separate modes for grasping and articulating. To achieve this, we first generate distance-based contact maps conditioned on the object trajectory with an articulation-aware feature representation, revealing rich bimanual patterns for manipulation. The learned contact prior is then used to guide our hand motion generator, producing diverse and realistic bimanual motions for object movement and articulation. Our work offers key insights into feature representation and contact prior for articulated objects, demonstrating their effectiveness in taming the complex, high-dimensional space of bimanual hand-object interactions. Through comprehensive quantitative experiments, we demonstrate a clear step towards simplified and high-quality hand-object animations that excel over the state-of-the-art in motion quality and diversity.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_BimArt_A_Unified_Approach_for_the_Synthesis_of_3D_Bimanual_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_BimArt_A_Unified_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Wanyue and Dabral, Rishabh and Golyanik, Vladislav and Choutas, Vasileios and Alvarado, Eduardo and Beeler, Thabo and Habermann, Marc and Theobalt, Christian},
    title     = {BimArt: A Unified Approach for the Synthesis of 3D Bimanual Interaction with Articulated Objects},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27694-27705}
}

raw_affiliation:
[{"affiliation": "MPI for Informatics, SIC 2VIA Center", "city": "Saarbr\u00fccken", "country": "Germany"}, {"affiliation": "Google", "city": "Mountain View", "country": "USA"}]


title: Open-World Amodal Appearance Completion
authors: Jiayang Ao, Yanbei Jiang, Qiuhong Ke, Krista A. Ehinger
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6490-6499
affiliations: ["The University of Melbourne", "Monash University"]
cities: ["Melbourne", "Melbourne"]
countries: ["Australia", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ao_Open-World_Amodal_Appearance_Completion_CVPR_2025_paper.html

abstract:
Understanding and reconstructing occluded objects is a challenging problem, especially in open-world scenarios where categories and contexts are diverse and unpredictable. Traditional methods, however, are typically restricted to closed sets of object categories, limiting their use in complex, open-world scenes. We introduce Open-World Amodal Appearance Completion, a training-free framework that expands amodal completion capabilities by accepting flexible text queries as input. Our approach generalizes to arbitrary objects specified by both direct terms and abstract queries. We term this capability reasoning amodal completion, where the system reconstructs the full appearance of the queried object based on the provided image and language query. Our framework unifies segmentation, occlusion analysis, and inpainting to handle complex occlusions and generates completed objects as RGBA elements, enabling seamless integration into applications such as 3D reconstruction and image editing. Extensive evaluations demonstrate the effectiveness of our approach in generalizing to novel objects and occlusions, establishing a new benchmark for amodal completion in open-world settings. Code and datasets available: https://github.com/saraao/amodal.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ao_Open-World_Amodal_Appearance_Completion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ao_Open-World_Amodal_Appearance_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ao_2025_CVPR,
    author    = {Ao, Jiayang and Jiang, Yanbei and Ke, Qiuhong and Ehinger, Krista A.},
    title     = {Open-World Amodal Appearance Completion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6490-6499}
}

raw_affiliation:
[{"affiliation": "The University of Melbourne", "city": "Melbourne", "country": "Australia"}, {"affiliation": "Monash University", "city": "Melbourne", "country": "Australia"}]


