# papers

title: Perception Tokens Enhance Visual Reasoning in Multimodal Language Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3836-3845
affiliations: ["University of Washington", "Google Research"]
cities: ["Seattle", "Mountain View"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bigverdi_Perception_Tokens_Enhance_Visual_Reasoning_in_Multimodal_Language_Models_CVPR_2025_paper.html

authors:
Mahtab Bigverdi, Zelun Luo, Cheng-Yu Hsieh, Ethan Shen, Dongping Chen, Linda G. Shapiro, Ranjay Krishna

abstract:
Multimodal language models (MLMs) still face challenges in fundamental visual perception tasks where specialized models excel. Tasks requiring reasoning about 3D structures benefit from depth estimation, and reasoning about 2D object instances benefits from object detection. Yet, MLMs can not produce intermediate depth or boxes to reason over. Finetuning MLMs on relevant data doesn't generalize well and outsourcing computation to specialized vision tools is too compute-intensive and memory-inefficient. To address this, we introduce Perception Tokens, intrinsic image representations designed to assist reasoning tasks where language is insufficient. Perception tokens act as auxiliary reasoning tokens, akin to chain-of-thought prompts in language models. For example, in a depth-related task, an MLM augmented with perception tokens can reason by generating a depth map as tokens, enabling it to solve the problem effectively. We propose AURORA, a training method that augments MLMs with perception tokens for improved reasoning over visual inputs. AUROA leverages a VQVAE to transform intermediate image representations, such as depth maps into a tokenized format and bounding box tokens, which is then used in a multi-task training framework. AURORA achieves notable improvements across counting benchmarks: +10.8% on BLINK, +11.3% on CVBench, and +8.3% on SEED-Bench, outperforming finetuning approaches in generalization across datasets. It also improves on relative depth: over +6% on BLINK. With perception tokens, AURORA expands the scope of MLMs beyond language-based reasoning, paving the way for more effective visual reasoning capabilities. Code and data will be released at the https://aurora-perception.github.io.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bigverdi_Perception_Tokens_Enhance_Visual_Reasoning_in_Multimodal_Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bigverdi_Perception_Tokens_Enhance_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bigverdi_2025_CVPR,
    author    = {Bigverdi, Mahtab and Luo, Zelun and Hsieh, Cheng-Yu and Shen, Ethan and Chen, Dongping and Shapiro, Linda G. and Krishna, Ranjay},
    title     = {Perception Tokens Enhance Visual Reasoning in Multimodal Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3836-3845}
}

raw_affiliation:
[{"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}, {"affiliation": "Google Research", "city": "Mountain View", "country": "USA"}]


title: Are Images Indistinguishable to Humans Also Indistinguishable to Classifiers?
authors: Zebin You, Xinyu Zhang, Hanzhong Guo, Jingdong Wang, Chongxuan Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28790-28800
cities: ["Beijing", "Beijing", "Adelaide", "Beijing"]
countries: ["China", "China", "Australia", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/You_Are_Images_Indistinguishable_to_Humans_Also_Indistinguishable_to_Classifiers_CVPR_2025_paper.html

abstract:
The ultimate goal of generative models is to perfectly capture the data distribution. For image generation, common metrics of visual quality (e.g., FID) and the perceived truthfulness of generated images seem to suggest that we are nearing this goal. However, through distribution classification tasks, we reveal that, from the perspective of neural network-based classifiers, even advanced diffusion models are still far from this goal. Specifically, classifiers are able to consistently and effortlessly distinguish real images from generated ones across various settings. Moreover, we uncover an intriguing discrepancy: classifiers can easily differentiate between diffusion models with comparable performance (e.g., U-ViT-H vs. DiT-XL), but struggle to distinguish between models within the same family but of different scales (e.g., EDM2-XS vs. EDM2-XXL). Our methodology carries several important implications. First, it naturally serves as a diagnostic tool for diffusion models by analyzing specific features of generated data. Second, it sheds light on the model autophagy disorder and offers insights into the use of generated data: augmenting real data with generated data is more effective than replacing it. Third, classifier guidance can significantly enhance the realism of generated images.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/You_Are_Images_Indistinguishable_to_Humans_Also_Indistinguishable_to_Classifiers_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/You_Are_Images_Indistinguishable_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{You_2025_CVPR,
    author    = {You, Zebin and Zhang, Xinyu and Guo, Hanzhong and Wang, Jingdong and Li, Chongxuan},
    title     = {Are Images Indistinguishable to Humans Also Indistinguishable to Classifiers?},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28790-28800}
}

raw_affiliation:
[{"affiliation": "Gaoling School of Artificial Intelligence, Renmin University of China", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing Key Laboratory of Big Data Management and Analysis Methods", "city": "Beijing", "country": "China"}, {"affiliation": "The University of Adelaide", "city": "Adelaide", "country": "Australia"}, {"affiliation": "Baidu VIS", "city": "Beijing", "country": "China"}]

affiliations:
["Gaoling School of Artificial Intelligence, Renmin University of China", "Beijing Key Laboratory of Big Data Management and Analysis Methods", "The University of Adelaide", "Baidu VIS"]


title: X-Dyna: Expressive Dynamic Human Image Animation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5499-5509
cities: ["Los Angeles", "Beijing", "Stanford", "Los Angeles", "San Diego"]
countries: ["USA", "China", "USA", "USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chang_X-Dyna_Expressive_Dynamic_Human_Image_Animation_CVPR_2025_paper.html

authors:
Di Chang, Hongyi Xu, You Xie, Yipeng Gao, Zhengfei Kuang, Shengqu Cai, Chenxu Zhang, Guoxian Song, Chao Wang, Yichun Shi, Zeyuan Chen, Shijie Zhou, Linjie Luo, Gordon Wetzstein, Mohammad Soleymani

abstract:
We introduce X-Dyna, a novel zero-shot, diffusion-based pipeline for animating a single human image using facial expressions and body movements derived from a driving video, that generates realistic, context-aware dynamics for both the subject and the surrounding environment. Building on prior approaches centered on human pose control, X-Dyna addresses key factors underlying the loss of dynamic details, enhancing the lifelike qualities of human video animations.At the core of our approach is the Dynamics-Adapter, a lightweight module that effectively integrates reference appearance context into the spatial attentions of the diffusion backbone while preserving the capacity of motion modules in synthesizing fluid and intricate dynamic details. Beyond body pose control, we connect a local control module with our model to capture identity-disentangled facial expressions, facilitating accurate expression transfer for enhanced realism in animated scenes. Together, these components form a unified framework capable of learning physical human motion and natural scene dynamics from a diverse blend of human and scene videos. Comprehensive qualitative and quantitative evaluations demonstrate that X-Dyna outperforms state-of-the-art methods, creating highly lifelike and expressive animations. The code is available at https://github.com/bytedance/X-Dyna

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chang_X-Dyna_Expressive_Dynamic_Human_Image_Animation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chang_X-Dyna_Expressive_Dynamic_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Chang_2025_CVPR,
    author    = {Chang, Di and Xu, Hongyi and Xie, You and Gao, Yipeng and Kuang, Zhengfei and Cai, Shengqu and Zhang, Chenxu and Song, Guoxian and Wang, Chao and Shi, Yichun and Chen, Zeyuan and Zhou, Shijie and Luo, Linjie and Wetzstein, Gordon and Soleymani, Mohammad},
    title     = {X-Dyna: Expressive Dynamic Human Image Animation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5499-5509}
}

raw_affiliation:
[{"affiliation": "University of Southern California", "city": "Los Angeles", "country": "USA"}, {"affiliation": "ByteDance", "city": "Beijing", "country": "China"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "University of California Los Angeles", "city": "Los Angeles", "country": "USA"}, {"affiliation": "University of California San Diego", "city": "San Diego", "country": "USA"}]

affiliations:
["University of Southern California", "ByteDance", "Stanford University", "University of California Los Angeles", "University of California San Diego"]


title: Understanding Multi-layered Transmission Matrices
authors: Anat Levin, Marina Alterman
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23164-23173
affiliations: ["Department of Electrical and Computer Engineering, Technion"]
cities: ["Haifa"]
countries: ["Israel"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Levin_Understanding_Multi-layered_Transmission_Matrices_CVPR_2025_paper.html

abstract:
Transmission matrices, mapping the propagation of light from one end of the tissue to the other, form an important mathematical tool in the analysis of tissue scattering and the design of wavefront shaping systems. To understand the relationship between their content and the volumetric structure of the tissue, we wish to fit them with multi-slice models, composed of a set of planar aberrations spaced throughout the volume. The number of layers used in such a model would largely affect the amount of information compression and the ease in which we can use such layered models in a wavefront-shaping system. This work offers a theoretical study of such multi-layered models. We attempt to understand how many layers are required for a good fit, and how does the approximation degrade when a smaller number of such layers is used.  We show analytically that transmission matrices can be well fitted with very sparse layers. This leads to optimistic predictions on our ability to use them to design future wavefront shaping systems which can correct tissue aberration over a wide field-of-view.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Levin_Understanding_Multi-layered_Transmission_Matrices_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Levin_Understanding_Multi-layered_Transmission_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Levin_2025_CVPR,
    author    = {Levin, Anat and Alterman, Marina},
    title     = {Understanding Multi-layered Transmission Matrices},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23164-23173}
}

raw_affiliation:
[{"affiliation": "Department of Electrical and Computer Engineering, Technion", "city": "Haifa", "country": "Israel"}]


authors: Weikang Bian, Zhaoyang Huang, Xiaoyu Shi, Yijin Li, Fu-Yun Wang, Hongsheng Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21717-21727
affiliations: ["CUHK MMLab", "CPII under InnoHK", "A volution AI"]
cities: ["Hong Kong", "Hong Kong", "Unknown"]
countries: ["China", "China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bian_GS-DiT_Advancing_Video_Generation_with_Dynamic_3D_Gaussian_Fields_through_CVPR_2025_paper.html

title:
GS-DiT: Advancing Video Generation with Dynamic 3D Gaussian Fields through Efficient Dense 3D Point Tracking

abstract:
4D video control is essential in video generation as it enables the use of sophisticated lens techniques, such as multi-camera shooting and dolly zoom, which are currently unsupported by existing methods. Training a video Diffusion Transformer (DiT) directly to control 4D content requires expensive multi-view videos. Inspired by Monocular Dynamic novel View Synthesis (MDVS) that optimizes a 4D representation and renders videos according to different 4D elements, such as camera pose and object motion editing, we bring dynamic 3D Gaussian fields to video generation. Specifically, we propose a novel framework that constructs dynamic 3D Gaussian fields with dense 3D point tracking and renders the Gaussian field for all video frames. Then we finetune a pretrained DiT to generate videos following the guidance of the rendered video, dubbed as GS-DiT. To boost the training of the GS-DiT, we also propose an efficient Dense 3D Point Tracking (D3D-PT) method for the dynamic 3D Gaussian field construction. Our D3D-PT outperforms SpatialTracker, the state-of-the-art sparse 3D point tracking method, in accuracy and accelerates the inference speed by two orders of magnitude. During the inference stage, GS-DiT can generate videos with the same dynamic content while adhering to different camera parameters, addressing a significant limitation of current video generation models. GS-DiT demonstrates strong generalization capabilities and extends the 4D controllability of Gaussian splatting to video generation beyond just camera poses. It supports advanced cinematic effects through the manipulation of the Gaussian field and camera intrinsics, making it a powerful tool for creative video production. Demos are available at https://wkbian.github.io/Projects/GS-DiT/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bian_GS-DiT_Advancing_Video_Generation_with_Dynamic_3D_Gaussian_Fields_through_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bian_GS-DiT_Advancing_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bian_2025_CVPR,
    author    = {Bian, Weikang and Huang, Zhaoyang and Shi, Xiaoyu and Li, Yijin and Wang, Fu-Yun and Li, Hongsheng},
    title     = {GS-DiT: Advancing Video Generation with Dynamic 3D Gaussian Fields through Efficient Dense 3D Point Tracking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21717-21727}
}

raw_affiliation:
[{"affiliation": "CUHK MMLab", "city": "Hong Kong", "country": "China"}, {"affiliation": "CPII under InnoHK", "city": "Hong Kong", "country": "China"}, {"affiliation": "A volution AI", "city": "Unknown", "country": "Unknown"}]


title: AnyMoLe: Any Character Motion In-betweening Leveraging Video Diffusion Models
authors: Kwan Yun, Seokhyeon Hong, Chaelin Kim, Junyong Noh
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27838-27848
raw_affiliation: [{"affiliation": "KAIST, Visual Media Lab", "city": "Daejeon", "country": "South Korea"}]
affiliations: ["KAIST, Visual Media Lab"]
cities: ["Daejeon"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yun_AnyMoLe_Any_Character_Motion_In-betweening_Leveraging_Video_Diffusion_Models_CVPR_2025_paper.html

abstract:
Despite recent advancements in learning-based motion in-betweening, a key limitation has been overlooked: the requirement for character-specific datasets. In this work, we introduce AnyMoLe, a novel method that addresses this limitation by leveraging video diffusion models to generate motion in-between frames for arbitrary characters without external data. Our approach employs a two-stage frame generation process to enhance contextual understanding. Furthermore, to bridge the domain gap between real-world and rendered character animations, we introduce ICAdapt, a fine-tuning technique for video diffusion models. Additionally, we propose a "motion-video mimicking" optimization technique, enabling seamless motion generation for characters with arbitrary joint structures using 2D and 3D-aware features. AnyMoLe significantly reduces data dependency while generating smooth and realistic transitions, making it applicable to a wide range of motion in-betweening tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yun_AnyMoLe_Any_Character_Motion_In-betweening_Leveraging_Video_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yun_AnyMoLe_Any_Character_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yun_2025_CVPR,
    author    = {Yun, Kwan and Hong, Seokhyeon and Kim, Chaelin and Noh, Junyong},
    title     = {AnyMoLe: Any Character Motion In-betweening Leveraging Video Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27838-27848}
}


title: Towards Optimizing Large-Scale Multi-Graph Matching in Bioimaging
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11569-11578
affiliations: ["Heidelberg University", "University of Bonn"]
cities: ["Heidelberg", "Bonn"]
countries: ["Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kahl_Towards_Optimizing_Large-Scale_Multi-Graph_Matching_in_Bioimaging_CVPR_2025_paper.html

authors:
Max Kahl, Sebastian Stricker, Lisa Hutschenreiter, Florian Bernard, Carsten Rother, Bogdan Savchynskyy

abstract:
Multi-graph matching is an important problem in computer vision. Our task comes from bioimaging, where a set of 100 3D-microscopic images of worms have to be brought into correspondence. Surprisingly, virtually all existing methods are not applicable to this large-scale, real-world problem since they either assume a complete or dense problem setting, and they have so far only been applied to small-scale, toy or synthetic problems. Despite claims in literature that methods addressing complete multi-graph matching are applicable in an incomplete setting, our first contribution is to prove that their runtime would be excessive and impractical.  Our second contribution is a new method for incomplete multi-graph matching that applies to real-world, larger-scale problems. We experimentally show that for our bioimaging application we are able to attain results in less than two minutes, whereas the only competing approach requires at least half an hour while producing far worse results. Furthermore, even for small-scale, dense or complete problem instances we achieve results that are at least on par with the leading methods, but an order of magnitude faster.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kahl_Towards_Optimizing_Large-Scale_Multi-Graph_Matching_in_Bioimaging_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kahl_Towards_Optimizing_Large-Scale_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kahl_2025_CVPR,
    author    = {Kahl, Max and Stricker, Sebastian and Hutschenreiter, Lisa and Bernard, Florian and Rother, Carsten and Savchynskyy, Bogdan},
    title     = {Towards Optimizing Large-Scale Multi-Graph Matching in Bioimaging},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11569-11578}
}

raw_affiliation:
[{"affiliation": "Heidelberg University", "city": "Heidelberg", "country": "Germany"}, {"affiliation": "University of Bonn", "city": "Bonn", "country": "Germany"}]


authors: Li Lun, Kunyu Feng, Qinglong Ni, Ling Liang, Yuan Wang, Ying Li, Dunshan Yu, Xiaoxin Cui
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3540-3551
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lun_Towards_Effective_and_Sparse_Adversarial_Attack_on_Spiking_Neural_Networks_CVPR_2025_paper.html

title:
Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradients

abstract:
Spiking neural networks (SNNs) have shown their competence in handling spatial-temporal event-based data with low energy consumption. Similar to conventional artificial neural networks (ANNs), SNNs are also vulnerable to gradient-based adversarial attacks, wherein gradients are calculated by spatial-temporal back-propagation (STBP) and surrogate gradients (SGs). However, the SGs may be invisible for an inference-only model as they do not influence the inference results, and current gradient-based attacks are ineffective for binary dynamic images captured by the dynamic vision sensor (DVS). While some approaches addressed the issue of invisible SGs through universal SGs, their SGs lack a correlation with the victim model, resulting in sub-optimal performance. Moreover, the imperceptibility of existing SNN-based binary attacks is still insufficient. In this paper, we introduce an innovative potential-dependent surrogate gradient (PDSG) method to establish a robust connection between the SG and the model, thereby enhancing the adaptability of adversarial attacks across various models with invisible SGs. Additionally, we propose the sparse dynamic attack (SDA) to effectively attack binary dynamic images. Utilizing a generation-reduction paradigm, SDA can fully optimize the sparsity of adversarial perturbations. Experimental results demonstrate that our PDSG and SDA outperform state-of-the-art SNN-based attacks across various models and datasets. Specifically, our PDSG achieves 100% attack success rate on ImageNet, and our SDA obtains 82% attack success rate by modifying only 0.24% of the pixels on CIFAR10DVS. The code is available at https://github.com/ryime/PDSG-SDA .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lun_Towards_Effective_and_Sparse_Adversarial_Attack_on_Spiking_Neural_Networks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lun_Towards_Effective_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lun_2025_CVPR,
    author    = {Lun, Li and Feng, Kunyu and Ni, Qinglong and Liang, Ling and Wang, Yuan and Li, Ying and Yu, Dunshan and Cui, Xiaoxin},
    title     = {Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradients},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3540-3551}
}

raw_affiliation:
[{"affiliation": "School of Integrated Circuits, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Software and Microelectronics, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Institute of Microelectronics, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["School of Integrated Circuits, Peking University", "School of Software and Microelectronics, Peking University", "Institute of Microelectronics, Chinese Academy of Sciences"]


title: Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation
authors: Gianni Franchi, Nacim Belkhir, Dat Nguyen Trong, Guoxuan Xia, Andrea Pilzer
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8062-8072
cities: ["Paris", "Unknown", "London", "Unknown"]
countries: ["France", "Unknown", "UK", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Franchi_Towards_Understanding_and_Quantifying_Uncertainty_for_Text-to-Image_Generation_CVPR_2025_paper.html

abstract:
Uncertainty quantification in text-to-image (T2I) generative models is crucial for understanding model behavior and improving output reliability. In this paper, we are the first to quantify and evaluate the uncertainty of T2I models with respect to the prompt. Alongside adapting existing approaches designed to measure uncertainty in the image space, we also introduce Prompt-based UNCertainty Estimation for T2I models (PUNC), a novel method leveraging Large Vision-Language Models (LVLMs) to better address uncertainties arising from the semantics of the prompt and generated images. PUNC utilizes a LVLM to caption a generated image, and then compares the caption with the original prompt in the more semantically meaningful text space. PUNC also enables the disentanglement of both aleatoric and epistemic uncertainties via precision and recall, which image-space approaches are unable to do.  Extensive experiments demonstrate that PUNC outperforms state-of-the-art uncertainty estimation techniques across various settings. Uncertainty quantification in text-to-image generation models can be used on various applications including bias detection, copyright protection, and OOD detection. We also introduce a comprehensive dataset of text prompts and generation pairs to foster further research in uncertainty quantification for generative models. Our findings illustrate that PUNC not only achieves competitive performance but also enables novel applications in evaluating and improving the trustworthiness of text-to-image models. The code is available at https://github.com/ENSTA-U2IS-AI/Uncertainty_diffusion

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Franchi_Towards_Understanding_and_Quantifying_Uncertainty_for_Text-to-Image_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Franchi_Towards_Understanding_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Franchi_2025_CVPR,
    author    = {Franchi, Gianni and Belkhir, Nacim and Trong, Dat Nguyen and Xia, Guoxuan and Pilzer, Andrea},
    title     = {Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8062-8072}
}

raw_affiliation:
[{"affiliation": "U2IS, ENSTA Paris, Institut Polytechnique de Paris, 1", "city": "Paris", "country": "France"}, {"affiliation": "Mirai,2", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Imperial College London,3", "city": "London", "country": "UK"}, {"affiliation": "NVIDIA4", "city": "Unknown", "country": "Unknown"}]

affiliations:
["U2IS, ENSTA Paris, Institut Polytechnique de Paris, 1", "Mirai,2", "Imperial College London,3", "NVIDIA4"]


title: PS-Diffusion: Photorealistic Subject-Driven Image Editing with Disentangled Control and Attention
authors: Weicheng Wang, Guoli Jia, Zhongqi Zhang, Liang Lin, Jufeng Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18302-18312
cities: ["Tianjin", "Shenzhen", "Beijing", "Guangzhou", "Shenzhen"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_PS-Diffusion_Photorealistic_Subject-Driven_Image_Editing_with_Disentangled_Control_and_Attention_CVPR_2025_paper.html

abstract:
Diffusion models pre-trained on large-scale paired image-text data achieve significant success in image editing. To convey more fine-grained visual details, subject-driven editing integrates subjects in user-provided reference images into existing scenes. However, it is challenging to obtain photorealistic results, which simulate contextual interactions, such as reflections, illumination, and shadows, induced by merging the target object into the source image. To address this issue, we propose PS-Diffusion, which ensures realistic and consistent object-scene blending while maintaining the invariance of subject appearance during editing. To be specific, we first divide the contextual interactions into those occurring in the foreground and the background areas. The effect of the former is estimated through intrinsic image decomposition, and the region of the latter is predicted in an additional background effect control branch. Moreover, we propose an effect attention module to disentangle the learning processes of interaction and subject, alleviating confusion between them. Additionally, we introduce a synthesized dataset, Replace-5K, consisting of 5,000 image pairs with invariant subject and contextual interactions via 3D rendering. Extensive quantitative and qualitative experiments on our dataset and two real-world datasets demonstrate that our method achieves state-of-the-art performance. The code is available in the https://github.com/wei-cheng777/PS-Diffusion.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_PS-Diffusion_Photorealistic_Subject-Driven_Image_Editing_with_Disentangled_Control_and_Attention_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Weicheng and Jia, Guoli and Zhang, Zhongqi and Lin, Liang and Yang, Jufeng},
    title     = {PS-Diffusion: Photorealistic Subject-Driven Image Editing with Disentangled Control and Attention},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18302-18312}
}

raw_affiliation:
[{"affiliation": "VCIP & TMCC & DISSec, College of Computer Science, Nankai University", "city": "Tianjin", "country": "China"}, {"affiliation": "Pengcheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "Electronic Engineering Department, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer Science and Engineering, Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Nankai International Advanced Research Institute (SHENZHEN\u00b7FUTIAN)", "city": "Shenzhen", "country": "China"}]

affiliations:
["VCIP & TMCC & DISSec, College of Computer Science, Nankai University", "Pengcheng Laboratory", "Electronic Engineering Department, Tsinghua University", "School of Computer Science and Engineering, Sun Yat-sen University", "Nankai International Advanced Research Institute (SHENZHEN\u00b7FUTIAN)"]


authors: Shuyang Hao, Bryan Hooi, Jun Liu, Kai-Wei Chang, Zi Huang, Yujun Cai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19890-19899
affiliations: ["Computer Vision Foundation", "IEEE"]
cities: ["Los Alamitos", "New York"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hao_Exploring_Visual_Vulnerabilities_via_Multi-Loss_Adversarial_Search_for_Jailbreaking_Vision-Language_CVPR_2025_paper.html

title:
Exploring Visual Vulnerabilities via Multi-Loss Adversarial Search for Jailbreaking Vision-Language Models

abstract:
Despite inheriting security measures from underlying language models, Vision-Language Models (VLMs) may still be vulnerable to safety alignment issues. Through empirical analysis, we uncover two critical findings: scenario-matched images can significantly amplify harmful outputs, and contrary to common assumptions in gradient-based attacks, minimal loss values do not guarantee optimal attack effectiveness. Building on these insights, we introduce MLAI (Multi-Loss Adversarial Images), a novel jailbreak framework that leverages scenario-aware image generation for semantic alignment, exploits flat minima theory for robust adversarial image selection, and employs multi-image collaborative attacks for enhanced effectiveness. Extensive experiments demonstrate MLAI's significant impact, achieving attack success rates of 77.75% on MiniGPT-4 and 82.80% on LLaVA-2, substantially outperforming existing methods by margins of 34.37% and 12.77% respectively. Furthermore, MLAI shows considerable transferability to commercial black-box VLMs, achieving up to 60.11% success rate. Our work reveals fundamental visual vulnerabilities in current VLMs safety mechanisms and underscores the need for stronger defenses. Warning: This paper contains potentially harmful example text.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hao_Exploring_Visual_Vulnerabilities_via_Multi-Loss_Adversarial_Search_for_Jailbreaking_Vision-Language_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hao_Exploring_Visual_Vulnerabilities_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hao_2025_CVPR,
    author    = {Hao, Shuyang and Hooi, Bryan and Liu, Jun and Chang, Kai-Wei and Huang, Zi and Cai, Yujun},
    title     = {Exploring Visual Vulnerabilities via Multi-Loss Adversarial Search for Jailbreaking Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19890-19899}
}

raw_affiliation:
[{"affiliation": "Computer Vision Foundation", "city": "Los Alamitos", "country": "USA"}, {"affiliation": "IEEE", "city": "New York", "country": "USA"}]


title: LLaVA-ST: A Multimodal Large Language Model for Fine-Grained Spatial-Temporal Understanding
authors: Hongyu Li, Jinyu Chen, Ziyu Wei, Shaofei Huang, Tianrui Hui, Jialin Gao, Xiaoming Wei, Si Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8592-8603
cities: ["Beijing", "Hefei", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_LLaVA-ST_A_Multimodal_Large_Language_Model_for_Fine-Grained_Spatial-Temporal_Understanding_CVPR_2025_paper.html

abstract:
Recent advancements in multimodal large language models (MLLMs) have shown promising results, yet existing approaches struggle to effectively handle both temporal and spatial localization simultaneously. This challenge stems from two key issues: first, incorporating spatial-temporal localization introduces a vast number of coordinate combinations, complicating the alignment of linguistic and visual coordinate representations; second, encoding fine-grained temporal and spatial information during video feature compression is inherently difficult. To address these issues, we propose LLaVA-ST, a MLLM for fine-grained spatial-temporal multimodal understanding. In LLaVA-ST, we propose Language-Aligned Positional Embedding, which embeds the textual coordinate special token into the visual space, simplifying the alignment of fine-grained spatial-temporal correspondences. Additionally, we design the Spatial-Temporal Packer, which decouples the feature compression of temporal and spatial resolutions into two distinct point-to-region attention processing streams. Furthermore, we propose ST-Align dataset with 4.3M training samples for fine-grained spatial-temporal multimodal understanding. With ST-align, we present a progressive training pipeline that aligns the visual and textual feature through sequential coarse-to-fine stages.Additionally, we introduce an ST-Align benchmark to evaluate spatial-temporal interleaved fine-grained understanding tasks, which include Spatial-Temporal Video Grounding (STVG) , Event Localization and Captioning (ELC) and Spatial Video Grounding (SVG). LLaVA-ST achieves outstanding performance on 11 benchmarks requiring fine-grained temporal, spatial, or spatial-temporal interleaving multimodal understanding.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_LLaVA-ST_A_Multimodal_Large_Language_Model_for_Fine-Grained_Spatial-Temporal_Understanding_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Hongyu and Chen, Jinyu and Wei, Ziyu and Huang, Shaofei and Hui, Tianrui and Gao, Jialin and Wei, Xiaoming and Liu, Si},
    title     = {LLaVA-ST: A Multimodal Large Language Model for Fine-Grained Spatial-Temporal Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8592-8603}
}

raw_affiliation:
[{"affiliation": "School of Artificial Intelligence, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer Science and Information Engineering, Hefei University of Technology", "city": "Hefei", "country": "China"}, {"affiliation": "Institute of Information Engineering, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Meituan", "city": "Beijing", "country": "China"}]

affiliations:
["School of Artificial Intelligence, Beihang University", "School of Computer Science and Information Engineering, Hefei University of Technology", "Institute of Information Engineering, Chinese Academy of Sciences", "Meituan"]


title: Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large Language Models
authors: Yuhao Dong, Zuyan Liu, Hai-Long Sun, Jingkang Yang, Winston Hu, Yongming Rao, Ziwei Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9062-9072
affiliations: ["S-Lab, Nanyang Technological University", "Tencent", "Tsinghua University", "Nanjing University"]
cities: ["Singapore", "Shenzhen", "Beijing", "Nanjing"]
countries: ["Singapore", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dong_Insight-V_Exploring_Long-Chain_Visual_Reasoning_with_Multimodal_Large_Language_Models_CVPR_2025_paper.html

abstract:
Large Language Models (LLMs) demonstrate enhanced capabilities and reliability by reasoning more, evolving from Chain-of-Thought prompting to product-level solutions like OpenAI o1.  Despite various efforts to improve LLM reasoning, high-quality long-chain reasoning data and optimized training pipelines still remain inadequately explored in vision-language tasks. In this paper, we present Insight-V, an early effort to 1) scalably produce long and robust reasoning data for complex multi-modal tasks, and 2) an effective training pipeline to enhance the reasoning capabilities of multi-modal large language models (MLLMs). Specifically, to create long and structured reasoning data without human labor, we design a two-step pipeline with a progressive strategy to generate sufficiently long and diverse reasoning paths and a multi-granularity assessment method to ensure data quality. We observe that directly supervising MLLMs with such long and complex reasoning data will not yield ideal reasoning ability. To tackle this problem, we design a multi-agent system consisting of a reasoning agent dedicated to performing long-chain reasoning and a summary agent trained to judge and summarize reasoning results. We further incorporate an iterative DPO algorithm to enhance the reasoning agent's generation stability and quality. Based on the popular LLaVA-NeXT model, our method shows an average improvement of 7.5% across seven challenging multi-modal benchmarks requiring visual reasoning.  We also achieve a 4.2% improvement on a stronger base MLLM, highlighting the potential to further advance state-of-the-art models. Benefiting from our multi-agent system, Insight-V can also easily maintain or improve performance on perception-focused multi-modal tasks. We will make our data and code publicly available to promote future research in this emerging field.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dong_Insight-V_Exploring_Long-Chain_Visual_Reasoning_with_Multimodal_Large_Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dong_Insight-V_Exploring_Long-Chain_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dong_2025_CVPR,
    author    = {Dong, Yuhao and Liu, Zuyan and Sun, Hai-Long and Yang, Jingkang and Hu, Winston and Rao, Yongming and Liu, Ziwei},
    title     = {Insight-V: Exploring Long-Chain Visual Reasoning with Multimodal Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9062-9072}
}

raw_affiliation:
[{"affiliation": "S-Lab, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Tencent", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}]


title: MaIR: A Locality- and Continuity-Preserving Mamba for Image Restoration
authors: Boyun Li, Haiyu Zhao, Wenxin Wang, Peng Hu, Yuanbiao Gou, Xi Peng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7491-7501
cities: ["Chengdu", "Chengdu"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_MaIR_A_Locality-_and_Continuity-Preserving_Mamba_for_Image_Restoration_CVPR_2025_paper.html

abstract:
Recent advancements in Mamba have shown promising results in image restoration. These methods typically flatten 2D images into multiple distinct 1D sequences along rows and columns, process each sequence independently using selective scan operation, and recombine them to form the outputs. However, such a paradigm overlooks two vital aspects: i) the local relationships and spatial continuity inherent in natural images, and ii) the discrepancies among sequences unfolded through totally different ways. To overcome the drawbacks, we explore two problems in Mamba-based restoration methods: i) how to design a scanning strategy preserving both locality and continuity while facilitating restoration, and ii) how to aggregate the distinct sequences unfolded in totally different ways. To address these problems, we propose a novel Mamba-based Image Restoration model (MaIR), which consists of Nested S-shaped Scanning strategy (NSS) and Sequence Shuffle Attention block (SSA). Specifically, NSS preserves locality and continuity of the input images through the stripe-based scanning region and the S-shaped scanning path, respectively. SSA aggregates sequences through calculating attention weights within the corresponding channels of different sequences. Thanks to NSS and SSA, MaIR surpasses 40 baselines across 14 challenging datasets, achieving state-of-the-art performance on the tasks of image super-resolution, denoising, deblurring and dehazing.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_MaIR_A_Locality-_and_Continuity-Preserving_Mamba_for_Image_Restoration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_MaIR_A_Locality-_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Boyun and Zhao, Haiyu and Wang, Wenxin and Hu, Peng and Gou, Yuanbiao and Peng, Xi},
    title     = {MaIR: A Locality- and Continuity-Preserving Mamba for Image Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7491-7501}
}

raw_affiliation:
[{"affiliation": "College of Computer Science, Sichuan University, China.", "city": "Chengdu", "country": "China"}, {"affiliation": "National Key Laboratory of Fundamental Algorithms and Models for Engineering Numerical Simulation, Sichuan University, China.", "city": "Chengdu", "country": "China"}]

affiliations:
["College of Computer Science, Sichuan University, China.", "National Key Laboratory of Fundamental Algorithms and Models for Engineering Numerical Simulation, Sichuan University, China."]


title: Few-shot Implicit Function Generation via Equivariance
authors: Suizhi Huang, Xingyi Yang, Hongtao Lu, Xinchao Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16262-16272
affiliations: ["Shanghai Jiao Tong University", "National University of Singapore"]
cities: ["Shanghai", "Singapore"]
countries: ["China", "Singapore"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Few-shot_Implicit_Function_Generation_via_Equivariance_CVPR_2025_paper.html

abstract:
Implicit Neural Representations (INRs) have emerged as a powerful framework for representing continuous signals. However, generating diverse INR weights remains challenging due to limited training data. We introduce Few-shot Implicit Function Generation, a new problem setup that aims to generate diverse yet functionally consistent INR weights from only a few examples. This is challenging because even for the same signal, the optimal INRs can vary significantly depending on their initializations. To tackle this, we propose EquiGen, a framework that can generate new INRs from limited data. The core idea is that functionally similar networks can be transformed into one another through weight permutations, forming an equivariance group. By projecting these weights into an equivariant latent space, we enable diverse generation within these groups, even with few examples. EquiGen implements this through an equivariant encoder trained via contrastive learning and smooth augmentation, an equivariance-guided diffusion process, and controlled perturbations in the equivariant subspace. Experiments on 2D image and 3D shape INR datasets demonstrate that our approach effectively generates diverse INR weights while preserving their functional properties in few-shot scenarios. Code is available at https://github.com/JeanDiable/EquiGen.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Few-shot_Implicit_Function_Generation_via_Equivariance_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_Few-shot_Implicit_Function_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Suizhi and Yang, Xingyi and Lu, Hongtao and Wang, Xinchao},
    title     = {Few-shot Implicit Function Generation via Equivariance},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16262-16272}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}]


title: RSAR: Restricted State Angle Resolver and Rotated SAR Benchmark
authors: Xin Zhang, Xue Yang, Yuxuan Li, Jian Yang, Ming-Ming Cheng, Xiang Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7416-7426
affiliations: ["VCIP, CS, Nankai University", "Shanghai AI Laboratory", "NKIARI, Shenzhen Futian"]
cities: ["Tianjin", "Shanghai", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_RSAR_Restricted_State_Angle_Resolver_and_Rotated_SAR_Benchmark_CVPR_2025_paper.html

abstract:
Rotated object detection has made significant progress in the optical remote sensing. However, advancements in the Synthetic Aperture Radar (SAR) field are laggard behind, primarily due to the absence of a large-scale dataset. Annotating such a dataset is inefficient and costly. A promising solution is to employ a weakly supervised model (e.g., trained with available horizontal boxes only) to generate pseudo-rotated boxes for reference before manual calibration. Unfortunately, the existing weakly supervised models exhibit limited accuracy in predicting the object's angle. Previous works attempt to enhance angle prediction by using angle resolvers that decouple angles into cosine and sine encodings. In this work, we first reevaluate these resolvers from a unified perspective of dimension mapping and expose that they share the same shortcomings: these methods overlook the unit cycle constraint inherent in these encodings, easily leading to prediction biases. To address this issue, we propose the Unit Cycle Resolver (UCR), which incorporates a unit circle constraint loss to improve angle prediction accuracy. Our approach can effectively improve the performance of existing state-of-the-art weakly supervised methods and even surpasses fully supervised models on existing optical benchmarks (i.e., DOTA-v1.0). With the aid of UCR, we further annotate and introduce RSAR, the largest multi-class rotated SAR object detection dataset to date. Extensive experiments on both RSAR and optical datasets demonstrate that our UCR enhances angle prediction accuracy.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_RSAR_Restricted_State_Angle_Resolver_and_Rotated_SAR_Benchmark_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_RSAR_Restricted_State_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Xin and Yang, Xue and Li, Yuxuan and Yang, Jian and Cheng, Ming-Ming and Li, Xiang},
    title     = {RSAR: Restricted State Angle Resolver and Rotated SAR Benchmark},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7416-7426}
}

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


title: Dual Energy-Based Model with Open-World Uncertainty Estimation for Out-of-distribution Detection
authors: Qi Chen, Hu Ding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25728-25737
affiliations: ["University of Science and Technology of China"]
cities: ["Hefei"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Dual_Energy-Based_Model_with_Open-World_Uncertainty_Estimation_for_Out-of-distribution_Detection_CVPR_2025_paper.html

abstract:
Out-of-distribution (OOD) detection is crucial for machine learning models deployed in open-world environments. However, existing methods often struggle with model over-confidence or rely heavily on empirical energy value estimation, limiting their scalability and generalizability. This paper introduces DEBO (Dual Energy-Based Model for Out-of-distribution Detection), a novel approach that addresses these limitations through an innovative dual classifier architecture and a unified energy-based objective function. DEBO enhances the standard classification framework by integrating a dual-purpose output space within a single classifier. The primary component classifies in-distribution (ID) data conventionally, while the secondary component captures open-world information and estimates uncertainty. Our method overcomes the dependence of traditional energy model-based OOD detection methods on empirical energy estimation while maintaining theoretical guarantees. Theoretical analysis demonstrates that DEBO promotes low energy and high confidence for ID data, while simultaneously inducing higher energy and decreased confidence for OOD samples. Extensive experiments conducted on benchmark datasets reveal that DEBO achieves state-of-the-art OOD detection performance while maintaining comparable classification accuracy on ID data.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Dual_Energy-Based_Model_with_Open-World_Uncertainty_Estimation_for_Out-of-distribution_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Dual_Energy-Based_Model_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Qi and Ding, Hu},
    title     = {Dual Energy-Based Model with Open-World Uncertainty Estimation for Out-of-distribution Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25728-25737}
}


title: DTGBrepGen: A Novel B-rep Generative Model through Decoupling Topology and Geometry
authors: Jing Li, Yihang Fu, Falai Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21438-21447
affiliations: ["University of Science and Technology of China"]
cities: ["Hefei"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_DTGBrepGen_A_Novel_B-rep_Generative_Model_through_Decoupling_Topology_and_CVPR_2025_paper.html

abstract:
Boundary representation (B-rep) of geometric models is a fundamental format in Computer-Aided Design (CAD). However,  automatically generating valid and high-quality B-rep models remains challenging due to the complex interdependence between the topology and geometry of the models. Existing methods tend to prioritize geometric representation while giving insufficient attention to topological constraints, making it difficult to maintain structural validity and geometric accuracy. In this paper, we propose DTGBrepGen, a novel topology-geometry decoupled framework for B-rep generation that explicitly addresses both aspects. Our approach first generates valid topological structures through a two-stage process that independently models edge-face and edge-vertex adjacency relationships. Subsequently, we employ Transformer-based diffusion models for sequential geometry generation, progressively generating vertex coordinates, followed by edge geometries and face geometries which are represented as B-splines. Extensive experiments on diverse CAD datasets show that DTGBrepGen significantly outperforms existing methods in both topological validity and geometric accuracy, achieving higher validity rates and producing more diverse and realistic B-reps. Our code is publicly available at https://github.com/jinli99/DTGBrepGen.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_DTGBrepGen_A_Novel_B-rep_Generative_Model_through_Decoupling_Topology_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_DTGBrepGen_A_Novel_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Jing and Fu, Yihang and Chen, Falai},
    title     = {DTGBrepGen: A Novel B-rep Generative Model through Decoupling Topology and Geometry},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21438-21447}
}


title: Continuous Space-Time Video Resampling with Invertible Motion Steganography
authors: Yuantong Zhang, Zhenzhong Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2116-2126
raw_affiliation: [{"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}]
affiliations: ["Wuhan University"]
cities: ["Wuhan"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Continuous_Space-Time_Video_Resampling_with__Invertible_Motion_Steganography_CVPR_2025_paper.html

abstract:
Space-time video resampling aims to conduct both spatial-temporal downsampling and upsampling processes to achieve high-quality video reconstruction.Although there has been much progress, some major challenges still exist, such as how to preserve motion information during temporal resampling while avoiding blurring artifacts, and how to achieve flexible temporal and spatial resampling factors. In this paper, we introduce an Invertible Motion Steganography Module (IMSM), designed to preserve motion information in high-frame-rate videos. This module embeds motion information from high-frame-rate videos into downsampled frames with lower frame rates in a visually imperceptible manner. Its reversible nature allows the motion information to be recovered, facilitating the reconstruction of high-frame-rate videos. Furthermore, we propose a 3D implicit feature modulation technique that enables continuous spatiotemporal resampling. With tailored training strategies, our method supports flexible frame rate conversions, including non-integer changes like 30 FPS to 24 FPS and vice versa.  Extensive experiments show that our method significantly outperforms existing solutions across multiple datasets in various video resampling tasks with high flexibility. Codes will be made available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Continuous_Space-Time_Video_Resampling_with__Invertible_Motion_Steganography_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Continuous_Space-Time_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Yuantong and Chen, Zhenzhong},
    title     = {Continuous Space-Time Video Resampling with  Invertible Motion Steganography},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2116-2126}
}


title: Schedule On the Fly: Diffusion Time Prediction for Faster and Better Image Generation
authors: Zilyu Ye, Zhiyang Chen, Tiancheng Li, Zemin Huang, Weijian Luo, Guo-Jun Qi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23412-23422
cities: ["Hangzhou", "Guangzhou", "Beijing", "Hangzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ye_Schedule_On_the_Fly_Diffusion_Time_Prediction_for_Faster_and_CVPR_2025_paper.html

abstract:
Diffusion and flow matching models have achieved remarkable success in text-to-image generation. However, these models typically rely on the predetermined denoising schedules for all prompts. The multi-step reverse diffusion process can be regarded as a kind of chain-of-thought for generating high-quality images step by step. Therefore, diffusion models should reason for each instance to determine the optimal noise schedule adaptively, achieving high generation quality with sampling efficiency. In this paper, we introduce the Time Prediction Diffusion Model (TPDM) for this. TPDM employs a plug-and-play Time Prediction Module (TPM) that predicts the next noise level based on current latent features at each denoising step.  We train the TPM using reinforcement learning to maximize a reward that encourages high final image quality while penalizing excessive denoising steps. With such an adaptive scheduler, TPDM not only generates high-quality images that are aligned closely with human preferences but also adjusts diffusion time and the number of denoising steps on the fly, enhancing both performance and efficiency. With Stable Diffusion 3 Medium architecture, TPDM achieves an aesthetic score of 5.44 and a human preference score (HPS) of 29.59, while using around 50% fewer denoising steps to achieve better performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ye_Schedule_On_the_Fly_Diffusion_Time_Prediction_for_Faster_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ye_Schedule_On_the_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ye_2025_CVPR,
    author    = {Ye, Zilyu and Chen, Zhiyang and Li, Tiancheng and Huang, Zemin and Luo, Weijian and Qi, Guo-Jun},
    title     = {Schedule On the Fly: Diffusion Time Prediction for Faster and Better Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23412-23422}
}

raw_affiliation:
[{"affiliation": "MAPLE Lab, Westlake University", "city": "Hangzhou", "country": "China"}, {"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Institute of Advanced Technology, Westlake Institute for Advanced Study", "city": "Hangzhou", "country": "China"}]

affiliations:
["MAPLE Lab, Westlake University", "South China University of Technology", "Peking University", "Institute of Advanced Technology, Westlake Institute for Advanced Study"]


title: Learning Audio-guided Video Representation with Gated Attention for Video-Text Retrieval
authors: Boseung Jeong, Jicheol Park, Sungyeon Kim, Suha Kwak
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26202-26211
affiliations: ["Dept. of CSE, POSTECH", "Graduate School of AI, POSTECH"]
cities: ["Pohang", "Pohang"]
countries: ["South Korea", "South Korea"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jeong_Learning_Audio-guided_Video_Representation_with_Gated_Attention_for_Video-Text_Retrieval_CVPR_2025_paper.html

abstract:
Video-text retrieval, the task of retrieving videos based on a textual query or vice versa, is of paramount importance for video understanding and multimodal information retrieval. Recent methods in this area rely primarily on visual and textual features and often ignore audio, although it helps enhance overall comprehension of video content.Moreover, traditional models that incorporate audio blindly utilize the audio input regardless of whether it is useful or not, resulting in suboptimal video representation. To address these limitations, we propose a novel video-text retrieval framework, Audio-guided VIdeo representation learning with GATEd attention (AVIGATE), that effectively leverages audio cues through a gated attention mechanism that selectively filters out uninformative audio signals.In addition, we propose an adaptive margin-based contrastive loss to deal with the inherently unclear positive-negative relationship between video and text, which facilitates learning better video-text alignment.Our extensive experiments demonstrate that AVIGATE achieves state-of-the-art performance on all the public benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jeong_Learning_Audio-guided_Video_Representation_with_Gated_Attention_for_Video-Text_Retrieval_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jeong_Learning_Audio-guided_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jeong_2025_CVPR,
    author    = {Jeong, Boseung and Park, Jicheol and Kim, Sungyeon and Kwak, Suha},
    title     = {Learning Audio-guided Video Representation with Gated Attention for Video-Text Retrieval},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26202-26211}
}

raw_affiliation:
[{"affiliation": "Dept. of CSE, POSTECH", "city": "Pohang", "country": "South Korea"}, {"affiliation": "Graduate School of AI, POSTECH", "city": "Pohang", "country": "South Korea"}]


title: ProtoDepth: Unsupervised Continual Depth Completion with Prototypes
authors: Patrick Rim, Hyoungseob Park, S. Gangopadhyay, Ziyao Zeng, Younjoon Chung, Alex Wong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6304-6316
raw_affiliation: [{"affiliation": "Yale Vision Lab", "city": "New Haven", "country": "USA"}]
affiliations: ["Yale Vision Lab"]
cities: ["New Haven"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Rim_ProtoDepth_Unsupervised_Continual_Depth_Completion_with_Prototypes_CVPR_2025_paper.html

abstract:
We present ProtoDepth, a novel prototype-based approach for continual learning of unsupervised depth completion, the multimodal 3D reconstruction task of predicting dense depth maps from RGB images and sparse point clouds. The unsupervised learning paradigm is well-suited for continual learning, as ground truth is not needed. However, when training on new non-stationary distributions, depth completion models will catastrophically forget previously learned information. We address forgetting by learning prototype sets that adapt the latent features of a frozen pretrained model to new domains. Since the original weights are not modified, ProtoDepth does not forget when test-time domain identity is known. To extend ProtoDepth to the challenging setting where the test-time domain identity is withheld, we propose to learn domain descriptors that enable the model to select the appropriate prototype set for inference. We evaluate ProtoDepth on benchmark dataset sequences, where we reduce forgetting compared to baselines by 52.2% for indoor and 53.2% for outdoor to achieve the state of the art.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Rim_ProtoDepth_Unsupervised_Continual_Depth_Completion_with_Prototypes_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Rim_ProtoDepth_Unsupervised_Continual_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Rim_2025_CVPR,
    author    = {Rim, Patrick and Park, Hyoungseob and Gangopadhyay, S. and Zeng, Ziyao and Chung, Younjoon and Wong, Alex},
    title     = {ProtoDepth: Unsupervised Continual Depth Completion with Prototypes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6304-6316}
}


title: vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation
authors: Bastian Wittmann, Yannick Wattenberg, Tamaz Amiranashvili, Suprosanna Shit, Bjoern Menze
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20874-20884
affiliations: ["University of Zurich", "ETH Zurich", "Technical University of Munich"]
cities: ["Zurich", "Zurich", "Munich"]
countries: ["Switzerland", "Switzerland", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wittmann_vesselFM_A_Foundation_Model_for_Universal_3D_Blood_Vessel_Segmentation_CVPR_2025_paper.html

abstract:
Segmenting 3D blood vessels is a critical yet challenging task in medical image analysis. This is due to significant imaging modality-specific variations in artifacts, vascular patterns and scales, signal-to-noise ratios, and background tissues. These variations, along with domain gaps arising from varying imaging protocols, limit the generalization of existing supervised learning-based methods, requiring tedious voxel-level annotations for each dataset separately. While foundation models promise to alleviate this limitation, they typically fail to generalize to the task of blood vessel segmentation, posing a unique, complex problem. In this work, we present vesselFM, a foundation model designed specifically for the broad task of 3D blood vessel segmentation. Unlike previous models, vesselFM can effortlessly generalize to unseen domains. To achieve zero-shot generalization, we train vesselFM on three heterogeneous data sources: a large, curated annotated dataset, data generated by a domain randomization scheme, and data sampled from a flow matching-based generative model. Extensive evaluations show that vesselFM outperforms state-of-the-art medical image segmentation foundation models across four (pre-)clinically relevant imaging modalities in zero-, one-, and few-shot scenarios, therefore providing a universal solution for 3D blood vessel segmentation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wittmann_vesselFM_A_Foundation_Model_for_Universal_3D_Blood_Vessel_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wittmann_vesselFM_A_Foundation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wittmann_2025_CVPR,
    author    = {Wittmann, Bastian and Wattenberg, Yannick and Amiranashvili, Tamaz and Shit, Suprosanna and Menze, Bjoern},
    title     = {vesselFM: A Foundation Model for Universal 3D Blood Vessel Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20874-20884}
}

raw_affiliation:
[{"affiliation": "University of Zurich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}]


authors: Hongxiang Zhao, Xingchen Liu, Mutian Xu, Yiming Hao, Weikai Chen, Xiaoguang Han
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27683-27693
affiliations: ["SSE, CUHKSZ", "FNii, CUHKSZ"]
cities: ["Shenzhen", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_TASTE-Rob_Advancing_Video_Generation_of_Task-Oriented_Hand-Object_Interaction_for_Generalizable_CVPR_2025_paper.html

title:
TASTE-Rob: Advancing Video Generation of Task-Oriented Hand-Object Interaction for Generalizable Robotic Manipulation

abstract:
We address key limitations in existing datasets and models for task-oriented hand-object interaction video generation, a critical approach of generating video demonstrations for robotic imitation learning. Current datasets, such as Ego4D, often suffer from inconsistent view perspectives and misaligned interactions, leading to reduced video quality and limiting their applicability for precise imitation learning tasks. Towards this end, we introduce TASTE-Rob -- a pioneering large-scale dataset of 100,856 ego-centric hand-object interaction videos. Each video is meticulously aligned with language instructions and recorded from a consistent camera viewpoint to ensure interaction clarity. By fine-tuning a Video Diffusion Model (VDM) on TASTE-Rob, we achieve realistic object interactions, though we observed occasional inconsistencies in hand grasping postures. To enhance realism, we introduce a three-stage pose-refinement pipeline that improves hand posture accuracy in generated videos. Our curated dataset, coupled with the specialized pose-refinement framework, provides notable  performance gains in generating high-quality, task-oriented hand-object interaction videos, resulting in achieving superior generalizable robotic manipulation. To foster further advancements in the field, TASTE-Rob dataset and source code will be made publicly available on our website https://taste-rob.github.io.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_TASTE-Rob_Advancing_Video_Generation_of_Task-Oriented_Hand-Object_Interaction_for_Generalizable_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_TASTE-Rob_Advancing_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Hongxiang and Liu, Xingchen and Xu, Mutian and Hao, Yiming and Chen, Weikai and Han, Xiaoguang},
    title     = {TASTE-Rob: Advancing Video Generation of Task-Oriented Hand-Object Interaction for Generalizable Robotic Manipulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27683-27693}
}

raw_affiliation:
[{"affiliation": "SSE, CUHKSZ", "city": "Shenzhen", "country": "China"}, {"affiliation": "FNii, CUHKSZ", "city": "Shenzhen", "country": "China"}]


title: NoT: Federated Unlearning via Weight Negation
authors: Yasser H. Khalil, Leo Brunswic, Soufiane Lamghari, Xu Li, Mahdi Beitollahi, Xi Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25759-25769
affiliations: ["Huawei Noah\u2019s Ark Lab", "Huawei Technologies Canada Inc."]
cities: ["Montreal", "Ottawa"]
countries: ["Canada", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Khalil_NoT_Federated_Unlearning_via_Weight_Negation_CVPR_2025_paper.html

abstract:
Federated unlearning (FU) aims to remove a participant's data contributions from a trained federated learning (FL) model, ensuring privacy and regulatory compliance. Traditional FU methods often depend on auxiliary storage on either the client or server side or require direct access to the data targeted for removal--a dependency that may not be feasible if the data is no longer available. To overcome these limitations, we propose NoT, a novel and efficient FU algorithm based on weight negation (multiplying by -1), which circumvents the need for additional storage and access to the target data. We argue that effective and efficient unlearning can be achieved by perturbing model parameters away from the set of optimal parameters, yet being well-positioned for quick re-optimization. This technique, though seemingly contradictory, is theoretically grounded: we prove that the weight negation perturbation effectively disrupts inter-layer co-adaptation, inducing unlearning while preserving an approximate optimality property, thereby enabling rapid recovery. Experimental results across three datasets and three model architectures demonstrate that NoT significantly outperforms existing baselines in unlearning efficacy as well as in communication and computational efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Khalil_NoT_Federated_Unlearning_via_Weight_Negation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Khalil_NoT_Federated_Unlearning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Khalil_2025_CVPR,
    author    = {Khalil, Yasser H. and Brunswic, Leo and Lamghari, Soufiane and Li, Xu and Beitollahi, Mahdi and Chen, Xi},
    title     = {NoT: Federated Unlearning via Weight Negation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25759-25769}
}

raw_affiliation:
[{"affiliation": "Huawei Noah\u2019s Ark Lab", "city": "Montreal", "country": "Canada"}, {"affiliation": "Huawei Technologies Canada Inc.", "city": "Ottawa", "country": "Canada"}]


authors: Jeonghwan Kim, Jisoo Kim, Jeonghyeon Na, Hanbyul Joo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1816-1828
raw_affiliation: [{"affiliation": "Seoul National University", "city": "Seoul", "country": "South Korea"}]
affiliations: ["Seoul National University"]
cities: ["Seoul"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_ParaHome_Parameterizing_Everyday_Home_Activities_Towards_3D_Generative_Modeling_of_CVPR_2025_paper.html

title:
ParaHome: Parameterizing Everyday Home Activities Towards 3D Generative Modeling of Human-Object Interactions

abstract:
To enable machines to understand the way humans interact with the physical world in daily life, 3D interaction signals should be captured in natural settings, allowing people to engage with multiple objects in a range of sequential and casual manipulations. To achieve this goal, we introduce our ParaHome system designed to capture dynamic 3D movements of humans and objects within a common home environment.  Our system features a multi-view setup with 70 synchronized RGB cameras, along with wearable motion capture devices including an IMU-based body suit and hand motion capture gloves. By leveraging the ParaHome system, we collect a new human-object interaction dataset, including 486 minutes of sequences across 207 captures with 38 participants, offering advancements with three key aspects: (1) capturing body motion and dexterous hand manipulation motion alongside multiple objects within a contextual home environment; (2) encompassing sequential and concurrent manipulations paired with text descriptions; and (3) including articulated objects with multiple parts represented by 3D parameterized models. We present detailed design justifications for our system, and perform key generative modeling experiments to demonstrate the potential of our dataset.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_ParaHome_Parameterizing_Everyday_Home_Activities_Towards_3D_Generative_Modeling_of_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_ParaHome_Parameterizing_Everyday_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Jeonghwan and Kim, Jisoo and Na, Jeonghyeon and Joo, Hanbyul},
    title     = {ParaHome: Parameterizing Everyday Home Activities Towards 3D Generative Modeling of Human-Object Interactions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1816-1828}
}


title: Adapting to the Unknown: Training-Free Audio-Visual Event Perception with Dynamic Thresholds
authors: Eitan Shaar, Ariel Shaulov, Gal Chechik, Lior Wolf
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3142-3151
affiliations: ["Bar-Ilan University", "Tel-Aviv University", "NVIDIA"]
cities: ["Ramat Gan", "Tel Aviv", "Santa Clara"]
countries: ["Israel", "Israel", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shaar_Adapting_to_the_Unknown_Training-Free_Audio-Visual_Event_Perception_with_Dynamic_CVPR_2025_paper.html

abstract:
In the domain of audio-visual event perception, which focuses on the temporal localization and classification of events across distinct modalities (audio and visual), existing approaches are constrained by the vocabulary available in their training data. This limitation significantly impedes their capacity to generalize to novel, unseen event categories. Furthermore, the annotation process for this task is labor-intensive, requiring extensive manual labeling across modalities and temporal segments, limiting the scalability of current methods. Current state-of-the-art models ignore the shifts in event distributions over time, reducing their ability to adjust to changing video dynamics. Additionally, previous methods rely on late fusion to combine audio and visual information. While straightforward, this approach results in a significant loss of multimodal interactions.  To address these challenges, we propose Audio-Visual Adaptive Video Analysis (AV2A), a model-agnostic approach that requires no further training and integrates a score-level fusion technique to retain richer multimodal interactions. AV2A also includes a within-video label shift algorithm, leveraging input video data and predictions from prior frames to dynamically adjust event distributions for subsequent frames. Moreover, we present the first training-free, open-vocabulary baseline for audio-visual event perception, demonstrating that AV2A achieves substantial improvements over naive training-free baselines. We demonstrate the effectiveness of AV2A on both zero-shot and weakly-supervised state-of-the-art methods, achieving notable improvements in performance metrics over existing approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shaar_Adapting_to_the_Unknown_Training-Free_Audio-Visual_Event_Perception_with_Dynamic_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shaar_Adapting_to_the_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shaar_2025_CVPR,
    author    = {Shaar, Eitan and Shaulov, Ariel and Chechik, Gal and Wolf, Lior},
    title     = {Adapting to the Unknown: Training-Free Audio-Visual Event Perception with Dynamic Thresholds},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3142-3151}
}

raw_affiliation:
[{"affiliation": "Bar-Ilan University", "city": "Ramat Gan", "country": "Israel"}, {"affiliation": "Tel-Aviv University", "city": "Tel Aviv", "country": "Israel"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}]


title: OpenHumanVid: A Large-Scale High-Quality Dataset for Enhancing Human-Centric Video Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7752-7762
cities: ["Shanghai", "Beijing", "Shanghai", "Shanghai"]
countries: ["China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_OpenHumanVid_A_Large-Scale_High-Quality_Dataset_for_Enhancing_Human-Centric_Video_Generation_CVPR_2025_paper.html

authors:
Hui Li, Mingwang Xu, Yun Zhan, Shan Mu, Jiaye Li, Kaihui Cheng, Yuxuan Chen, Tan Chen, Mao Ye, Jingdong Wang, Siyu Zhu

abstract:
Recent advancements in visual generation technologies have markedly increased the scale and availability of video datasets, which are crucial for training effective video generation models. However, a significant lack of high-quality, human-centric video datasets presents a challenge to progress in this field. To bridge this gap, we introduce OpenHumanVid, a large-scale and high-quality human-centric video dataset characterized by precise and detailed captions that encompass both human appearance and motion states, along with supplementary human motion conditions, including skeleton sequences and speech audio. To validate the efficacy of this dataset and the associated training strategies, we propose an extension of existing classical diffusion transformer architectures and conduct further pretraining of our models on the proposed dataset. Our findings yield two critical insights: First, the incorporation of a large-scale, high-quality dataset substantially enhances evaluation metrics for generated human videos while preserving performance in general video generation tasks. Second, the effective alignment of text with human appearance, human motion, and facial motion is essential for producing high-quality video outputs. Based on these insights and corresponding methodologies, the straightforward extended network trained on the proposed dataset demonstrates an obvious improvement in the generation of human-centric videos.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_OpenHumanVid_A_Large-Scale_High-Quality_Dataset_for_Enhancing_Human-Centric_Video_Generation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Hui and Xu, Mingwang and Zhan, Yun and Mu, Shan and Li, Jiaye and Cheng, Kaihui and Chen, Yuxuan and Chen, Tan and Ye, Mao and Wang, Jingdong and Zhu, Siyu},
    title     = {OpenHumanVid: A Large-Scale High-Quality Dataset for Enhancing Human-Centric Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7752-7762}
}

raw_affiliation:
[{"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Baidu Inc", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai Jiaotong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Academy of AI for Science", "city": "Shanghai", "country": "China"}]

affiliations:
["Fudan University", "Baidu Inc", "Shanghai Jiaotong University", "Shanghai Academy of AI for Science"]


title: Classifier-Free Guidance Inside the Attraction Basin May Cause Memorization
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12871-12879
affiliations: ["New York University", "Sony AI", "Sony Group Corporation"]
cities: ["New York", "Tokyo", "Tokyo"]
countries: ["USA", "Japan", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jain_Classifier-Free_Guidance_Inside_the_Attraction_Basin_May_Cause_Memorization_CVPR_2025_paper.html

authors:
Anubhav Jain, Yuya Kobayashi, Takashi Shibuya, Yuhta Takida, Nasir Memon, Julian Togelius, Yuki Mitsufuji

abstract:
Diffusion models are prone to exactly reproduce images from the training data. This exact reproduction of the training data is concerning as it can lead to copyright infringement and/or leakage of privacy-sensitive information. In this paper, we present a novel perspective on the memorization phenomenon and propose a simple yet effective approach to mitigate it. We argue that memorization occurs because of an attraction basin in the denoising process which steers the diffusion trajectory towards a memorized image. However, this can be mitigated by guiding the diffusion trajectory away from the attraction basin by not applying classifier-free guidance until an ideal transition point occurs from which classifier-free guidance is applied. This leads to the generation of non-memorized images that are high in image quality and well-aligned with the conditioning mechanism. To further improve on this, we present a new guidance technique, opposite guidance, that escapes the attraction basin sooner in the denoising process. We demonstrate the existence of attraction basins in various scenarios in which memorization occurs, and we show that our proposed approach successfully mitigates memorization.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jain_Classifier-Free_Guidance_Inside_the_Attraction_Basin_May_Cause_Memorization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jain_Classifier-Free_Guidance_Inside_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jain_2025_CVPR,
    author    = {Jain, Anubhav and Kobayashi, Yuya and Shibuya, Takashi and Takida, Yuhta and Memon, Nasir and Togelius, Julian and Mitsufuji, Yuki},
    title     = {Classifier-Free Guidance Inside the Attraction Basin May Cause Memorization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12871-12879}
}

raw_affiliation:
[{"affiliation": "New York University", "city": "New York", "country": "USA"}, {"affiliation": "Sony AI", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Sony Group Corporation", "city": "Tokyo", "country": "Japan"}]


title: Track Any Anomalous Object:A Granular Video Anomaly Detection Pipeline
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8689-8699
cities: ["Xiamen", "Xiamen", "Hong Kong", "Charlottesville"]
countries: ["China", "China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Track_Any_Anomalous_ObjectA_Granular_Video_Anomaly_Detection_Pipeline_CVPR_2025_paper.html

authors:
Yuzhi Huang, Chenxin Li, Haitao Zhang, Zixu Lin, Yunlong Lin, Hengyu Liu, Wuyang Li, Xinyu Liu, Jiechao Gao, Yue Huang, Xinghao Ding, Yixuan Yuan

abstract:
Video anomaly detection (VAD) is crucial in scenarios such as surveillance and autonomous driving, where timely detection of unexpected activities is essential. Albeit existing methods have primarily focused on detecting anomalous objects in videos--either by identifying anomalous frames or objects--they often neglect finer-grained analysis, such as anomalous pixels, which limits their ability to capture a broader range of anomalies. To address this challenge, we propose an innovative VAD framework called \underline T rack Any \underline A nomalous \underline O bject (\textit TAO ), which introduces a Granular Video Anomaly Detection Framework that, for the first time, integrates the detection of multiple fine-grained anomalous objects into a unified framework. Unlike methods that assign anomaly scores to every pixel at each moment, our approach transforms the problem into pixel-level tracking of anomalous objects. By linking anomaly scores to subsequent tasks such as image segmentation and video tracking, our method eliminates the need for threshold selection and achieves more precise anomaly localization, even in long and challenging video sequences. Experiments on extensive datasets demonstrate that \textit TAO  achieves state-of-the-art performance, setting a new progress for VAD by providing a practical, granular, and holistic solution.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Track_Any_Anomalous_ObjectA_Granular_Video_Anomaly_Detection_Pipeline_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_Track_Any_Anomalous_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Yuzhi and Li, Chenxin and Zhang, Haitao and Lin, Zixu and Lin, Yunlong and Liu, Hengyu and Li, Wuyang and Liu, Xinyu and Gao, Jiechao and Huang, Yue and Ding, Xinghao and Yuan, Yixuan},
    title     = {Track Any Anomalous Object:A Granular Video Anomaly Detection Pipeline},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8689-8699}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Multimedia Trusted Perception and Efficien Computing, Ministry of Education of China, Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "School of Informatics, Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "The University of Virginia", "city": "Charlottesville", "country": "USA"}]

affiliations:
["Key Laboratory of Multimedia Trusted Perception and Efficien Computing, Ministry of Education of China, Xiamen University", "School of Informatics, Xiamen University", "The Chinese University of Hong Kong", "The University of Virginia"]


title: RANGE: Retrieval Augmented Neural Fields for Multi-Resolution Geo-Embeddings
authors: Aayush Dhakal, Srikumar Sastry, Subash Khanal, Adeel Ahmad, Eric Xing, Nathan Jacobs
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24680-24689
affiliations: ["Washington University in St. Louis1", "Taylor Geospatial Institute2"]
cities: ["St. Louis", "St. Louis"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dhakal_RANGE_Retrieval_Augmented_Neural_Fields_for_Multi-Resolution_Geo-Embeddings_CVPR_2025_paper.html

abstract:
The choice of representation for geographic location significantly impacts the accuracy of models for a broad range of geospatial tasks, including fine-grained species classification, population density estimation, and biome classification. Recent works like SatCLIP and GeoCLIP learn such representations by contrastively aligning geolocation with co-located images. While these methods work exceptionally well, in this paper, we posit that the current training strategies fail to fully capture the important visual features. We provide an information theoretic perspective on why the resulting embeddings from these methods discard crucial visual information that is important for many downstream tasks. To solve this problem, we propose a novel retrieval-augmented strategy called RANGE. We build our method on the intuition that the visual features of a location can be estimated by combining the visual features from multiple similar-looking locations. We evaluate our method across a wide variety of tasks. Our results show that RANGE outperforms the existing state-of-the-art models with significant margins in most tasks. We show gains of up to 13.1% on classification tasks and 0.145 R^2 on regression tasks. All our code and models will be made available on github.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dhakal_RANGE_Retrieval_Augmented_Neural_Fields_for_Multi-Resolution_Geo-Embeddings_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dhakal_RANGE_Retrieval_Augmented_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dhakal_2025_CVPR,
    author    = {Dhakal, Aayush and Sastry, Srikumar and Khanal, Subash and Ahmad, Adeel and Xing, Eric and Jacobs, Nathan},
    title     = {RANGE: Retrieval Augmented Neural Fields for Multi-Resolution Geo-Embeddings},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24680-24689}
}

raw_affiliation:
[{"affiliation": "Washington University in St. Louis1", "city": "St. Louis", "country": "USA"}, {"affiliation": "Taylor Geospatial Institute2", "city": "St. Louis", "country": "USA"}]


title: Magma: A Foundation Model for Multimodal AI Agents
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14203-14214
cities: ["Redmond", "College Park", "Madison", "Daejeon", "Seattle"]
countries: ["USA", "USA", "USA", "South Korea", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Magma_A_Foundation_Model_for_Multimodal_AI_Agents_CVPR_2025_paper.html

authors:
Jianwei Yang, Reuben Tan, Qianhui Wu, Ruijie Zheng, Baolin Peng, Yongyuan Liang, Yu Gu, Mu Cai, Seonghyeon Ye, Joel Jang, Yuquan Deng, Jianfeng Gao

abstract:
We present Magma, a foundation model that serves multimodal AI agentic tasks in both the digital and physical worlds.  Magma is a significant extension of vision-language (VL) models in that it not only retains the VL understanding ability (verbal intelligence) of the latter, but is also equipped with the ability to ground and act in the visual-spatial world (spatial-temporal intelligence). To endow agentic capabilities for tasks ranging from UI navigation to robot manipulation, Magma is trained on large amounts of heterogeneous datasets that span from images, videos to robotics data, where actionable visual objects (e.g. clickable buttons in GUI) in images are labeled by Set-of-Mark (SoM) for action grounding, and object movements (e.g. trace of human hands or robotic arms) in videos are labeled by Trace-of-Mark (ToM) for action planning. Extensive experiments show that SoM and ToM help bridge the gap between verbal and action abilities and significantly enhance spatio-temporal intelligence which is fundamental to agentic tasks, as shown in Fig.1. In particular, Magma creates new state-of-the-art results on UI navigation and robotic manipulation tasks, outperforming previous models that are specifically tailored to these tasks. Moreover, Magma preserves strong multimodal understanding ability and compares favorably to popular large multimodal models that are trained on much larger datasets. We have made our model and code public for reproducibility at https://microsoft.github.io/Magma.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Magma_A_Foundation_Model_for_Multimodal_AI_Agents_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Magma_A_Foundation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Jianwei and Tan, Reuben and Wu, Qianhui and Zheng, Ruijie and Peng, Baolin and Liang, Yongyuan and Gu, Yu and Cai, Mu and Ye, Seonghyeon and Jang, Joel and Deng, Yuquan and Gao, Jianfeng},
    title     = {Magma: A Foundation Model for Multimodal AI Agents},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14203-14214}
}

raw_affiliation:
[{"affiliation": "Microsoft Research", "city": "Redmond", "country": "USA"}, {"affiliation": "University of Maryland", "city": "College Park", "country": "USA"}, {"affiliation": "University of Wisconsin-Madison", "city": "Madison", "country": "USA"}, {"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}]

affiliations:
["Microsoft Research", "University of Maryland", "University of Wisconsin-Madison", "KAIST", "University of Washington"]


title: SimMotionEdit: Text-Based Human Motion Editing with Motion Similarity Prediction
authors: Zhengyuan Li, Kai Cheng, Anindita Ghosh, Uttaran Bhattacharya, Liangyan Gui, Aniket Bera
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27827-27837
cities: ["West Lafayette", "Saarbr\u00fccken", "San Jose", "Urbana"]
countries: ["USA", "Germany", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_SimMotionEdit_Text-Based_Human_Motion_Editing_with_Motion_Similarity_Prediction_CVPR_2025_paper.html

abstract:
Text-based 3D human motion editing is a critical yet challenging task in computer vision and graphics. While training-free approaches have been explored, the recent release of the MotionFix dataset, which includes source-text-motion triplets, has opened new avenues for training, yielding promising results. However, existing methods struggle with precise control, often leading to misalignment between motion semantics and language instructions. In this paper, we introduce a related task --- motion similarity prediction --- and propose a multi-task training paradigm, where we train the model jointly on motion editing and motion similarity prediction to foster the learning of semantically meaningful representations. To complement this task, we design an advanced Diffusion-Transformer-based architecture that separately handles motion similarity prediction and motion editing. Extensive experiments demonstrate the state-of-the-art performance of our approach in both editing alignment and fidelity.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_SimMotionEdit_Text-Based_Human_Motion_Editing_with_Motion_Similarity_Prediction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_SimMotionEdit_Text-Based_Human_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Zhengyuan and Cheng, Kai and Ghosh, Anindita and Bhattacharya, Uttaran and Gui, Liangyan and Bera, Aniket},
    title     = {SimMotionEdit: Text-Based Human Motion Editing with Motion Similarity Prediction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27827-27837}
}

raw_affiliation:
[{"affiliation": "Purdue University", "city": "West Lafayette", "country": "USA"}, {"affiliation": "DFKI, MPI-INF, Saarland Informatics Campus", "city": "Saarbr\u00fccken", "country": "Germany"}, {"affiliation": "Adobe Inc.", "city": "San Jose", "country": "USA"}, {"affiliation": "University of Illinois Urbana-Champaign", "city": "Urbana", "country": "USA"}]

affiliations:
["Purdue University", "DFKI, MPI-INF, Saarland Informatics Campus", "Adobe Inc.", "University of Illinois Urbana-Champaign"]


title: Object-aware Sound Source Localization via Audio-Visual Scene Understanding
authors: Sung Jin Um, Dongjin Kim, Sangmin Lee, Jung Uk Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8342-8351
affiliations: ["Kyung Hee University", "KAIST AI", "Sungkyunkwan University"]
cities: ["Seoul", "Daejeon", "Suwon"]
countries: ["South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Um_Object-aware_Sound_Source_Localization_via_Audio-Visual_Scene_Understanding_CVPR_2025_paper.html

abstract:
Audio-visual sound source localization task aims to spatially localize sound-making objects within visual scenes by integrating visual and audio cues. However, existing methods struggle with accurately localizing sound-making objects in complex scenes, particularly when visually similar silent objects coexist. This limitation arises primarily from their reliance on simple audio-visual correspondence, which does not capture fine-grained semantic differences between sound-making and silent objects. To address these challenges, we propose a novel sound source localization framework leveraging Multimodal Large Language Models (MLLMs) to generate detailed contextual information that explicitly distinguishes between sound-making foreground objects and silent background objects. To effectively integrate this detailed information, we introduce two novel loss functions: Object-aware Contrastive Alignment (OCA) loss and Object Region Isolation (ORI) loss. Extensive experimental results on MUSIC and VGGSound datasets demonstrate the effectiveness of our approach, significantly outperforming existing methods in both single-source and multi-source localization scenarios. Code and generated detailed contextual information are available at: https://github.com/VisualAIKHU/OA-SSL.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Um_Object-aware_Sound_Source_Localization_via_Audio-Visual_Scene_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Um_Object-aware_Sound_Source_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Um_2025_CVPR,
    author    = {Um, Sung Jin and Kim, Dongjin and Lee, Sangmin and Kim, Jung Uk},
    title     = {Object-aware Sound Source Localization via Audio-Visual Scene Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8342-8351}
}

raw_affiliation:
[{"affiliation": "Kyung Hee University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "KAIST AI", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "Sungkyunkwan University", "city": "Suwon", "country": "South Korea"}]


authors: Zelin Li, Chenwei Wang, Zhaoke Huang, Yiming Ma, Cunming Zhao, Zhongying Zhao, Hong Yan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16091-16100
affiliations: ["1,2", "2", "3"]
cities: ["Shenzhen", "Shenzhen", "Beijing"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Volume_Tells_Dual_Cycle-Consistent_Diffusion_for_3D_Fluorescence_Microscopy_De-noising_CVPR_2025_paper.html

title:
Volume Tells: Dual Cycle-Consistent Diffusion for 3D Fluorescence Microscopy De-noising and Super-Resolution

abstract:
3D fluorescence microscopy is essential for understanding fundamental life processes through long-term live-cell imaging. However, due to inherent issues in imaging principles, it faces significant challenges including spatially varying noise and anisotropic resolution, where the axial resolution lags behind the lateral resolution up to 4.5 times. Meanwhile, laser power is kept low to maintain cell viability, leading to inaccessible low-noise and high-resolution paired ground truth (GT).  To tackle these limitations, a dual Cycle-consistent Diffusion is proposed to effectively mine intra-volume imaging priors within 3D cell volumes in an unsupervised manner, i.e., Volume Tells (VTCD), achieving de-noising and super-resolution (SR) simultaneously. Specifically, a spatially iso-distributed denoiser is designed to exploit the noise distribution consistency between adjacent low-noise and high-noise regions within the 3D cell volume, suppressing the spatially varying noise. Then, in light of the structural consistency of the cell volume, a cross-plane global-propagation SR module propagates high-resolution details from the XY plane into adjacent regions in the XZ and YZ planes, progressively enhancing resolution across the entire 3D cell volume. Experimental results on 10 in vivo cellular dataset demonstrate high improvements in both denoising and super-resolution, with axial resolution enhanced from  430nm to  90nm

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Volume_Tells_Dual_Cycle-Consistent_Diffusion_for_3D_Fluorescence_Microscopy_De-noising_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Volume_Tells_Dual_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Zelin and Wang, Chenwei and Huang, Zhaoke and Ma, Yiming and Zhao, Cunming and Zhao, Zhongying and Yan, Hong},
    title     = {Volume Tells: Dual Cycle-Consistent Diffusion for 3D Fluorescence Microscopy De-noising and Super-Resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16091-16100}
}

raw_affiliation:
[{"affiliation": "1,2", "city": "Shenzhen", "country": "China"}, {"affiliation": "2", "city": "Shenzhen", "country": "China"}, {"affiliation": "3", "city": "Beijing", "country": "China"}]


title: SerialGen: Personalized Image Generation by First Standardization Then Personalization
authors: Cong Xie, Han Zou, Ruiqi Yu, Yan Zhang, Zhenpeng Zhan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2847-2856
raw_affiliation: [{"affiliation": "Global Business Unit, Baidu Inc.", "city": "Beijing", "country": "China"}]
affiliations: ["Global Business Unit, Baidu Inc."]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_SerialGen_Personalized_Image_Generation_by_First_Standardization_Then_Personalization_CVPR_2025_paper.html

abstract:
In this work, we are interested in achieving both high text controllability and whole-body appearance consistency in the generation of personalized human characters. We propose a novel framework, named SerialGen, which is a serial generation method consisting of two stages: first, a standardization stage that standardizes reference images, and then a personalized generation stage based on the standardized reference. Furthermore, we introduce two modules aimed at enhancing the standardization process. Our experimental results validate the proposed framework's ability to produce personalized images that faithfully recover the reference image's whole-body appearance while accurately responding to a wide range of text prompts. Through thorough analysis, we highlight the critical contribution of the proposed serial generation method and standardization model, evidencing enhancements in appearance consistency between reference and output images and across serial outputs generated from diverse text prompts. The term "Serial" in this work carries a double meaning: it refers to the two-stage method and also underlines our ability to generate serial images with consistent appearance throughout.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_SerialGen_Personalized_Image_Generation_by_First_Standardization_Then_Personalization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_SerialGen_Personalized_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Cong and Zou, Han and Yu, Ruiqi and Zhang, Yan and Zhan, Zhenpeng},
    title     = {SerialGen: Personalized Image Generation by First Standardization Then Personalization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2847-2856}
}


title: From Head to Tail: Efficient Black-box Model Inversion Attack via Long-tailed Learning
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29288-29298
cities: ["Wuhan", "Shanghai", "Hangzhou", "Buffalo"]
countries: ["China", "China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_From_Head_to_Tail_Efficient_Black-box_Model_Inversion_Attack_via_CVPR_2025_paper.html

authors:
Ziang Li, Hongguang Zhang, Juan Wang, Meihui Chen, Hongxin Hu, Wenzhe Yi, Xiaoyang Xu, Mengda Yang, Chenjun Ma

abstract:
Model Inversion Attacks (MIAs) aim to reconstruct private training data from models, leading to privacy leakage, particularly in facial recognition systems. Although many studies have enhanced the effectiveness of white-box MIAs, less attention has been paid to improving efficiency and utility under limited attacker capabilities. Existing black-box MIAs necessitate an impractical number of queries, incurring significant overhead. Therefore, we analyze the limitations of existing MIAs and introduce Surrogate Model-based Inversion with Long-tailed Enhancement (SMILE), a high-resolution oriented and query-efficient MIA for the black-box setting. We begin by analyzing the initialization of MIAs from a data distribution perspective and propose a long-tailed surrogate training method to obtain high-quality initial points. We then enhance the attack's effectiveness by employing the gradient-free black-box optimization algorithm selected by NGOpt. Our experiments show that SMILE outperforms existing state-of-the-art black-box MIAs while requiring only about 5% of the query overhead.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_From_Head_to_Tail_Efficient_Black-box_Model_Inversion_Attack_via_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_From_Head_to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Ziang and Zhang, Hongguang and Wang, Juan and Chen, Meihui and Hu, Hongxin and Yi, Wenzhe and Xu, Xiaoyang and Yang, Mengda and Ma, Chenjun},
    title     = {From Head to Tail: Efficient Black-box Model Inversion Attack via Long-tailed Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29288-29298}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Shanghai Innovation Institute", "city": "Shanghai", "country": "China"}, {"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Department of Computer Science and Engineering, University at Buffalo, SUNY", "city": "Buffalo", "country": "USA"}]

affiliations:
["Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University", "Shanghai Innovation Institute", "Ant Group", "Department of Computer Science and Engineering, University at Buffalo, SUNY"]


title: Augmented Deep Contexts for Spatially Embedded Video Coding
authors: Yifan Bian, Chuanbo Tang, Li Li, Dong Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2094-2104
affiliations: ["University of Science and Technology of China"]
cities: ["Hefei"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bian_Augmented_Deep_Contexts_for_Spatially_Embedded_Video_Coding_CVPR_2025_paper.html

abstract:
Most Neural Video Codecs (NVCs) only employ temporal references to generate temporal-only contexts and latent prior. These temporal-only NVCs fail to handle large motions or emerging objects due to limited contexts and misaligned latent prior. To relieve the limitations, we propose a Spatially Embedded Video Codec (SEVC), in which the low-resolution video is compressed for spatial references. Firstly, our SEVC leverages both spatial and temporal references to generate augmented motion vectors and hybrid spatial-temporal contexts. Secondly, to address the misalignment issue in latent prior and enrich the prior information, we introduce a spatial-guided latent prior augmented by multiple temporal latent representations. At last, we design a joint spatial-temporal optimization to learn quality-adaptive bit allocation for spatial references, further boosting rate-distortion performance. Experimental results show that our SEVC effectively alleviates the limitations in handling large motions or emerging objects, and also reduces 11.9% more bitrate than the previous state-of-the-art NVC while providing an additional low-resolution bitstream. Our code and model are available at https://github.com/EsakaK/SEVC.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bian_Augmented_Deep_Contexts_for_Spatially_Embedded_Video_Coding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bian_Augmented_Deep_Contexts_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bian_2025_CVPR,
    author    = {Bian, Yifan and Tang, Chuanbo and Li, Li and Liu, Dong},
    title     = {Augmented Deep Contexts for Spatially Embedded Video Coding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2094-2104}
}


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28831-28841
cities: ["Liverpool", "Singapore", "Warwick", "Shenzhen"]
countries: ["UK", "SG", "UK", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_SIDA_Social_Media_Image_Deepfake_Detection_Localization_and_Explanation_with_CVPR_2025_paper.html

title:
SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model

authors:
Zhenglin Huang, Jinwei Hu, Xiangtai Li, Yiwei He, Xingyu Zhao, Bei Peng, Baoyuan Wu, Xiaowei Huang, Guangliang Cheng

abstract:
The rapid advancement of generative models in creating highly realistic images poses substantial risks for misinformation dissemination. For instance, a synthetic image, when shared on social media, can mislead extensive audiences and erode trust in digital content, resulting in severe repercussions. Despite some progress, academia has not yet created a large and diversified deepfake detection dataset for social media, nor has it devised an effective solution to address this issue. In this paper, we introduce the Social media Image Detection dataSet (SID-Set), which offers three key advantages: (1) extensive volume, featuring 300K AI-generated/tampered and authentic images with comprehensive annotations, (2) broad diversity, encompassing fully synthetic and tampered images across various classes, and (3) elevated realism, with images that are predominantly indistinguishable from genuine ones through mere visual inspection. Furthermore, leveraging the exceptional capabilities of large multimodal models, we propose a new image deepfake detection, localization, and explanation framework, named SIDA (Social media Image Detection, localization, and explanation Assistant). SIDA not only discerns the authenticity of images, but also delineates tampered regions through mask prediction and provides textual explanations of the model's judgment criteria. Compared with state-of-the-art deepfake detection models on SID-Set and other benchmarks, extensive experiments demonstrate that SIDA achieves superior performance among diversified settings. The code, model, and dataset will be released.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_SIDA_Social_Media_Image_Deepfake_Detection_Localization_and_Explanation_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_SIDA_Social_Media_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Zhenglin and Hu, Jinwei and Li, Xiangtai and He, Yiwei and Zhao, Xingyu and Peng, Bei and Wu, Baoyuan and Huang, Xiaowei and Cheng, Guangliang},
    title     = {SIDA: Social Media Image Deepfake Detection, Localization and Explanation with Large Multimodal Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28831-28841}
}

raw_affiliation:
[{"affiliation": "University of Liverpool", "city": "Liverpool", "country": "UK"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "SG"}, {"affiliation": "WMG, University of Warwick", "city": "Warwick", "country": "UK"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Shenzhen", "country": "China"}]

affiliations:
["University of Liverpool", "Nanyang Technological University", "WMG, University of Warwick", "The Chinese University of Hong Kong"]


title: Matrix3D: Large Photogrammetry Model All-in-One
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11250-11263
affiliations: ["Nanjing University", "Apple", "The Hong Kong University of Science and Technology"]
cities: ["Nanjing", "Cupertino", "Hong Kong"]
countries: ["China", "USA", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lu_Matrix3D_Large_Photogrammetry_Model_All-in-One_CVPR_2025_paper.html

authors:
Yuanxun Lu, Jingyang Zhang, Tian Fang, Jean-Daniel Nahmias, Yanghai Tsin, Long Quan, Xun Cao, Yao Yao, Shiwei Li

abstract:
We present Matrix3D, a unified model that performs several photogrammetry subtasks, including pose estimation, depth prediction, and novel view synthesis using just the same model. Matrix3D utilizes a multi-modal diffusion transformer (DiT) to integrate transformations across several modalities, such as images, camera parameters, and depth maps. The key to Matrix3D's large-scale multi-modal training lies in the incorporation of a mask learning strategy. This enables full-modality model training even with partially complete data, such as bi-modality data of image-pose and image-depth pairs, thus significantly increases the pool of available training data. Matrix3D demonstrates state-of-the-art performance in pose estimation and novel view synthesis tasks. Additionally, it offers fine-grained control through multi-round interactions, making it an innovative tool for 3D content creation. Project page: https://nju-3dv.github.io/projects/matrix3d.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_Matrix3D_Large_Photogrammetry_Model_All-in-One_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lu_Matrix3D_Large_Photogrammetry_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lu_2025_CVPR,
    author    = {Lu, Yuanxun and Zhang, Jingyang and Fang, Tian and Nahmias, Jean-Daniel and Tsin, Yanghai and Quan, Long and Cao, Xun and Yao, Yao and Li, Shiwei},
    title     = {Matrix3D: Large Photogrammetry Model All-in-One},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11250-11263}
}

raw_affiliation:
[{"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "Apple", "city": "Cupertino", "country": "USA"}, {"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}]


title: Object-Centric Prompt-Driven Vision-Language-Action Model for Robotic Manipulation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27638-27648
affiliations: ["School of Computer Science, Peking University", "PKU-Agibot Lab"]
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Object-Centric_Prompt-Driven_Vision-Language-Action_Model_for_Robotic_Manipulation_CVPR_2025_paper.html

authors:
Xiaoqi Li, Jingyun Xu, Mingxu Zhang, Jiaming Liu, Yan Shen, Iaroslav Ponomarenko, Jiahui Xu, Liang Heng, Siyuan Huang, Shanghang Zhang, Hao Dong

abstract:
In robotic manipulation, task goals can be conveyed through various modalities, such as language, goal images, and goal videos. However, natural language can be ambiguous, while images or videos may offer overly detailed specifications. To address these challenges, we propose a novel approach using comprehensive multi-modal prompts that explicitly convey both low-level actions and high-level planning in a simple manner. Specifically, for each key-frame in the task sequence, our method allows for manual or automatic generation of simple and expressive 2D visual prompts overlaid on RGB images. These prompts represent the required task goals, such as the end-effector pose and the desired movement direction after contact. We develop a training strategy that enables the model to interpret these visual-language prompts and predict the corresponding contact poses and movement directions in SE(3) space. Furthermore, by sequentially executing all key-frame steps, the model can complete long-horizon tasks. This approach not only helps the model explicitly understand the task objectives but also enhances its robustness on unseen tasks by providing easily interpretable prompts. We evaluate our method in both simulated and real-world environments, demonstrating its robust manipulation capabilities.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Object-Centric_Prompt-Driven_Vision-Language-Action_Model_for_Robotic_Manipulation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Object-Centric_Prompt-Driven_Vision-Language-Action_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Xiaoqi and Xu, Jingyun and Zhang, Mingxu and Liu, Jiaming and Shen, Yan and Ponomarenko, Iaroslav and Xu, Jiahui and Heng, Liang and Huang, Siyuan and Zhang, Shanghang and Dong, Hao},
    title     = {Object-Centric Prompt-Driven Vision-Language-Action Model for Robotic Manipulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27638-27648}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "PKU-Agibot Lab", "city": "Beijing", "country": "China"}]


authors: Ping Wang, Lishun Wang, Gang Qu, Xiaodong Wang, Yulun Zhang, Xin Yuan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 411-421
affiliations: ["Westlake University", "Zhejiang University", "Shanghai Jiao Tong University"]
cities: ["Hangzhou", "Hangzhou", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Proximal_Algorithm_Unrolling_Flexible_and_Efficient_Reconstruction_Networks_for_Single-Pixel_CVPR_2025_paper.html

title:
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction Networks for Single-Pixel Imaging

abstract:
Deep-unrolling and plug-and-play (PnP) approaches have become the de-facto standard solvers for single-pixel imaging (SPI) inverse problem. PnP approaches, a class of iterative algorithms where regularization is implicitly performed by an off-the-shelf deep denoiser, are flexible for varying compression ratios (CRs) but are limited in reconstruction accuracy and speed. Conversely, unrolling approaches, a class of multi-stage neural networks where a truncated iterative optimization process is transformed into an end-to-end trainable network, typically achieve better accuracy with faster inference but require fine-tuning or even retraining when CR changes. In this paper, we address the challenge of integrating the strengths of both classes of solvers. To this end, we design an efficient deep image restorer (DIR) for the unrolling of HQS (half quadratic splitting) and ADMM (alternating direction method of multipliers). More importantly, a general proximal trajectory (PT) loss function is proposed to train HQS/ADMM-unrolling networks such that learned DIR approximates the proximal operator of an ideal explicit restoration regularizer. Extensive experiments demonstrate that, the resulting proximal unrolling networks can not only flexibly handle varying CRs with a single model like PnP algorithms, but also outperform previous CR-specific unrolling networks in both reconstruction accuracy and speed. Source codes and models are available at https://github.com/pwangcs/ProxUnroll.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Proximal_Algorithm_Unrolling_Flexible_and_Efficient_Reconstruction_Networks_for_Single-Pixel_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Proximal_Algorithm_Unrolling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Ping and Wang, Lishun and Qu, Gang and Wang, Xiaodong and Zhang, Yulun and Yuan, Xin},
    title     = {Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction Networks for Single-Pixel Imaging},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {411-421}
}

raw_affiliation:
[{"affiliation": "Westlake University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]


title: 3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement
authors: Yihang Luo, Shangchen Zhou, Yushi Lan, Xingang Pan, Chen Change Loy
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16430-16440
affiliations: ["S-Lab, Nanyang Technological University"]
cities: ["Singapore"]
countries: ["Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Luo_3DEnhancer_Consistent_Multi-View_Diffusion_for_3D_Enhancement_CVPR_2025_paper.html

abstract:
Despite advances in neural rendering, due to the scarcity of high-quality 3D datasets and the inherent limitations of multi-view diffusion models, view synthesis and 3D model generation are restricted to low resolutions with suboptimal multi-view consistency. In this study, we present a novel 3D enhancement pipeline, dubbed 3DEnhancer, which employs a multi-view latent diffusion model to enhance coarse 3D inputs while preserving multi-view consistency. Our method includes a pose-aware encoder and a diffusion-based denoiser to refine low-quality multi-view images, along with data augmentation and a multi-view attention module with epipolar aggregation to maintain consistent, high-quality 3D outputs across views. Unlike existing video-based approaches, our model supports seamless multi-view enhancement with improved coherence across diverse viewing angles. Extensive evaluations show that 3DEnhancer significantly outperforms existing methods, boosting both multi-view enhancement and per-instance 3D optimization tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Luo_3DEnhancer_Consistent_Multi-View_Diffusion_for_3D_Enhancement_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Luo_3DEnhancer_Consistent_Multi-View_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Luo_2025_CVPR,
    author    = {Luo, Yihang and Zhou, Shangchen and Lan, Yushi and Pan, Xingang and Loy, Chen Change},
    title     = {3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16430-16440}
}

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


title: Investigating the Role of Weight Decay in Enhancing Nonconvex SGD
authors: Tao Sun, Yuhao Huang, Li Shen, Kele Xu, Bao Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15287-15296
cities: ["Changsha", "Salt Lake City", "Guangzhou", "Changsha", "Salt Lake City"]
countries: ["China", "USA", "China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_Investigating_the_Role_of_Weight_Decay_in_Enhancing_Nonconvex_SGD_CVPR_2025_paper.html

abstract:
Weight decay is a widely used technique in training machine learning models, known to empirically enhance the generalization of Stochastic Gradient Descent (SGD). While intuitively weight decay allows SGD to train a regularized model rather than the original one, there is limited theoretical understanding of why SGD with weight decay (SGDW) yields results consistent with the unregularized model, or how weight decay improves generalization. This paper establishes a convergence theory for SGDW in the context of the unregularized model, under weaker assumptions than previous analyses of weight decay. Our theory demonstrates that weight decay does not accelerate the convergence of SGD. For generalization, we provide the first theoretical proof of weight decay's benefit in nonconvex optimization. Additionally, we extend our results to sign-based stochastic gradient algorithms, such as SignSGD. Numerical experiments on classical benchmarks validate our theoretical findings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_Investigating_the_Role_of_Weight_Decay_in_Enhancing_Nonconvex_SGD_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_Investigating_the_Role_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Tao and Huang, Yuhao and Shen, Li and Xu, Kele and Wang, Bao},
    title     = {Investigating the Role of Weight Decay in Enhancing Nonconvex SGD},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15287-15296}
}

raw_affiliation:
[{"affiliation": "National University of Defense Technology", "city": "Changsha", "country": "China"}, {"affiliation": "University of Utah", "city": "Salt Lake City", "country": "USA"}, {"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "National University of Defense Technology", "city": "Changsha", "country": "China"}, {"affiliation": "University of Utah", "city": "Salt Lake City", "country": "USA"}]

affiliations:
["National University of Defense Technology", "University of Utah", "Sun Yat-sen University", "National University of Defense Technology", "University of Utah"]


title: MarkushGrapher: Joint Visual and Textual Recognition of Markush Structures
authors: Lucas Morin, Valery Weber, Ahmed Nassar, Gerhard Ingmar Meijer, Luc Van Gool, Yawei Li, Peter Staar
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14505-14515
affiliations: ["IBM Research", "ETH Zurich", "INSAIT"]
cities: ["Zurich", "Zurich", "Sofia"]
countries: ["Switzerland", "Switzerland", "Bulgaria"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Morin_MarkushGrapher_Joint_Visual_and_Textual_Recognition_of_Markush_Structures_CVPR_2025_paper.html

abstract:
The automated analysis of chemical literature holds promise to accelerate discovery in fields such as material science and drug development. In particular, search capabilities for chemical structures and Markush structures (chemical structure templates) within patent documents are valuable, e.g., for prior-art search. Advancements have been made in the automatic extraction of chemical structures from text and images, yet the Markush structures remain largely unexplored due to their complex multi-modal nature. In this work, we present MarkushGrapher, a multi-modal approach for recognizing Markush structures in documents. Our method jointly encodes text, image, and layout information through a Vision-Text-Layout encoder and an Optical Chemical Structure Recognition vision encoder. These representations are merged and used to auto-regressively generate a sequential graph representation of the Markush structure along with a table defining its variable groups. To overcome the lack of real-world training data, we propose a synthetic data generation pipeline that produces a wide range of realistic Markush structures. Additionally, we present M2S, the first annotated benchmark of real-world Markush structures, to advance research on this challenging task. Extensive experiments demonstrate that our approach outperforms state-of-the-art chemistry-specific and general-purpose vision-language models in most evaluation settings. Code, models, and datasets are available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Morin_MarkushGrapher_Joint_Visual_and_Textual_Recognition_of_Markush_Structures_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Morin_MarkushGrapher_Joint_Visual_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Morin_2025_CVPR,
    author    = {Morin, Lucas and Weber, Valery and Nassar, Ahmed and Meijer, Gerhard Ingmar and Van Gool, Luc and Li, Yawei and Staar, Peter},
    title     = {MarkushGrapher: Joint Visual and Textual Recognition of Markush Structures},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14505-14515}
}

raw_affiliation:
[{"affiliation": "IBM Research", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "INSAIT", "city": "Sofia", "country": "Bulgaria"}]


title: Depth Any Camera: Zero-Shot Metric Depth Estimation from Any Camera
authors: Yuliang Guo, Sparsh Garg, S. Mahdi H. Miangoleh, Xinyu Huang, Liu Ren
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26996-27006
cities: ["Palo Alto", "Pittsburgh", "Burnaby"]
countries: ["USA", "USA", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_Depth_Any_Camera_Zero-Shot_Metric_Depth_Estimation_from_Any_Camera_CVPR_2025_paper.html

abstract:
While recent depth foundation models exhibit strong zero-shot generalization, achieving accurate metric depth across diverse camera types--particularly those with large fields of view (FoV) such as fisheye and 360-degree cameras--remains a significant challenge. This paper presents Depth Any Camera (DAC), a powerful zero-shot metric depth estimation framework that extends a perspective-trained model to effectively handle cameras with varying FoVs. The framework is designed to ensure that all existing 3D data can be leveraged, regardless of the specific camera types used in new applications. Remarkably, DAC is trained exclusively on perspective images but generalizes seamlessly to fisheye and 360-degree cameras without the need for specialized training data. DAC employs Equi-Rectangular Projection (ERP) as a unified image representation, enabling consistent processing of images with diverse FoVs. Its key components include a pitch-aware Image-to-ERP conversion for efficient online augmentation in ERP space, a FoV alignment operation to support effective training across a wide range of FoVs, and multi-resolution data augmentation to address resolution disparities between training and testing. DAC achieves state-of-the-art zero-shot metric depth estimation, improving delta-1 accuracy by up to 50% on multiple fisheye and 360-degree datasets compared to prior metric depth foundation models, demonstrating robust generalization across camera types.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_Depth_Any_Camera_Zero-Shot_Metric_Depth_Estimation_from_Any_Camera_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_Depth_Any_Camera_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Yuliang and Garg, Sparsh and Miangoleh, S. Mahdi H. and Huang, Xinyu and Ren, Liu},
    title     = {Depth Any Camera: Zero-Shot Metric Depth Estimation from Any Camera},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26996-27006}
}

raw_affiliation:
[{"affiliation": "Bosch Research North America & Bosch Center for Arti\ufb01cial Intelligence (BCAI)", "city": "Palo Alto", "country": "USA"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "Simon Fraser University", "city": "Burnaby", "country": "Canada"}]

affiliations:
["Bosch Research North America & Bosch Center for Arti\ufb01cial Intelligence (BCAI)", "Carnegie Mellon University", "Simon Fraser University"]


title: Image Quality Assessment: From Human to Machine Preference
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7570-7581
cities: ["Shanghai", "Shanghai", "Unknown", "Singapore"]
countries: ["China", "China", "Unknown", "Singapore"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Image_Quality_Assessment_From_Human_to_Machine_Preference_CVPR_2025_paper.html

authors:
Chunyi Li, Yuan Tian, Xiaoyue Ling, Zicheng Zhang, Haodong Duan, Haoning Wu, Ziheng Jia, Xiaohong Liu, Xiongkuo Min, Guo Lu, Weisi Lin, Guangtao Zhai

abstract:
Image Quality Assessment (IQA) based on human subjective preferences has undergone extensive research in the past decades. However, with the development of communication protocols, the visual data consumption volume of machines has gradually surpassed that of humans. For machines, the preference depends on downstream tasks such as segmentation and detection, rather than visual appeal. Considering the huge gap between human and machine vision systems, this paper proposes the topic: Image Quality Assessment for Machine Vision for the first time. Specifically, we (1) defined the subjective preferences of machines, including downstream tasks, test models, and evaluation metrics; (2) established the Machine Preference Database (MPD), which contains 2.25M fine-grained annotations and 30k reference/distorted image pair instances; (3) verified the performance of mainstream IQA algorithms on MPD. Experiments show that current IQA metrics are human-centric and cannot accurately characterize machine preferences. We sincerely hope that MPD can promote the evolution of IQA from human to machine preferences. Project page is on: https://github.com/lcysyzxdxc/MPD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Image_Quality_Assessment_From_Human_to_Machine_Preference_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Chunyi and Tian, Yuan and Ling, Xiaoyue and Zhang, Zicheng and Duan, Haodong and Wu, Haoning and Jia, Ziheng and Liu, Xiaohong and Min, Xiongkuo and Lu, Guo and Lin, Weisi and Zhai, Guangtao},
    title     = {Image Quality Assessment: From Human to Machine Preference},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7570-7581}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai AI Lab", "city": "Shanghai", "country": "China"}, {"affiliation": "Moonshot AI", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]

affiliations:
["Shanghai Jiao Tong University", "Shanghai AI Lab", "Moonshot AI", "Nanyang Technological University"]


title: ShotAdapter: Text-to-Multi-Shot Video Generation with Diffusion Models
authors: Ozgur Kara, Krishna Kumar Singh, Feng Liu, Duygu Ceylan, James M. Rehg, Tobias Hinz
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28405-28415
affiliations: ["UIUC", "Adobe"]
cities: ["Urbana", "San Jose"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kara_ShotAdapter_Text-to-Multi-Shot_Video_Generation_with_Diffusion_Models_CVPR_2025_paper.html

abstract:
Current diffusion-based text-to-video methods are limited to producing short video clips of a single shot and lack the capability to generate multi-shot videos with discrete transitions where the same character performs distinct activities across the same or different backgrounds. To address this limitation we propose a framework that includes a dataset collection pipeline and architectural extensions to video diffusion models to enable text-to-multi-shot video generation. Our approach enables generation of multi-shot videos as a single video with full attention across all frames of all shots, ensuring character and background consistency, and allows users to control the number, duration, and content of shots through shot-specific conditioning. This is achieved by incorporating a transition token into the text-to-video model to control at which frames a new shot begins and a local attention masking strategy which controls the transition token's effect and allows shot-specific prompting. To obtain training data we propose a novel data collection pipeline to construct a multi-shot video dataset from existing single-shot video datasets. Extensive experiments demonstrate that fine-tuning a pre-trained text-to-video model for a few thousand iterations is enough for the model to subsequently be able to generate multi-shot videos with shot-specific control, outperforming the baselines. You can find more details in our webpage.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kara_ShotAdapter_Text-to-Multi-Shot_Video_Generation_with_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kara_ShotAdapter_Text-to-Multi-Shot_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kara_2025_CVPR,
    author    = {Kara, Ozgur and Singh, Krishna Kumar and Liu, Feng and Ceylan, Duygu and Rehg, James M. and Hinz, Tobias},
    title     = {ShotAdapter: Text-to-Multi-Shot Video Generation with Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28405-28415}
}

raw_affiliation:
[{"affiliation": "UIUC", "city": "Urbana", "country": "USA"}, {"affiliation": "Adobe", "city": "San Jose", "country": "USA"}]


title: Context-Aware Multimodal Pretraining
authors: Karsten Roth, Zeynep Akata, Dima Damen, Ivana Balazevic, Olivier J. Henaff
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4267-4279
cities: ["T\u00fcbingen", "Munich", "Munich", "London"]
countries: ["Germany", "Germany", "Germany", "United Kingdom"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Roth_Context-Aware_Multimodal_Pretraining_CVPR_2025_paper.html

abstract:
Large-scale multimodal representation learning successfully optimizes for zero-shot transfer at test time. Yet the standard pretraining paradigm (contrastive learning on large amounts of image-text data) does not explicitly encourage representations to support few-shot adaptation. In this work, we propose a simple, but carefully designed extension to multimodal pretraining which enables representations to accommodate additional context. Using this objective, we show that vision-language models can be trained to exhibit significantly increased few-shot adaptation: across 21 downstream tasks, we find up to four-fold improvements in test-time sample efficiency, and average few-shot adaptation gains of over 5%, while retaining zero-shot generalization performance across model scales and training durations. In particular, equipped with simple, training-free, metric-based adaptation mechanisms, our representations surpass significantly more complex optimization-based adaptation schemes.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Roth_Context-Aware_Multimodal_Pretraining_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Roth_Context-Aware_Multimodal_Pretraining_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Roth_2025_CVPR,
    author    = {Roth, Karsten and Akata, Zeynep and Damen, Dima and Balazevic, Ivana and Henaff, Olivier J.},
    title     = {Context-Aware Multimodal Pretraining},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4267-4279}
}

raw_affiliation:
[{"affiliation": "T\u00a8ubingen AI Center", "city": "T\u00fcbingen", "country": "Germany"}, {"affiliation": "Munich Center for ML", "city": "Munich", "country": "Germany"}, {"affiliation": "Helmholtz Munich, TU Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Google DeepMind", "city": "London", "country": "United Kingdom"}]

affiliations:
["T\u00a8ubingen AI Center", "Munich Center for ML", "Helmholtz Munich, TU Munich", "Google DeepMind"]


title: Sound Bridge: Associating Egocentric and Exocentric Videos via Audio Cues
authors: Sihong Huang, Jiaxin Wu, Xiaoyong Wei, Yi Cai, Dongmei Jiang, Yaowei Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28942-28951
cities: ["Guangzhou", "Hong Kong", "Chengdu", "Shenzhen", "Shenzhen"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Sound_Bridge_Associating_Egocentric_and_Exocentric_Videos_via_Audio_Cues_CVPR_2025_paper.html

abstract:
Understanding human behavior and the environmental information in the egocentric video is very challenging due to the invisibility of some actions (e.g., laughing and sneezing) and the local nature of the first-person view. Leveraging the corresponding exocentric video to provide global context has shown promising results. However, existing visual-to-visual and visual-to-textual Ego-Exo video alignment methods struggle with the problem that there could be non-visual overlap for the same activity. To address this, we propose using sound as a bridge, as audio is often consistent across Ego-Exo videos. However, direct audio-to-audio alignment lacks context. Thus, we introduce two context-aware sound modules: one aligns audio with vision via a visual-audio cross-attention module, and another aligns text with sound closed caption generated by LLM. Experimental results on two Ego-Exo video association benchmarks show that either of the two proposed modules manages to improve the state-of-the-art methods. Moreover, the proposed sound-aware egocentric or exocentric representation boosts the performance of downstream tasks, such as action recognition of exocentric videos and scene recognition of egocentric videos. The code and models can be accessed at  https://github.com/shhuangcoder/SoundBridge.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Sound_Bridge_Associating_Egocentric_and_Exocentric_Videos_via_Audio_Cues_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_Sound_Bridge_Associating_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Sihong and Wu, Jiaxin and Wei, Xiaoyong and Cai, Yi and Jiang, Dongmei and Wang, Yaowei},
    title     = {Sound Bridge: Associating Egocentric and Exocentric Videos via Audio Cues},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28942-28951}
}

raw_affiliation:
[{"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "China"}, {"affiliation": "Sichuan University", "city": "Chengdu", "country": "China"}, {"affiliation": "Harbin Institute of Technology, Shenzhen", "city": "Shenzhen", "country": "China"}, {"affiliation": "Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}]

affiliations:
["South China University of Technology", "The Hong Kong Polytechnic University", "Sichuan University", "Harbin Institute of Technology, Shenzhen", "Peng Cheng Laboratory"]


title: Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection
authors: Jiahao Xu, Zikai Zhang, Rui Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20654-20664
raw_affiliation: [{"affiliation": "University of Nevada, Reno", "city": "Reno", "country": "USA"}]
affiliations: ["University of Nevada, Reno"]
cities: ["Reno"]
countries: ["USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Detecting_Backdoor_Attacks_in_Federated_Learning_via_Direction_Alignment_Inspection_CVPR_2025_paper.html

abstract:
The distributed nature of training makes Federated Learning (FL) vulnerable to backdoor attacks, where malicious model updates aim to compromise the global model's performance on specific tasks. Existing defense methods show limited efficacy as they overlook the inconsistency between benign and malicious model updates regarding both general and fine-grained directions. To fill this gap, we introduce AlignIns, a novel defense method designed to safeguard FL systems against backdoor attacks. AlignIns looks into the direction of each model update through a direction alignment inspection process. Specifically, it examines the alignment of model updates with the overall update direction and analyzes the distribution of the signs of their significant parameters, comparing them with the principle sign across all model updates. Model updates that exhibit an unusual degree of alignment are considered malicious and thus be filtered out. We provide the theoretical analysis of the robustness of AlignIns and its propagation error in FL. Our empirical results on both independent and identically distributed (IID) and non-IID datasets demonstrate that AlignIns achieves higher robustness compared to the state-of-the-art defense methods. Code is available at https://anonymous.4open.science/r/AlignIns.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_Detecting_Backdoor_Attacks_in_Federated_Learning_via_Direction_Alignment_Inspection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_Detecting_Backdoor_Attacks_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Jiahao and Zhang, Zikai and Hu, Rui},
    title     = {Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20654-20664}
}


title: OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24838-24848
affiliations: ["Shanghai AI Laboratory", "Abaka AI", "2077AI"]
cities: ["Shanghai", "Unknown", "Unknown"]
countries: ["China", "Unknown", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ouyang_OmniDocBench_Benchmarking_Diverse_PDF_Document_Parsing_with_Comprehensive_Annotations_CVPR_2025_paper.html

authors:
Linke Ouyang, Yuan Qu, Hongbin Zhou, Jiawei Zhu, Rui Zhang, Qunshu Lin, Bin Wang, Zhiyuan Zhao, Man Jiang, Xiaomeng Zhao, Jin Shi, Fan Wu, Pei Chu, Minghao Liu, Zhenxiang Li, Chao Xu, Bo Zhang, Botian Shi, Zhongying Tu, Conghui He

abstract:
Document content extraction is a critical task in computer vision, underpinning the data needs of large language models (LLMs) and retrieval-augmented generation (RAG) systems. Despite recent progress, current document parsing methods have not been fairly and comprehensively evaluated due to the narrow coverage of document types and the simplified, unrealistic evaluation procedures in existing benchmarks. To address these gaps, we introduce OmniDocBench, a novel benchmark featuring high-quality annotations across nine document sources, including academic papers, textbooks, and more challenging cases such as handwritten notes and densely typeset newspapers. OmniDocBench supports flexible, multi-level evaluations--ranging from an end-to-end assessment to the task-specific and attribute-based analysis--using 19 layout categories and 15 attribute labels. We conduct a thorough evaluation of both pipeline-based methods and end-to-end vision-language models, revealing their strengths and weaknesses across different document types. OmniDocBench sets a new standard for the fair, diverse, and fine-grained evaluation in document parsing. Dataset and code are available at https://github.com/opendatalab/OmniDocBench.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ouyang_OmniDocBench_Benchmarking_Diverse_PDF_Document_Parsing_with_Comprehensive_Annotations_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ouyang_OmniDocBench_Benchmarking_Diverse_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ouyang_2025_CVPR,
    author    = {Ouyang, Linke and Qu, Yuan and Zhou, Hongbin and Zhu, Jiawei and Zhang, Rui and Lin, Qunshu and Wang, Bin and Zhao, Zhiyuan and Jiang, Man and Zhao, Xiaomeng and Shi, Jin and Wu, Fan and Chu, Pei and Liu, Minghao and Li, Zhenxiang and Xu, Chao and Zhang, Bo and Shi, Botian and Tu, Zhongying and He, Conghui},
    title     = {OmniDocBench: Benchmarking Diverse PDF Document Parsing with Comprehensive Annotations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24838-24848}
}

raw_affiliation:
[{"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Abaka AI", "city": "Unknown", "country": "Unknown"}, {"affiliation": "2077AI", "city": "Unknown", "country": "Unknown"}]


title: LayoutVLM: Differentiable Optimization of 3D Layout via Vision-Language Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29469-29478
affiliations: ["Stanford University", "Google Research"]
cities: ["Stanford", "Mountain View"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_LayoutVLM_Differentiable_Optimization_of_3D_Layout_via_Vision-Language_Models_CVPR_2025_paper.html

authors:
Fan-Yun Sun, Weiyu Liu, Siyi Gu, Dylan Lim, Goutam Bhat, Federico Tombari, Manling Li, Nick Haber, Jiajun Wu

abstract:
Spatial reasoning is a fundamental aspect of human cognition, enabling intuitive understanding and manipulation of objects in three-dimensional space. While foundation models demonstrate remarkable performance on some benchmarks, they still struggle with 3D reasoning tasks like arranging objects in space according to open-ended language instructions, particularly in dense and physically constrained environments. We introduce LayoutVLM, a framework and scene layout representation that exploits the semantic knowledge of Vision-Language Models (VLMs) and supports differentiable optimization to ensure physical plausibility. LayoutVLM employs VLMs to generate two mutually reinforcing representations from visually marked images, and a self-consistent decoding process to improve VLMs spatial planning. Our experiments show that LayoutVLM addresses the limitations of existing LLM and constraint-based approaches, producing physically plausible 3D layouts better aligned with the semantic intent of input language instructions. We also demonstrate that fine-tuning VLMs with the proposed scene layout representation extracted from existing scene datasets can improve their reasoning performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_LayoutVLM_Differentiable_Optimization_of_3D_Layout_via_Vision-Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_LayoutVLM_Differentiable_Optimization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Fan-Yun and Liu, Weiyu and Gu, Siyi and Lim, Dylan and Bhat, Goutam and Tombari, Federico and Li, Manling and Haber, Nick and Wu, Jiajun},
    title     = {LayoutVLM: Differentiable Optimization of 3D Layout via Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29469-29478}
}

raw_affiliation:
[{"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "Google Research", "city": "Mountain View", "country": "USA"}]


title: Point Clouds Meets Physics: Dynamic Acoustic Field Fitting Network for Point Cloud Understanding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22182-22192
cities: ["Singapore", "Shanghai", "Singapore", "Beijing", "Singapore", "Beijing", "Beijing", "Halmstad"]
countries: ["Singapore", "China", "Singapore", "China", "Singapore", "China", "China", "Sweden"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Point_Clouds_Meets_Physics_Dynamic_Acoustic_Field_Fitting_Network_for_CVPR_2025_paper.html

authors:
Changshuo Wang, Shuting He, Xiang Fang, Jiawei Han, Zhonghang Liu, Xin Ning, Weijun Li, Prayag Tiwari

abstract:
While existing pre-training-based methods have enhanced point cloud model performance, they have not fundamentally resolved the challenge of local structure representation in point clouds. The limited representational capacity of pure point cloud models continues to constrain the potential of cross-modal fusion methods and performance across various tasks. To address this challenge, we propose a Dynamic Acoustic Field Fitting Network (DAF-Net), inspired by physical acoustic principles. Specifically, we represent local point clouds as acoustic fields and introduce a novel Acoustic Field Convolution (AF-Conv), which treats local aggregation as an acoustic energy field modeling problem and captures fine-grained local shape awareness by dividing the local area into near field and far field. Furthermore, drawing inspiration from multi-frequency wave phenomena and dynamic convolution, we develop the Dynamic Acoustic Field Convolution (DAF-Conv) based on AF-Conv. DAF-Conv dynamically generates multiple weights based on local geometric priors, effectively enhancing adaptability to diverse geometric features. Additionally, we design a Global Shape-Aware (GSA) layer incorporating EdgeConv and multi-head attention mechanisms, which combines with DAF-Conv to form the DAF Block. These blocks are then stacked to create a hierarchical DAFNet architecture. Extensive experiments demonstrate that DAFNet significantly outperforms existing methods across multiple tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Point_Clouds_Meets_Physics_Dynamic_Acoustic_Field_Fitting_Network_for_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Changshuo and He, Shuting and Fang, Xiang and Han, Jiawei and Liu, Zhonghang and Ning, Xin and Li, Weijun and Tiwari, Prayag},
    title     = {Point Clouds Meets Physics: Dynamic Acoustic Field Fitting Network for Point Cloud Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22182-22192}
}

raw_affiliation:
[{"affiliation": "NTU", "city": "Singapore", "country": "Singapore"}, {"affiliation": "SUFE", "city": "Shanghai", "country": "China"}, {"affiliation": "NTU", "city": "Singapore", "country": "Singapore"}, {"affiliation": "BIT", "city": "Beijing", "country": "China"}, {"affiliation": "SMU", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Institute of Semiconductors, CAS", "city": "Beijing", "country": "China"}, {"affiliation": "Institute of Semiconductors, CAS", "city": "Beijing", "country": "China"}, {"affiliation": "Halmstad University", "city": "Halmstad", "country": "Sweden"}]

affiliations:
["NTU", "SUFE", "NTU", "BIT", "SMU", "Institute of Semiconductors, CAS", "Institute of Semiconductors, CAS", "Halmstad University"]


title: Faster Parameter-Efficient Tuning with Token Redundancy Reduction
authors: Kwonyoung Kim, Jungin Park, Jin Kim, Hyeongjun Kwon, Kwanghoon Sohn
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30189-30198
affiliations: ["Yonsei University", "Korea Institute of Science and Technology (KIST)"]
cities: ["Seoul", "Seoul"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Faster_Parameter-Efficient_Tuning_with_Token_Redundancy_Reduction_CVPR_2025_paper.html

abstract:
Parameter-efficient tuning (PET) aims to transfer pre-trained foundation models to downstream tasks by learning a small number of parameters. Compared to traditional fine-tuning, which updates the entire model, PET significantly reduces storage and transfer costs for each task regardless of exponentially increasing pre-trained model capacity. However, most PET methods inherit the inference latency of their large backbone models and often introduce additional computational overhead due to additional modules (e.g. adapters), limiting their practicality for compute-intensive applications. In this paper, we propose Faster Parameter-Efficient Tuning (FPET), a novel approach that enhances inference speed and training efficiency while maintaining high storage efficiency. Specifically, we introduce a plug-and-play token redundancy reduction module delicately designed for PET. This module refines tokens from the self-attention layer using an adapter to learn the accurate similarity between tokens and cuts off the tokens through a fully-differentiable token merging strategy, which uses a straight-through estimator for optimal token reduction. Experimental results prove that our FPET achieves faster inference and higher memory efficiency than the pre-trained backbone while keeping competitive performance on par with state-of-the-art PET methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Faster_Parameter-Efficient_Tuning_with_Token_Redundancy_Reduction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_Faster_Parameter-Efficient_Tuning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Kwonyoung and Park, Jungin and Kim, Jin and Kwon, Hyeongjun and Sohn, Kwanghoon},
    title     = {Faster Parameter-Efficient Tuning with Token Redundancy Reduction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30189-30198}
}

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


authors: Hui Zhang, Tingwei Gao, Jie Shao, Zuxuan Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12891-12900
cities: ["Shanghai", "Shanghai", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_BlockDance_Reuse_Structurally_Similar_Spatio-Temporal_Features_to_Accelerate_Diffusion_Transformers_CVPR_2025_paper.html

title:
BlockDance: Reuse Structurally Similar Spatio-Temporal Features to Accelerate Diffusion Transformers

abstract:
Diffusion models have demonstrated impressive generation capabilities, particularly with recent advancements leveraging transformer architectures to improve both visual and artistic quality. However, Diffusion Transformers (DiTs) continue to encounter challenges related to low inference speed, primarily due to the iterative denoising process. To address this issue, we propose BlockDance, a training-free approach that explores feature similarities at adjacent time steps to accelerate DiTs. Unlike previous feature-reuse methods that lack tailored reuse strategies for features at different scales, BlockDance prioritizes the identification of the most structurally similar features, referred to as Structurally Similar Spatio-Temporal (STSS) features. These features are primarily located within the structure-focused blocks of the transformer during the later stages of denoising. BlockDance caches and reuses these highly similar features to mitigate redundant computation, thereby accelerating DiTs while maximizing consistency with the generated results of the original model. Furthermore, considering the diversity of generated content and the varying distributions of redundant features, we introduce BlockDance-Ada, a lightweight decision-making network tailored for instance-specific acceleration. BlockDance-Ada dynamically allocates resources and provides superior content quality. Both BlockDance and BlockDance-Ada have proven effective across various generation tasks and models, achieving accelerations between 25% and 50% while maintaining generation quality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_BlockDance_Reuse_Structurally_Similar_Spatio-Temporal_Features_to_Accelerate_Diffusion_Transformers_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_BlockDance_Reuse_Structurally_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Hui and Gao, Tingwei and Shao, Jie and Wu, Zuxuan},
    title     = {BlockDance: Reuse Structurally Similar Spatio-Temporal Features to Accelerate Diffusion Transformers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12891-12900}
}

raw_affiliation:
[{"affiliation": "Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Collaborative Innovation Center of Intelligent Visual Computing", "city": "Shanghai", "country": "China"}, {"affiliation": "ByteDance Intelligent Creation", "city": "Beijing", "country": "China"}]

affiliations:
["Shanghai Key Lab of Intell. Info. Processing, School of CS, Fudan University", "Shanghai Collaborative Innovation Center of Intelligent Visual Computing", "ByteDance Intelligent Creation"]


title: Panorama Generation From NFoV Image Done Right
authors: Dian Zheng, Cheng Zhang, Xiao-Ming Wu, Cao Li, Chengfei Lv, Jian-Fang Hu, Wei-Shi Zheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21610-21619
cities: ["Guangzhou", "Melbourne", "Hangzhou", "Guangzhou"]
countries: ["China", "Australia", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zheng_Panorama_Generation_From_NFoV_Image_Done_Right_CVPR_2025_paper.html

abstract:
Generating 360-degree panoramas from narrow field of view (NFoV) image is a promising computer vision task for Virtual Reality (VR) applications. Existing methods mostly assess the generated panoramas with InceptionNet or CLIP based metrics, which tend to perceive the image quality and is not suitable for evaluating the distortion. In this work, we first propose a distortion-specific CLIP, named Distort-CLIP to accurately evaluate the panorama distortion and discover the "visual cheating" phenomenon in previous works (i.e., tending to improve the visual results by sacrificing distortion accuracy). This phenomenon arises because prior methods employ a single network to learn the distinct panorama distortion and content completion at once, which leads the model to prioritize optimizing the latter. To address the phenomenon, we propose PanoDecouple, a decoupled diffusion model framework, which decouples the panorama generation into distortion guidance and content completion, aiming to generate panoramas with both accurate distortion and visual appeal. Specifically, we design a DistortNet for distortion guidance by imposing panorama-specific distortion prior and a modified condition registration mechanism; and a ContentNet for content completion by imposing perspective image information. Additionally, a distortion correction loss function with Distort-CLIP is introduced to constrain the distortion explicitly. The extensive experiments validate that PanoDecouple surpasses existing methods both in distortion and visual metrics.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zheng_Panorama_Generation_From_NFoV_Image_Done_Right_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zheng_Panorama_Generation_From_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zheng_2025_CVPR,
    author    = {Zheng, Dian and Zhang, Cheng and Wu, Xiao-Ming and Li, Cao and Lv, Chengfei and Hu, Jian-Fang and Zheng, Wei-Shi},
    title     = {Panorama Generation From NFoV Image Done Right},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21610-21619}
}

raw_affiliation:
[{"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Monash University", "city": "Melbourne", "country": "Australia"}, {"affiliation": "Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China", "city": "Guangzhou", "country": "China"}]

affiliations:
["Sun Yat-sen University", "Monash University", "Alibaba Group", "Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China"]


title: Mamba-Adaptor: State Space Model Adaptor for Visual Recognition
authors: Fei Xie, Jiahao Nie, Yujin Tang, Wenkang Zhang, Hongshen Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20124-20134
affiliations: ["Shanghai Jiao Tong University", "Hangzhou Dianzi University", "Southeast University"]
cities: ["Shanghai", "Hangzhou", "Nanjing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_Mamba-Adaptor_State_Space_Model_Adaptor_for_Visual_Recognition_CVPR_2025_paper.html

abstract:
Recent State Space Models (SSM), especially Mamba, have demonstrated impressive performance in visual modeling and possess superior model efficiency. However, the application of Mamba to visual tasks suffers inferior performance due to three main constraints existing in the sequential model: 1) Casual computing is incapable of accessing global context; 2) Long-range forgetting occurs when computing the current hidden states; 3) Weak spatial structural modeling due to the transformed sequential input. To address these issues, we investigate a simple yet powerful vision task adapter for Mamba models, which consists of two functional modules: Adaptor-T and Adapator-S. When solving the hidden states for SSM, we apply a casual prediction module Adaptor-T to select a set of learnable locations as memory augmentation feature states to ease long-range forgetting issues. Moreover, we leverage Adapator-S, composed of multi-scale dilated convolutional kernels, to enhance the spatial modeling and introduce the image inductive bias into the feature output. Both modules can enlarge the context modeling in casual computing, as the output is enhanced by the inaccessible features. We explore three usages of Mamba-Adaptor: A general visual backbone for various vision tasks; A booster module to raise the performance of pretrained backbones; A highly efficient fine-tuning module that adapts the base model for transfer learning tasks. Extensive experiments verify the effectiveness of Mamba-Adapter in three settings. Notably, our Mamba-Adapter achieves state-of-the-art performance on the ImageNet and COCO benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_Mamba-Adaptor_State_Space_Model_Adaptor_for_Visual_Recognition_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_Mamba-Adaptor_State_Space_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Fei and Nie, Jiahao and Tang, Yujin and Zhang, Wenkang and Zhao, Hongshen},
    title     = {Mamba-Adaptor: State Space Model Adaptor for Visual Recognition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20124-20134}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Hangzhou Dianzi University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Southeast University", "city": "Nanjing", "country": "China"}]


title: Robust Message Embedding via Attention Flow-Based Steganography
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12840-12849
cities: ["Shanghai", "Hong Kong", "Chengdu", "Zhejiang"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ye_Robust_Message_Embedding_via_Attention_Flow-Based_Steganography_CVPR_2025_paper.html

authors:
Huayuan Ye, Shenzhuo Zhang, Shiqi Jiang, Jing Liao, Shuhang Gu, Dejun Zheng, Changbo Wang, Chenhui Li

abstract:
Image steganography can hide information in a host image and obtain a stego image that is perceptually indistinguishable from the original one. This technique has tremendous potential in scenarios like copyright protection and information retrospection. Some previous studies have proposed to enhance the robustness of the methods against image disturbances to increase their applicability. However, they generally cannot achieve a satisfying balance between the steganography quality and robustness. Instead of image-in-image steganography, we focus on the issue of message-in-image embedding that is robust to various real-world image distortions. This task aims to embed information into a natural image and the decoding result is required to be completely accurate, which increases the difficulty of data concealing and revealing. Inspired by the recent developments in transformer-based vision models, we discover that the tokenized representation of image is naturally suitable for steganography task. In this paper, we propose a novel message embedding framework, called Robust Message Steganography (RMSteg), which is competent to hide message via QR Code in a host image based on an normalizing flow-based model. The stego image derived by our method has imperceptible changes and the encoded message can be accurately restored even if the image is printed out and photographed. To our best knowledge, this is the first work that integrates the advantages of transformer models into normalizing flow. The code is available at https://github.com/huayuan4396/RMSteg.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ye_Robust_Message_Embedding_via_Attention_Flow-Based_Steganography_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ye_Robust_Message_Embedding_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ye_2025_CVPR,
    author    = {Ye, Huayuan and Zhang, Shenzhuo and Jiang, Shiqi and Liao, Jing and Gu, Shuhang and Zheng, Dejun and Wang, Changbo and Li, Chenhui},
    title     = {Robust Message Embedding via Attention Flow-Based Steganography},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12840-12849}
}

raw_affiliation:
[{"affiliation": "East China Normal University", "city": "Shanghai", "country": "China"}, {"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "University of Electronic Science and Technology of China", "city": "Chengdu", "country": "China"}, {"affiliation": "Zhijiang College of Zhejiang Univeristy of Technology", "city": "Zhejiang", "country": "China"}]

affiliations:
["East China Normal University", "City University of Hong Kong", "University of Electronic Science and Technology of China", "Zhijiang College of Zhejiang Univeristy of Technology"]


title: Task-driven Image Fusion with Learnable Fusion Loss
authors: Haowen Bai, Jiangshe Zhang, Zixiang Zhao, Yichen Wu, Lilun Deng, Yukun Cui, Tao Feng, Shuang Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7457-7468
cities: ["Xi'an", "Zurich", "Hong Kong", "Beijing", "Xi'an"]
countries: ["China", "Switzerland", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bai_Task-driven_Image_Fusion_with_Learnable_Fusion_Loss_CVPR_2025_paper.html

abstract:
Multi-modal image fusion aggregates information from multiple sensor sources, achieving superior visual quality and perceptual features compared to single-source images, often improving downstream tasks. However, current fusion methods for downstream tasks still use predefined fusion objectives that potentially mismatch the downstream tasks, limiting adaptive guidance and reducing model flexibility. To address this, we propose Task-driven Image Fusion (TDFusion), a fusion framework incorporating a learnable fusion loss guided by task loss. Specifically, our fusion loss includes learnable parameters modeled by a neural network called the loss generation module. This module is supervised by the downstream task loss in a meta-learning manner. The learning objective is to minimize the task loss of fused images after optimizing the fusion module with the fusion loss. Iterative updates between the fusion module and the loss module ensure that the fusion network evolves toward minimizing task loss, guiding the fusion process toward the task objectives. TDFusion's training relies entirely on the downstream task loss, making it adaptable to any specific task. It can be applied to any architecture of fusion and task networks. Experiments demonstrate TDFusion's performance through fusion experiments conducted on four different datasets, in addition to evaluations on semantic segmentation and object detection tasks. The code is available at https://github.com/HaowenBai/TDFusion.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bai_Task-driven_Image_Fusion_with_Learnable_Fusion_Loss_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bai_Task-driven_Image_Fusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bai_2025_CVPR,
    author    = {Bai, Haowen and Zhang, Jiangshe and Zhao, Zixiang and Wu, Yichen and Deng, Lilun and Cui, Yukun and Feng, Tao and Xu, Shuang},
    title     = {Task-driven Image Fusion with Learnable Fusion Loss},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7457-7468}
}

raw_affiliation:
[{"affiliation": "Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "ETH Z\u00a8urich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}]

affiliations:
["Xi\u2019an Jiaotong University", "ETH Z\u00a8urich", "City University of Hong Kong", "Tsinghua University", "Northwestern Polytechnical University"]


title: Compositional Targeted Multi-Label Universal Perturbations
authors: Hassan Mahmood, Ehsan Elhamifar
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20580-20591
affiliations: ["Northeastern University", "Northeastern University"]
cities: ["Boston", "Boston"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mahmood_Compositional_Targeted_Multi-Label_Universal_Perturbations_CVPR_2025_paper.html

abstract:
Generating targeted universal perturbations for multi-label recognition is a combinatorially hard problem that requires exponential time and space complexity. To address the problem, we propose a compositional framework. We show that a simple independence assumption on label-wise universal perturbations naturally leads to an efficient optimization that requires learning affine convex cones spanned by label-wise universal perturbations, significantly reducing the problem complexity to linear time and space. During inference, the framework allows generating universal perturbations for novel combinations of classes in constant time. We demonstrate the scalability of our method on large datasets and target sizes, evaluating its performance on NUS-WIDE, MS-COCO, and OpenImages using state-of-the-art multi-label recognition models. Our results show that our approach outperforms baselines and achieves results comparable to methods with exponential complexity.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mahmood_Compositional_Targeted_Multi-Label_Universal_Perturbations_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mahmood_Compositional_Targeted_Multi-Label_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mahmood_2025_CVPR,
    author    = {Mahmood, Hassan and Elhamifar, Ehsan},
    title     = {Compositional Targeted Multi-Label Universal Perturbations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20580-20591}
}

raw_affiliation:
[{"affiliation": "Northeastern University", "city": "Boston", "country": "USA"}, {"affiliation": "Northeastern University", "city": "Boston", "country": "USA"}]


authors: Mojtaba Nafez, Amirhossein Koochakian, Arad Maleki, Jafar Habibi, Mohammad Hossein Rohban
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20383-20394
raw_affiliation: [{"affiliation": "Sharif University of Technology, Iran", "city": "Tehran", "country": "Iran"}]
affiliations: ["Sharif University of Technology, Iran"]
cities: ["Tehran"]
countries: ["Iran"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nafez_PatchGuard_Adversarially_Robust_Anomaly_Detection_and_Localization_through_Vision_Transformers_CVPR_2025_paper.html

title:
PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomalies

abstract:
Anomaly Detection (AD) and Anomaly Localization (AL) are crucial in fields that demand high reliability, such as medical imaging and industrial monitoring. However, current AD and AL approaches are often susceptible to adversarial attacks due to limitations in training data, which typically include only normal, unlabeled samples. This study introduces PatchGuard, an adversarially robust AD and AL method that incorporates pseudo anomalies with localization masks within a Vision Transformer (ViT)-based architecture to address these vulnerabilities.We begin by examining the essential properties of pseudo anomalies, and follow it by providing theoretical insights into the attention mechanisms required to enhance the adversarial robustness of AD and AL systems. We then present our approach, which leverages Foreground-Aware Pseudo-Anomalies to overcome the deficiencies of previous anomaly-aware methods. Our method incorporates these crafted pseudo-anomaly samples into a ViT-based framework, with adversarial training guided by a novel loss function designed to improve model robustness, as supported by our theoretical analysis.Experimental results on well-established industrial and medical datasets demonstrate that PatchGuard significantly outperforms previous methods in adversarial settings, achieving performance gains of 53.2% in AD and 68.5% in AL, while also maintaining competitive accuracy in non-adversarial settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nafez_PatchGuard_Adversarially_Robust_Anomaly_Detection_and_Localization_through_Vision_Transformers_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nafez_PatchGuard_Adversarially_Robust_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nafez_2025_CVPR,
    author    = {Nafez, Mojtaba and Koochakian, Amirhossein and Maleki, Arad and Habibi, Jafar and Rohban, Mohammad Hossein},
    title     = {PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomalies},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20383-20394}
}


title: Sparse Point Cloud Patches Rendering via Splitting 2D Gaussians
authors: Changfeng Ma, Ran Bi, Jie Guo, Chongjun Wang, Yanwen Guo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27285-27294
affiliations: ["Nanjing University", "School of Software, North University of China"]
cities: ["Nanjing", "Unknown"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ma_Sparse_Point_Cloud_Patches_Rendering_via_Splitting_2D_Gaussians_CVPR_2025_paper.html

abstract:
Current learning-based methods predict NeRF or 3D Gaussians from point clouds to achieve photo-realistic rendering but still depend on categorical priors, dense point clouds, or additional refinements. Hence, we introduce a novel point cloud rendering method by predicting 2D Gaussians from point clouds. Our method incorporates two identical modules with an entire-patch architecture enabling the network to be generalized to multiple datasets. The module normalizes and initializes the Gaussians utilizing the point cloud information including normals, colors and distances. Then, splitting decoders are employed to refine the initial Gaussians by duplicating them and predicting more accurate results, making our methodology effectively accommodate sparse point clouds as well. Once trained, our approach exhibits direct generalization to point clouds across different categories. The predicted Gaussians are employed directly for rendering without additional refinement on the rendered images, retaining the benefits of 2D Gaussians. We conduct extensive experiments on various datasets, and the results demonstrate the superiority and generalization of our method, which achieves SOTA performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_Sparse_Point_Cloud_Patches_Rendering_via_Splitting_2D_Gaussians_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ma_Sparse_Point_Cloud_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Ma_2025_CVPR,
    author    = {Ma, Changfeng and Bi, Ran and Guo, Jie and Wang, Chongjun and Guo, Yanwen},
    title     = {Sparse Point Cloud Patches Rendering via Splitting 2D Gaussians},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27285-27294}
}

raw_affiliation:
[{"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "School of Software, North University of China", "city": "Unknown", "country": "China"}]


title: Distilling Monocular Foundation Model for Fine-grained Depth Completion
authors: Yingping Liang, Yutao Hu, Wenqi Shao, Ying Fu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22254-22265
cities: ["Beijing", "Nanjing", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liang_Distilling_Monocular_Foundation_Model_for_Fine-grained_Depth_Completion_CVPR_2025_paper.html

abstract:
Depth completion involves predicting dense depth maps from sparse LiDAR inputs, a critical task for applications such as autonomous driving and robotics. However, sparse depth annotations from sensors limit the availability of dense supervision, which is necessary for learning detailed geometric features. To overcome this limitation, we propose a two-stage knowledge distillation framework that leverages powerful monocular foundation models to provide dense supervision for depth completion. In the first stage, we introduce a pre-training strategy that generates diverse training data from natural images to distill geometric knowledge to depth completion. Specifically, we simulate LiDAR scans by utilizing monocular depth and mesh reconstruction, thereby creating training data without requiring ground-truth depth. Nonetheless, monocular depth estimation suffers from inherent scale ambiguity in real-world settings. To address this, in the second stage, we employ a scale- and shift-invariant loss (SSI Loss) to learn real-world scales when fine-tuning on real-world datasets. Our two-stage distillation framework enables depth completion models to harness the strengths of monocular foundation models. Experimental results show that models trained with our two-stage distillation framework achieve top-ranked performance on the KITTI benchmark, demonstrating improvements in both quantitative and qualitative metrics.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liang_Distilling_Monocular_Foundation_Model_for_Fine-grained_Depth_Completion_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Liang_2025_CVPR,
    author    = {Liang, Yingping and Hu, Yutao and Shao, Wenqi and Fu, Ying},
    title     = {Distilling Monocular Foundation Model for Fine-grained Depth Completion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22254-22265}
}

raw_affiliation:
[{"affiliation": "Beijing Institute of Technology", "city": "Beijing", "country": "China"}, {"affiliation": "Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Southeast University", "city": "Nanjing", "country": "China"}, {"affiliation": "Shanghai Al Laboratory", "city": "Shanghai", "country": "China"}]

affiliations:
["Beijing Institute of Technology", "Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications, Southeast University", "Shanghai Al Laboratory"]


title: LamRA: Large Multimodal Model as Your Advanced Retrieval Assistant
authors: Yikun Liu, Yajie Zhang, Jiayin Cai, Xiaolong Jiang, Yao Hu, Jiangchao Yao, Yanfeng Wang, Weidi Xie
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4015-4025
cities: ["Shanghai", "Shanghai", "China"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_LamRA_Large_Multimodal_Model_as_Your_Advanced_Retrieval_Assistant_CVPR_2025_paper.html

abstract:
With the rapid advancement of multimodal information retrieval, increasingly complex retrieval tasks have emerged. Existing methods predominately rely on task-specific fine-tuning of vision-language models, often those trained with image-text contrastive learning. In this paper, we explore the possibility of re-purposing generative Large Multimodal Models (LMMs) for retrieval. This approach enables unifying all retrieval tasks under the same formulation and, more importantly, allows for extrapolation towards unseen retrieval tasks without additional training. Our contributions can be summarised in the following aspects: (i) We introduce LamRA, a versatile framework designed to empower LMMs with sophisticated retrieval and reranking capabilities. (ii) For retrieval, we adopt a two-stage training strategy comprising language-only pre-training and multimodal instruction tuning to progressively enhance LMM's retrieval performance. (iii) For reranking, we employ joint training of both pointwise and listwise reranking, offering two distinct ways to further boost the retrieval performance. (iv) Extensive experiments underscore the efficacy of our method in handling more than ten retrieval tasks, demonstrating robust performance in both supervised and zero-shot settings, including scenarios involving previously unseen retrieval tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_LamRA_Large_Multimodal_Model_as_Your_Advanced_Retrieval_Assistant_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_LamRA_Large_Multimodal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yikun and Zhang, Yajie and Cai, Jiayin and Jiang, Xiaolong and Hu, Yao and Yao, Jiangchao and Wang, Yanfeng and Xie, Weidi},
    title     = {LamRA: Large Multimodal Model as Your Advanced Retrieval Assistant},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4015-4025}
}

raw_affiliation:
[{"affiliation": "School of Artificial Intelligence, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "CMIC, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Xiaohongshu Inc.", "city": "China", "country": "China"}]

affiliations:
["School of Artificial Intelligence, Shanghai Jiao Tong University", "CMIC, Shanghai Jiao Tong University", "Xiaohongshu Inc."]


title: AniGrad: Anisotropic Gradient-Adaptive Sampling for 3D Reconstruction From Monocular Video
authors: Noah Stier, Alex Rich, Pradeep Sen, Tobias Höllerer
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21814-21823
affiliations: ["University of California, Santa Barbara"]
cities: ["Santa Barbara"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Stier_AniGrad_Anisotropic_Gradient-Adaptive_Sampling_for_3D_Reconstruction_From_Monocular_Video_CVPR_2025_paper.html

abstract:
Recent image-based 3D reconstruction methods have achieved excellent quality for indoor scenes using 3D convolutional neural networks. However, they rely on a high-resolution grid in order to achieve detailed output surfaces, which is quite costly in terms of compute time, and it results in large mesh sizes that are more expensive to store, transmit, and render. In this paper we propose a new solution to this problem, using adaptive sampling. By re-formulating the final layers of the network, we are able to analytically bound the local surface complexity, and set the local sample rate accordingly. Our method, AniGrad, achieves an order of magnitude reduction in both surface extraction latency and mesh size, while preserving mesh accuracy and detail.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Stier_AniGrad_Anisotropic_Gradient-Adaptive_Sampling_for_3D_Reconstruction_From_Monocular_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Stier_AniGrad_Anisotropic_Gradient-Adaptive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Stier_2025_CVPR,
    author    = {Stier, Noah and Rich, Alex and Sen, Pradeep and H\"ollerer, Tobias},
    title     = {AniGrad: Anisotropic Gradient-Adaptive Sampling for 3D Reconstruction From Monocular Video},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21814-21823}
}

raw_affiliation:
[{"affiliation": "University of California, Santa Barbara", "city": "Santa Barbara", "country": "USA"}]


title: Neural Video Compression with Context Modulation
authors: Chuanbo Tang, Zhuoyuan Li, Yifan Bian, Li Li, Dong Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12553-12563
cities: ["Hefei", "Hefei"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_Neural_Video_Compression_with_Context_Modulation_CVPR_2025_paper.html

abstract:
Efficient video coding is highly dependent on exploiting the temporal redundancy, which is usually achieved by extracting and leveraging the temporal context in the emerging conditional coding-based neural video codec (NVC). Although the latest NVC has achieved remarkable progress in improving the compression performance, the inherent temporal context propagation mechanism lacks the ability to sufficiently leverage the reference information, limiting further improvement. In this paper, we address the limitation by modulating the temporal context with the reference frame in two steps. Specifically, we first propose the flow orientation to mine the inter-correlation between the reference frame and prediction frame for generating the additional oriented temporal context. Moreover, we introduce the context compensation to leverage the oriented context to modulate the propagated temporal context generated from the propagated reference feature. Through the synergy mechanism and decoupling loss supervision, the irrelevant propagated information can be effectively eliminated to ensure better context modeling. Experimental results demonstrate that our codec achieves on average 22.7% bitrate reduction over the advanced traditional video codec H.266/VVC, and offers an average 10.1% bitrate saving over the previous state-of-the-art NVC DCVC-FM. The code is available at https://github.com/Austin4USTC/DCMVC.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_Neural_Video_Compression_with_Context_Modulation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tang_Neural_Video_Compression_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Chuanbo and Li, Zhuoyuan and Bian, Yifan and Li, Li and Liu, Dong},
    title     = {Neural Video Compression with Context Modulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12553-12563}
}

raw_affiliation:
[{"affiliation": "MOE Key Laboratory of Brain-Inspired Intelligent Perception and Cognition", "city": "Hefei", "country": "China"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}]

affiliations:
["MOE Key Laboratory of Brain-Inspired Intelligent Perception and Cognition", "University of Science and Technology of China"]


title: Less Attention is More: Prompt Transformer for Generalized Category Discovery
authors: Wei Zhang, Baopeng Zhang, Zhu Teng, Wenxin Luo, Junnan Zou, Jianping Fan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30322-30331
cities: ["Beijing", "Unknown"]
countries: ["China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Less_Attention_is_More_Prompt_Transformer_for_Generalized_Category_Discovery_CVPR_2025_paper.html

abstract:
Generalized Category Discovery (GCD) typically relies on the pre-trained Vision Transformer (ViT) to extract features from a global receptive field, followed by contrastive learning to simultaneously classify unlabeled known classes and unknown classes without priors. Owing to the deficiency in the modeling capacity for inner-patch local information within ViT, current methods primarily focus on discriminative features at global level. This results in a model with more yet scattered attention, where neither excessive nor insufficient focus can grasp subtle differences to classify fine-grained unknown and known categories. To address this issue, we propose the AptGCD to deliver apt attention for GCD. It mimics the human brain how leveraging visual perception to refine local attention and comprehend global context by proposing a Meta Visual Prompt (MVP) and Prompt Transformer (PT). MVP is introduced into GCD for the first time, refining channel-level attention, while adaptively self-learning unique inner-patch features as prompts to achieve local visual modeling for our prompt transformer. Yet, relying solely on detailed features can lead to skewed judgments. Hence, PT harmonizes local and global representations, guiding the model's interpretation of features through broader contexts, thereby capturing more useful details with less attention. Extensive experiments on seven datasets demonstrate that AptGCD outperforms current methods, it achieves an average proportional 'New' accuracy improvement of approximately 9.2% over SOTA method on the all four fine-grained datasets, establishing a new standard in the field. The code is available at https://github.com/wendy26zhang/AptGCD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Less_Attention_is_More_Prompt_Transformer_for_Generalized_Category_Discovery_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Wei and Zhang, Baopeng and Teng, Zhu and Luo, Wenxin and Zou, Junnan and Fan, Jianping},
    title     = {Less Attention is More: Prompt Transformer for Generalized Category Discovery},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30322-30331}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology, Beijing Jiaotong University", "city": "Beijing", "country": "China"}, {"affiliation": "AI Lab at Lenovo Research", "city": "Unknown", "country": "Unknown"}]

affiliations:
["School of Computer Science and Technology, Beijing Jiaotong University", "AI Lab at Lenovo Research"]


title: On-Device Self-Supervised Learning of Low-Latency Monocular Depth from Only Events
authors: Jesse J. Hagenaars, Yilun Wu, Federico Paredes-Valles, Stein Stroobants, Guido C.H.E. de Croon
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17114-17123
affiliations: ["MA VLab, TU Delft", "EUISPC, Sony Semiconductor Solutions Europe, Sony Europe B.V"]
cities: ["Delft", "Europe"]
countries: ["Netherlands", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hagenaars_On-Device_Self-Supervised_Learning_of_Low-Latency_Monocular_Depth_from_Only_Events_CVPR_2025_paper.html

abstract:
Event cameras provide low-latency perception for only milliwatts of power. This makes them highly suitable for resource-restricted, agile robots such as small flying drones. Self-supervised learning based on contrast maximization holds great potential for event-based robot vision, as it foregoes the need for high-frequency ground truth and allows for online learning in the robot's operational environment. However, online, on-board learning raises the major challenge of achieving sufficient computational efficiency for real-time learning, while maintaining competitive visual perception performance. In this work, we improve the time and memory efficiency of the contrast maximization pipeline, making on-device learning of low-latency monocular depth possible. We demonstrate that online learning on board a small drone yields more accurate depth estimates and more successful obstacle avoidance behavior compared to only pre-training. Benchmarking experiments show that the proposed pipeline is not only efficient, but also achieves state-of-the-art depth estimation performance among self-supervised approaches. Our work taps into the unused potential of online, on-device robot learning, promising smaller reality gaps and better performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hagenaars_On-Device_Self-Supervised_Learning_of_Low-Latency_Monocular_Depth_from_Only_Events_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hagenaars_On-Device_Self-Supervised_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hagenaars_2025_CVPR,
    author    = {Hagenaars, Jesse J. and Wu, Yilun and Paredes-Valles, Federico and Stroobants, Stein and de Croon, Guido C.H.E.},
    title     = {On-Device Self-Supervised Learning of Low-Latency Monocular Depth from Only Events},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17114-17123}
}

raw_affiliation:
[{"affiliation": "MA VLab, TU Delft", "city": "Delft", "country": "Netherlands"}, {"affiliation": "EUISPC, Sony Semiconductor Solutions Europe, Sony Europe B.V", "city": "Europe", "country": "Unknown"}]


title: CoMM: A Coherent Interleaved Image-Text Dataset for Multimodal Understanding and Generation
authors: Wei Chen, Lin Li, Yongqi Yang, Bin Wen, Fan Yang, Tingting Gao, Yu Wu, Long Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8073-8082
cities: ["Hong Kong", "Wuhan", "Hong Kong", "Beijing"]
countries: ["China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_CoMM_A_Coherent_Interleaved_Image-Text_Dataset_for_Multimodal_Understanding_and_CVPR_2025_paper.html

abstract:
Interleaved image-text generation has emerged as a crucial multimodal task, aiming at creating sequences of interleaved visual and textual content given a query. Despite notable advancements in recent multimodal large language models (MLLMs), generating integrated image-text sequences that exhibit narrative coherence and entity and style consistency remains challenging due to poor training data quality. To address this gap, we introduce CoMM, a high-quality Coherent interleaved image-text MultiModal dataset designed to enhance the coherence, consistency, and alignment of generated multimodal content. Initially, CoMM harnesses raw data from diverse sources, focusing on instructional content and visual storytelling, establishing a foundation for coherent and consistent content. To further refine the data quality, we devise a multi-perspective filter strategy that leverages advanced pre-trained models to ensure the development of sentences, consistency of inserted images, and semantic alignment between them. Various quality evaluation metrics are designed to prove the high quality of the filtered dataset. Meanwhile, extensive few-shot experiments on various downstream tasks demonstrate CoMM's effectiveness in significantly enhancing the in-context learning capabilities of MLLMs. Moreover, we propose four new tasks to evaluate MLLMs' interleaved generation abilities, supported by a comprehensive evaluation framework. We believe CoMM opens a new avenue for advanced MLLMs with superior multimodal in-context learning and understanding ability. The dataset and codes will be released.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_CoMM_A_Coherent_Interleaved_Image-Text_Dataset_for_Multimodal_Understanding_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_CoMM_A_Coherent_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Wei and Li, Lin and Yang, Yongqi and Wen, Bin and Yang, Fan and Gao, Tingting and Wu, Yu and Chen, Long},
    title     = {CoMM: A Coherent Interleaved Image-Text Dataset for Multimodal Understanding and Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8073-8082}
}

raw_affiliation:
[{"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "AI Chip Center for Emerging Smart Systems", "city": "Hong Kong", "country": "China"}, {"affiliation": "Kuaishou Technology", "city": "Beijing", "country": "China"}]

affiliations:
["The Hong Kong University of Science and Technology", "Wuhan University", "AI Chip Center for Emerging Smart Systems", "Kuaishou Technology"]


authors: Ruicheng Wang, Sicheng Xu, Cassie Dai, Jianfeng Xiang, Yu Deng, Xin Tong, Jiaolong Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5261-5271
affiliations: ["USTC", "Microsoft Research", "Harvard", "Tsinghua University"]
cities: ["Hefei", "Redmond", "Cambridge", "Beijing"]
countries: ["China", "USA", "USA", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_MoGe_Unlocking_Accurate_Monocular_Geometry_Estimation_for_Open-Domain_Images_with_CVPR_2025_paper.html

title:
MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervision

abstract:
We present MoGe, a powerful model for recovering 3D geometry from monocular open-domain images. Given a single image, our model directly predicts a 3D point map of the captured scene with an affine-invariant representation, which is agnostic to true global scale and shift. This new representation precludes ambiguous supervision in training and facilitates effective geometry learning. Furthermore, we propose a set of novel global and local geometry supervision techniques that empower the model to learn high-quality geometry. These include a robust, optimal, and efficient point cloud alignment solver for accurate global shape learning, and a multi-scale local geometry loss promoting precise local geometry supervision. We train our model on a large, mixed dataset and demonstrate its strong generalizability and high accuracy. In our comprehensive evaluation on diverse unseen datasets, our model significantly outperforms state-of-the-art methods across all tasks, including monocular estimation of 3D point map, depth map, and camera field of view.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_MoGe_Unlocking_Accurate_Monocular_Geometry_Estimation_for_Open-Domain_Images_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_MoGe_Unlocking_Accurate_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Ruicheng and Xu, Sicheng and Dai, Cassie and Xiang, Jianfeng and Deng, Yu and Tong, Xin and Yang, Jiaolong},
    title     = {MoGe: Unlocking Accurate Monocular Geometry Estimation for Open-Domain Images with Optimal Training Supervision},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5261-5271}
}

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


title: Beyond Background Shift: Rethinking Instance Replay in Continual Semantic Segmentation
authors: Hongmei Yin, Tingliang Feng, Fan Lyu, Fanhua Shang, Hongying Liu, Wei Feng, Liang Wan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9839-9848
cities: ["Tianjin", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yin_Beyond_Background_Shift_Rethinking_Instance_Replay_in_Continual_Semantic_Segmentation_CVPR_2025_paper.html

abstract:
In this work, we focus on continual semantic segmentation (CSS), where segmentation networks are required to continuously learn new classes without erasing knowledge of previously learned ones. Although storing images of old classes and directly incorporating them into the training of new models has proven effective in mitigating catastrophic forgetting in classification tasks, this strategy presents notable limitations in CSS. Specifically, the stored and new images with partial category annotations leads to confusion between unannotated categories and the background, complicating model fitting. To tackle this issue, this paper proposes a novel Enhanced Instance Replay (EIR) method, which not only preserves knowledge of old classes while simultaneously eliminating background confusion by instance storage of old classes, but also mitigates background shifts in the new images by integrating stored instances with new images. By effectively resolving background shifts in both stored and new images, EIR alleviates catastrophic forgetting in the CSS task, thereby enhancing the model's capacity for CSS. Experimental results validate the efficacy of our approach, which significantly outperforms state-of-the-art CSS methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yin_Beyond_Background_Shift_Rethinking_Instance_Replay_in_Continual_Semantic_Segmentation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Yin_2025_CVPR,
    author    = {Yin, Hongmei and Feng, Tingliang and Lyu, Fan and Shang, Fanhua and Liu, Hongying and Feng, Wei and Wan, Liang},
    title     = {Beyond Background Shift: Rethinking Instance Replay in Continual Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9839-9848}
}

raw_affiliation:
[{"affiliation": "College of Intelligence and Computing, Tianjin University", "city": "Tianjin", "country": "China"}, {"affiliation": "New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["College of Intelligence and Computing, Tianjin University", "New Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences"]


title: ScaleLSD: Scalable Deep Line Segment Detection Streamlined
authors: Zeran Ke, Bin Tan, Xianwei Zheng, Yujun Shen, Tianfu Wu, Nan Xue
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6327-6336
cities: ["Wuhan", "Unknown", "Wuhan", "Raleigh"]
countries: ["China", "Unknown", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ke_ScaleLSD_Scalable_Deep_Line_Segment_Detection_Streamlined_CVPR_2025_paper.html

abstract:
This paper studies the problem of Line Segment Detection (LSD) for the characterization of line geometry in images, with the aim of learning a domain-agnostic robust LSD model that works well for any natural images. With the focus of scalable self-supervised learning of LSD, we revisit and streamline the fundamental designs of (deep and non-deep) LSD approaches to have a high-performing and efficient LSD learner, dubbed as ScaleLSD, for the curation of line geometry at scale from over 10M unlabeled real-world images. Our ScaleLSD works very well to detect much more number of line segments from any natural images even than the pioneered non-deep LSD approach, having a more complete and accurate geometric characterization of images using line segments. Experimentally, our proposed ScaleLSD is comprehensively testified under the zero-shot protocol in detection performance, single-view 3D geometry estimation, two-view line segment matching, and multiview 3D line mapping, all with excellent perfor- mance obtained. Based on the thorough evaluation, our ScaleLSD is observed to be the first deep approach that outperforms the pioneered non-deep LSD in all aspects we have tested, significantly expanding and reinforcing the versatility of the line geometry of images.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ke_ScaleLSD_Scalable_Deep_Line_Segment_Detection_Streamlined_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ke_ScaleLSD_Scalable_Deep_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Ke_2025_CVPR,
    author    = {Ke, Zeran and Tan, Bin and Zheng, Xianwei and Shen, Yujun and Wu, Tianfu and Xue, Nan},
    title     = {ScaleLSD: Scalable Deep Line Segment Detection Streamlined},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6327-6336}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Ant Group", "city": "Unknown", "country": "Unknown"}, {"affiliation": "LIESMARS, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Department of ECE, NC State University", "city": "Raleigh", "country": "USA"}]

affiliations:
["School of Computer Science, Wuhan University", "Ant Group", "LIESMARS, Wuhan University", "Department of ECE, NC State University"]


title: AToM: Aligning Text-to-Motion Model at Event-Level with GPT-4Vision Reward
authors: Haonan Han, Xiangzuo Wu, Huan Liao, Zunnan Xu, Zhongyuan Hu, Ronghui Li, Yachao Zhang, Xiu Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22746-22755
cities: ["Shenzhen", "Xiamen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Han_AToM_Aligning_Text-to-Motion_Model_at_Event-Level_with_GPT-4Vision_Reward_CVPR_2025_paper.html

abstract:
Recently, text-to-motion models open new possibilities for creating realistic human motion with greater efficiency and flexibility. However, aligning motion generation with event-level textual descriptions presents unique challenges due to the complex, nuanced relationship between textual prompts and desired motion outcomes. To address this issue, we introduce AToM, a framework that enhances the alignment between generated motion and text prompts by leveraging reward from GPT-4Vision. AToM comprises three main stages: Firstly, we construct a dataset MotionPrefer that pairs three types of event-level textual prompts with generated motions, which cover the integrity, temporal relationship and the frequency of motion. Secondly, we design a paradigm that utilizes GPT-4Vision for detailed motion annotation, including visual data formatting, task-specific instructions and scoring rules for each sub-task. Finally, we fine-tune an existing text-to-motion model using reinforcement learning guided by this paradigm. Experimental results demonstrate that AToM significantly improves the event-level alignment quality of text-to-motion generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Han_AToM_Aligning_Text-to-Motion_Model_at_Event-Level_with_GPT-4Vision_Reward_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Han_AToM_Aligning_Text-to-Motion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Han_2025_CVPR,
    author    = {Han, Haonan and Wu, Xiangzuo and Liao, Huan and Xu, Zunnan and Hu, Zhongyuan and Li, Ronghui and Zhang, Yachao and Li, Xiu},
    title     = {AToM: Aligning Text-to-Motion Model at Event-Level with GPT-4Vision Reward},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22746-22755}
}

raw_affiliation:
[{"affiliation": "Shenzhen International Graduate School, Tsinghua University", "city": "Shenzhen", "country": "China"}, {"affiliation": "School of Informatics, Xiamen University", "city": "Xiamen", "country": "China"}]

affiliations:
["Shenzhen International Graduate School, Tsinghua University", "School of Informatics, Xiamen University"]


title: Revisiting MAE Pre-training for 3D Medical Image Segmentation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5186-5196
countries: ["Germany", "Germany", "Germany", "Germany", "Germany", "Germany", "Italy", "Germany", "Germany"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wald_Revisiting_MAE_Pre-training_for_3D_Medical_Image_Segmentation_CVPR_2025_paper.html

authors:
Tassilo Wald, Constantin Ulrich, Stanislav Lukyanenko, Andrei Goncharov, Alberto Paderno, Maximilian Miller, Leander Maerkisch, Paul Jaeger, Klaus Maier-Hein

abstract:
Self-Supervised Learning (SSL) presents an exciting opportunity to unlock the potential of vast, untapped clinical datasets, for various downstream applications that suffer from the scarcity of labeled data. While SSL has revolutionized fields like natural language processing and computer vision, its adoption in 3D medical image computing has been limited by three key pitfalls: Small pre-training dataset sizes, architectures inadequate for 3D medical image analysis, and insufficient evaluation practices. In this paper, we address these issues by i) leveraging a large-scale dataset of 39k 3D brain MRI volumes and ii) using a Residual Encoder U-Net architecture within the state-of-the-art nnU-Net framework. iii) A robust development framework, incorporating 5 development and 8 testing brain MRI segmentation datasets, allowed performance-driven design decisions to optimize the simple concept of Masked Auto En- coders (MAEs) for 3D CNNs. The resulting model not only surpasses previous SSL methods but also outperforms the strong nnU-Net baseline by an average of approximately 3 Dice points setting a new state-of-the-art. Our code and models are made available here.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wald_Revisiting_MAE_Pre-training_for_3D_Medical_Image_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wald_Revisiting_MAE_Pre-training_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wald_2025_CVPR,
    author    = {Wald, Tassilo and Ulrich, Constantin and Lukyanenko, Stanislav and Goncharov, Andrei and Paderno, Alberto and Miller, Maximilian and Maerkisch, Leander and Jaeger, Paul and Maier-Hein, Klaus},
    title     = {Revisiting MAE Pre-training for 3D Medical Image Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5186-5196}
}

raw_affiliation:
[{"affiliation": "Division of Medical Image Computing, German Cancer Research Center (DKFZ)", "city": "Heidelberg", "country": "Germany"}, {"affiliation": "Helmholtz Imaging, DKFZ", "city": "Heidelberg", "country": "Germany"}, {"affiliation": "Faculty of Mathematics and Computer Science, University of Heidelberg", "city": "Heidelberg", "country": "Germany"}, {"affiliation": "Medical Faculty Heidelberg, University of Heidelberg", "city": "Heidelberg", "country": "Germany"}, {"affiliation": "National Center for Tumor Diseases (NCT), NCT Heidelberg", "city": "Heidelberg", "country": "Germany"}, {"affiliation": "FLOY", "city": "Munich", "country": "Germany"}, {"affiliation": "Department of Biomedical Sciences, Humanitas University (Milan), IRCCS Humanitas Research Hospital", "city": "Rozzano", "country": "Italy"}, {"affiliation": "Interactive Machine Learning Group, DKFZ Heidelberg", "city": "Heidelberg", "country": "Germany"}, {"affiliation": "Pattern Analysis and Learning Group, Department of Radiation Oncology", "city": "Heidelberg", "country": "Germany"}]

affiliations:
["Division of Medical Image Computing, German Cancer Research Center (DKFZ)", "Helmholtz Imaging, DKFZ", "Faculty of Mathematics and Computer Science, University of Heidelberg", "Medical Faculty Heidelberg, University of Heidelberg", "National Center for Tumor Diseases (NCT), NCT Heidelberg", "FLOY", "Department of Biomedical Sciences, Humanitas University (Milan), IRCCS Humanitas Research Hospital", "Interactive Machine Learning Group, DKFZ Heidelberg", "Pattern Analysis and Learning Group, Department of Radiation Oncology"]

cities:
["Heidelberg", "Heidelberg", "Heidelberg", "Heidelberg", "Heidelberg", "Munich", "Rozzano", "Heidelberg", "Heidelberg"]


title: Learning with Noisy Triplet Correspondence for Composed Image Retrieval
authors: Shuxian Li, Changhao He, Xiting Liu, Joey Tianyi Zhou, Xi Peng, Peng Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19628-19637
cities: ["Chengdu", "Atlanta", "Singapore", "Singapore", "Chengdu"]
countries: ["China", "USA", "Singapore", "Singapore", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Learning_with_Noisy_Triplet_Correspondence_for_Composed_Image_Retrieval_CVPR_2025_paper.html

abstract:
Composed Image Retrieval (CIR) enables editable image search by integrating a query pair--a reference image ref and a textual modification mod--to retrieve a target image tar that reflects the intended change. While existing CIR methods have shown promising performance using well-annotated triplets \langle ref, mod, tar \rangle, almost all of them implicitly assume these triplets are accurately associated with each other. In practice, however, this assumption is often violated due to the limited knowledge of annotators, inevitably leading to incorrect textual modifications and resulting in a practical yet less-touched problem: noisy triplet correspondence (NTC). To tackle this challenge, we propose a \underline T ask-oriented \underline M odification \underline E nhancement framework (TME) to learn robustly from noisy triplets, which comprises three key modules: Robust Fusion Query (RFQ), Pseudo Text Enhancement (PTE), and Task-Oriented Prompt (TOP). Specifically, to mitigate the adverse impact of noise, RFQ employs a sample selection strategy to divide the training triplets into clean and noisy sets, thus enhancing the reliability of the training data for robust learning. To further leverage the noisy data instead of discarding it, PTE unifies the triplet noise as an adapter mismatch problem, thereby adjusting mod to align with ref and tar in the mismatched triplet. Finally, TOP replaces ref in the clean set with a trainable prompt, which is then concatenated with mod to form a query independent of the visual reference, aiming to mitigate visually irrelevant noise. Extensive experiments on two domain-specific datasets demonstrate the robustness and superiority of TME, particularly in noisy scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Learning_with_Noisy_Triplet_Correspondence_for_Composed_Image_Retrieval_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Learning_with_Noisy_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Shuxian and He, Changhao and Liu, Xiting and Zhou, Joey Tianyi and Peng, Xi and Hu, Peng},
    title     = {Learning with Noisy Triplet Correspondence for Composed Image Retrieval},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19628-19637}
}

raw_affiliation:
[{"affiliation": "College of Computer Science, Sichuan University", "city": "Chengdu", "country": "China"}, {"affiliation": "Georgia Insitute of Technology", "city": "Atlanta", "country": "USA"}, {"affiliation": "Centre for Frontier Al Research, Agency for Science, Technology and Research", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Institute of High Performance Computing, Agency for Science, Technology and Research", "city": "Singapore", "country": "Singapore"}, {"affiliation": "National Key Laboratory of Fundamental Algorithms and Models for Engineering Simulation", "city": "Chengdu", "country": "China"}]

affiliations:
["College of Computer Science, Sichuan University", "Georgia Insitute of Technology", "Centre for Frontier Al Research, Agency for Science, Technology and Research", "Institute of High Performance Computing, Agency for Science, Technology and Research", "National Key Laboratory of Fundamental Algorithms and Models for Engineering Simulation"]


title: DoF-Gaussian: Controllable Depth-of-Field for 3D Gaussian Splatting
authors: Liao Shen, Tianqi Liu, Huiqiang Sun, Jiaqi Li, Zhiguo Cao, Wei Li, Chen Change Loy
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26462-26471
cities: ["Wuhan", "Singapore"]
countries: ["China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shen_DoF-Gaussian_Controllable_Depth-of-Field_for_3D_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
Recent advances in 3D Gaussian Splatting (3D-GS) have shown remarkable success in representing 3D scenes and generating high-quality, novel views in real-time. However, 3D-GS and its variants assume that input images are captured based on pinhole imaging and are fully in focus. This assumption limits their applicability, as real-world images often feature shallow depth-of-field (DoF). In this paper, we introduce DoF-Gaussian, a controllable depth-of-field method for 3D-GS. We develop a lens-based imaging model based on geometric optics principles to control DoF effects. To ensure accurate scene geometry, we incorporate depth priors adjusted per scene, and we apply defocus-to-focus adaptation to minimize the gap in the circle of confusion. We also introduce a synthetic dataset to assess refocusing capabilities and the model's ability to learn precise lens parameters. Our framework is customizable and supports various interactive applications. Extensive experiments confirm the effectiveness of our method. Our project is available at  https://dof-gaussian.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_DoF-Gaussian_Controllable_Depth-of-Field_for_3D_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shen_DoF-Gaussian_Controllable_Depth-of-Field_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shen_2025_CVPR,
    author    = {Shen, Liao and Liu, Tianqi and Sun, Huiqiang and Li, Jiaqi and Cao, Zhiguo and Li, Wei and Loy, Chen Change},
    title     = {DoF-Gaussian: Controllable Depth-of-Field for 3D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26462-26471}
}

raw_affiliation:
[{"affiliation": "School of AIA, Huazhong University of Science and T echnology", "city": "Wuhan", "country": "China"}, {"affiliation": "S-Lab, Nanyang T echnological University", "city": "Singapore", "country": "Singapore"}]

affiliations:
["School of AIA, Huazhong University of Science and T echnology", "S-Lab, Nanyang T echnological University"]


title: Parallelized Autoregressive Visual Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12955-12965
affiliations: ["University of Hong Kong", "ByteDance", "Peking University"]
cities: ["Hong Kong", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Parallelized_Autoregressive_Visual_Generation_CVPR_2025_paper.html

authors:
Yuqing Wang, Shuhuai Ren, Zhijie Lin, Yujin Han, Haoyuan Guo, Zhenheng Yang, Difan Zou, Jiashi Feng, Xihui Liu

abstract:
Autoregressive models have emerged as a powerful approach for visual generation but suffer from slow inference speed due to their sequential token-by-token prediction process. In this paper, we propose a simple yet effective approach for parallelized autoregressive visual generation that improves generation efficiency while preserving the advantages of autoregressive modeling. Our key insight is that parallel generation depends on visual token dependencies: tokens with weak dependencies can be generated in parallel, while strongly dependent adjacent tokens are difficult to generate together, as their independent sampling may lead to inconsistencies. Based on this observation, we develop a parallel generation strategy that generates distant tokens with weak dependencies in parallel while maintaining sequential generation for strongly dependent local tokens. Our approach can be seamlessly integrated into standard autoregressive models without modifying the architecture or tokenizer. Experiments on ImageNet and UCF-101 demonstrate that our method achieves a 3.6x speedup with comparable quality and up to 9.5x speedup with minimal quality degradation across both image and video generation tasks. We hope this work will inspire future research in efficient visual generation and unified autoregressive modeling. Project page: https://yuqingwang1029.github.io/PAR-project.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Parallelized_Autoregressive_Visual_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Parallelized_Autoregressive_Visual_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yuqing and Ren, Shuhuai and Lin, Zhijie and Han, Yujin and Guo, Haoyuan and Yang, Zhenheng and Zou, Difan and Feng, Jiashi and Liu, Xihui},
    title     = {Parallelized Autoregressive Visual Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12955-12965}
}

raw_affiliation:
[{"affiliation": "University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "ByteDance", "city": "Beijing", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}]


title: CGMatch: A Different Perspective of Semi-supervised Learning
authors: Bo Cheng, Jueqing Lu, Yuan Tian, Haifeng Zhao, Yi Chang, Lan Du
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15381-15391
affiliations: ["Jilin University", "Monash University", "Jinling Institute of Technology"]
cities: ["Changchun", "Melbourne", "Nanjing"]
countries: ["China", "Australia", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cheng_CGMatch_A_Different_Perspective_of_Semi-supervised_Learning_CVPR_2025_paper.html

abstract:
Semi-supervised learning (SSL) has garnered significant attention due to its ability to leverage limited labeled data and a large amount of unlabeled data to improve model generalization performance. Recent approaches achieve impressive successes by combining ideas from both consistency regularization and pseudo-labeling. However, these methods tend to underperform in the more realistic situations with relatively scarce labeled data. We argue that this issue arises because existing methods rely solely on the model's confidence, making them challenging to accurately assess the model's state and identify unlabeled examples contributing to the training phase when supervision information is limited, especially during the early stages of model training. In this paper, we propose a novel SSL model called CGMatch, which, for the first time, incorporates a new metric known as Count-Gap (CG). We demonstrate that CG is effective in discovering unlabeled examples beneficial for model training. Along with confidence, a commonly used metric in SSL, we propose a fine-grained dynamic selection (FDS) strategy. This strategy dynamically divides the unlabeled dataset into three subsets with different characteristics: easy-to-learn set, ambiguous set, and hard-to-learn set. By selective filtering subsets, and applying corresponding regularization with selected subsets, we mitigate the negative impact of incorrect pseudo-labels on model optimization and generalization. Extensive experimental results on several common SSL benchmarks indicate the effectiveness of CGMatch especially when the labeled data are particularly limited. Source code is available at  https://github.com/BoCheng-96/CGMatch.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cheng_CGMatch_A_Different_Perspective_of_Semi-supervised_Learning_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Cheng_2025_CVPR,
    author    = {Cheng, Bo and Lu, Jueqing and Tian, Yuan and Zhao, Haifeng and Chang, Yi and Du, Lan},
    title     = {CGMatch: A Different Perspective of Semi-supervised Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15381-15391}
}

raw_affiliation:
[{"affiliation": "Jilin University", "city": "Changchun", "country": "China"}, {"affiliation": "Monash University", "city": "Melbourne", "country": "Australia"}, {"affiliation": "Jinling Institute of Technology", "city": "Nanjing", "country": "China"}]


title: ChatHuman: Chatting about 3D Humans with Tools
authors: Jing Lin, Yao Feng, Weiyang Liu, Michael J. Black
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8150-8161
cities: ["T\u00fcbingen", "Stanford", "unknown", "Beijing", "Cambridge"]
countries: ["Germany", "USA", "unknown", "China", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_ChatHuman_Chatting_about_3D_Humans_with_Tools_CVPR_2025_paper.html

abstract:
Numerous methods have been proposed to detect, estimate, and analyze properties of people in images, including the estimation of 3D pose, shape, contact, human-object interaction, emotion, and more. While widely applicable in vision and other areas, such methods require expert knowledge to select, use, and interpret the results. To address this, we introduce ChatHuman, a language-driven system that combines and integrates the skills of these specialized methods.  ChatHuman functions as an assistant proficient in utilizing, analyzing, and interacting with tools specific to 3D human tasks, adeptly discussing and resolving related challenges. Built on a Large Language Model (LLM) framework, ChatHuman is trained to autonomously select, apply, and interpret a diverse set of tools in response to user inputs. Adapting LLMs to 3D human tasks presents challenges, including the need for domain-specific knowledge and the ability to interpret complex 3D outputs. The innovative features of ChatHuman include leveraging academic publications to instruct the LLM how to use the tools, employing a retrieval-augmented generation model to create in-context learning examples for managing new tools, and effectively discriminating and integrating tool results to enhance tasks related to 3D humans by transforming specialized 3D outputs into comprehensible formats. Our experiments demonstrate that ChatHuman surpasses existing models in both tool selection accuracy and overall performance across various 3D human tasks, and it supports interactive chatting with users. ChatHuman represents a significant step toward consolidating diverse analytical methods into a unified, robust system for 3D human tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_ChatHuman_Chatting_about_3D_Humans_with_Tools_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_ChatHuman_Chatting_about_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Jing and Feng, Yao and Liu, Weiyang and Black, Michael J.},
    title     = {ChatHuman: Chatting about 3D Humans with Tools},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8150-8161}
}

raw_affiliation:
[{"affiliation": "Max Planck Institute for Intelligent Systems", "city": "T\u00fcbingen", "country": "Germany"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "Meshcapade", "city": "unknown", "country": "unknown"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "University of Cambridge", "city": "Cambridge", "country": "UK"}]

affiliations:
["Max Planck Institute for Intelligent Systems", "Stanford University", "Meshcapade", "Tsinghua University", "University of Cambridge"]


title: FIction: 4D Future Interaction Prediction from Video
authors: Kumar Ashutosh, Georgios Pavlakos, Kristen Grauman
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17613-17625
raw_affiliation: [{"affiliation": "University of Texas at Austin", "city": "Austin", "country": "USA"}]
affiliations: ["University of Texas at Austin"]
cities: ["Austin"]
countries: ["USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ashutosh_FIction_4D_Future_Interaction_Prediction_from_Video_CVPR_2025_paper.html

abstract:
Anticipating how a person will interact with objects in an environment is essential for activity understanding, but existing methods are limited to the 2D space of video frames--capturing physically ungrounded predictions of "what" and ignoring the "where" and "how". We introduce FIction for 4D future interaction prediction from videos. Given an input video of a human activity, the goal is to predict which objects at what 3D locations the person will interact with in the next time period (e.g., cabinet, fridge), and how they will execute that interaction (e.g., poses for bending, reaching, pulling). Our novel model FIction fuses the past video observation of the person's actions and their environment to predict both the "where" and "how" of future interactions. Through comprehensive experiments on a variety of activities and real-world environments in Ego-Exo4D, we show that our proposed approach outperforms prior autoregressive and (lifted) 2D video models substantially, with more than 30% relative gains.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ashutosh_FIction_4D_Future_Interaction_Prediction_from_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ashutosh_FIction_4D_Future_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ashutosh_2025_CVPR,
    author    = {Ashutosh, Kumar and Pavlakos, Georgios and Grauman, Kristen},
    title     = {FIction: 4D Future Interaction Prediction from Video},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17613-17625}
}


title: D^2iT: Dynamic Diffusion Transformer for Accurate Image Generation
authors: Weinan Jia, Mengqi Huang, Nan Chen, Lei Zhang, Zhendong Mao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12860-12870
cities: ["Hefei", "Hefei"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jia_D2iT_Dynamic_Diffusion_Transformer_for_Accurate_Image_Generation_CVPR_2025_paper.html

abstract:
Diffusion models are widely recognized for their ability to generate high-fidelity images. Despite the excellent performance and scalability of the Diffusion Transformer (DiT) architecture, it applies fixed compression across different image regions during the diffusion process, disregarding the naturally varying information densities present in these regions. However, large compression leads to limited local realism, while small compression increases computational complexity and compromises global consistency, ultimately impacting the quality of generated images. To address these limitations, we propose dynamically compressing different image regions by recognizing the importance of different regions, and introduce a novel two-stage framework designed to enhance the effectiveness and efficiency of image generation: (1) Dynamic VAE (DVAE) at first stage employs a hierarchical encoder to encode different image regions at different downsampling rates, tailored to their specific information densities, thereby providing more accurate and natural latent codes for the diffusion process. (2) Dynamic Diffusion Transformer (D^2iT) at second stage generates images by predicting multi-grained noise, consisting of coarse-grained (less latent code in smooth regions) and fine-grained (more latent codes in detailed regions), through an novel combination of the Dynamic Grain Transformer and the Dynamic Content Transformer. The strategy of combining rough prediction of noise with detailed regions correction achieves a unification of global consistency and local realism. Comprehensive experiments on various generation tasks validate the effectiveness of our approach. Code will be released at https://github.com/jiawn-creator/Dynamic-DiT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jia_D2iT_Dynamic_Diffusion_Transformer_for_Accurate_Image_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jia_D2iT_Dynamic_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jia_2025_CVPR,
    author    = {Jia, Weinan and Huang, Mengqi and Chen, Nan and Zhang, Lei and Mao, Zhendong},
    title     = {D{\textasciicircum}2iT: Dynamic Diffusion Transformer for Accurate Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12860-12870}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Institute of Artificial intelligence, Hefei Comprehensive National Science Center", "city": "Hefei", "country": "China"}]

affiliations:
["University of Science and Technology of China", "Institute of Artificial intelligence, Hefei Comprehensive National Science Center"]


title: Scalable Autoregressive Monocular Depth Estimation
authors: Jinhong Wang, Jian Liu, Dongqi Tang, Weiqiang Wang, Wentong Li, Danny Chen, Jintai Chen, Jian Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6262-6272
affiliations: ["ZJU", "Ant Group", "University of Notre Dame", "HKUST(Guangzhou)"]
cities: ["Hangzhou", "Hangzhou", "Notre Dame", "Guangzhou"]
countries: ["China", "China", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Scalable_Autoregressive_Monocular_Depth_Estimation_CVPR_2025_paper.html

abstract:
This paper proposes a new autoregressive model as an effective and scalable monocular depth estimator. Our idea is simple: We tackle the monocular depth estimation (MDE) task with an autoregressive prediction paradigm, based on two core designs. First, our depth autoregressive model (DAR) treats the depth map of different resolutions as a set of tokens, and conducts the low-to-high resolution autoregressive objective with a patch-wise casual mask. Second, our DAR recursively discretizes the entire depth range into more compact intervals, and attains the coarse-to-fine granularity autoregressive objective in an ordinal-regression manner. By coupling these two autoregressive objectives, our DAR establishes new state-of-the-art (SOTA) on KITTI and NYU Depth v2 by clear margins. Further, our scalable approach allows us to scale the model up to 2.0B and achieve the best RMSE of 1.799 on the KITTI dataset (5% improvement) compared to 1.896 by the current SOTA (Depth Anything). DAR further showcases zero-shot generalization ability on unseen datasets. These results suggest that DAR yields superior performance with an autoregressive prediction paradigm, providing a promising approach to equip modern autoregressive large models (e.g., GPT-4o) with depth estimation capabilities. Project page: https://depth-ar.github.io/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Scalable_Autoregressive_Monocular_Depth_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Scalable_Autoregressive_Monocular_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Jinhong and Liu, Jian and Tang, Dongqi and Wang, Weiqiang and Li, Wentong and Chen, Danny and Chen, Jintai and Wu, Jian},
    title     = {Scalable Autoregressive Monocular Depth Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6262-6272}
}

raw_affiliation:
[{"affiliation": "ZJU", "city": "Hangzhou", "country": "China"}, {"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "University of Notre Dame", "city": "Notre Dame", "country": "USA"}, {"affiliation": "HKUST(Guangzhou)", "city": "Guangzhou", "country": "China"}]


authors: Chong Wang, Lanqing Guo, Zixuan Fu, Siyuan Yang, Hao Cheng, Alex C. Kot, Bihan Wen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23207-23216
cities: ["Singapore", "Austin", "Tianjin"]
countries: ["Singapore", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Reconciling_Stochastic_and_Deterministic_Strategies_for_Zero-shot_Image_Restoration_using_CVPR_2025_paper.html

title:
Reconciling Stochastic and Deterministic Strategies for Zero-shot Image Restoration using Diffusion Model in Dual

abstract:
Plug-and-play (PnP) methods offer an iterative strategy for solving image restoration (IR) problems in a zero-shot manner, using a learned discriminative denoiser as the implicit prior. More recently, a sampling-based variant of this approach, which utilizes a pre-trained generative diffusion model, has gained great popularity for solving IR problems through stochastic sampling. The IR results using PnP with a pre-trained diffusion model demonstrate distinct advantages compared to those using discriminative denoisers, i.e., improved perceptual quality while sacrificing the data fidelity. The unsatisfactory results are due to the lack of integration of these strategies in the IR tasks. In this work, we propose a novel zero-shot IR scheme, dubbed Reconciling Diffusion Model in Dual (RDMD), which leverages only a single pre-trained diffusion model to construct two complementary regularizers. Specifically, the diffusion model in RDMD will iteratively perform deterministic denoising and stochastic sampling, aiming to achieve high-fidelity image restoration with appealing perceptual quality.RDMD also allows users to customize the distortion-perception tradeoff with a single hyperparameter, enhancing the adaptability of the restoration process in different practical scenarios. Extensive experiments on several IR tasks demonstrate that our proposed method could achieve superior results compared to existing approaches on both the FFHQ and ImageNet datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Reconciling_Stochastic_and_Deterministic_Strategies_for_Zero-shot_Image_Restoration_using_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Chong and Guo, Lanqing and Fu, Zixuan and Yang, Siyuan and Cheng, Hao and Kot, Alex C. and Wen, Bihan},
    title     = {Reconciling Stochastic and Deterministic Strategies for Zero-shot Image Restoration using Diffusion Model in Dual},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23207-23216}
}

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

affiliations:
["Nanyang Technological University, Singapore", "The University of Texas at Austin, USA", "Hebei University of Technology, China"]


title: Hierarchical Flow Diffusion for Efficient Frame Interpolation
authors: Yang Hai, Guo Wang, Tan Su, Wenjie Jiang, Yinlin Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22943-22952
affiliations: ["Insta360 Research", "MagicLeap"]
cities: ["Shenzhen", "Sunnyvale"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hai_Hierarchical_Flow_Diffusion_for_Efficient_Frame_Interpolation_CVPR_2025_paper.html

abstract:
Most recent diffusion-based methods still show a large gap compared to non-diffusion methods for video frame interpolation, in both accuracy and efficiency. Most of them formulate the problem as a denoising procedure in latent space directly, which is less effective caused by the large latent space. We propose to model bilateral optical flow explicitly by hierarchical diffusion models, which has much smaller search space in the denoising procedure. Based on the flow diffusion model, we then use a flow-guided image synthesizer to produce the final result. We train the flow diffusion model and the image synthesizer end to end. Our method achieves state of the art in accuracy, and 10+ times faster than other diffusion-based methods. The project page is at: https://hfd-interpolation.github.io.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hai_Hierarchical_Flow_Diffusion_for_Efficient_Frame_Interpolation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Hai_2025_CVPR,
    author    = {Hai, Yang and Wang, Guo and Su, Tan and Jiang, Wenjie and Hu, Yinlin},
    title     = {Hierarchical Flow Diffusion for Efficient Frame Interpolation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22943-22952}
}

raw_affiliation:
[{"affiliation": "Insta360 Research", "city": "Shenzhen", "country": "China"}, {"affiliation": "MagicLeap", "city": "Sunnyvale", "country": "USA"}]


title: Recurrence-Enhanced Vision-and-Language Transformers for Robust Multimodal Document Retrieval
authors: Davide Caffagni, Sara Sarto, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9286-9295
affiliations: ["University of Modena and Reggio Emilia", "IIT-CNR"]
cities: ["Modena", "Italy"]
countries: ["Italy", "Italy"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Caffagni_Recurrence-Enhanced_Vision-and-Language_Transformers_for_Robust_Multimodal_Document_Retrieval_CVPR_2025_paper.html

abstract:
Cross-modal retrieval is gaining increasing efficacy and interest from the research community, thanks to large-scale training, novel architectural and learning designs, and its application in LLMs and multimodal LLMs. In this paper, we move a step forward and design an approach that allows for multimodal queries - composed of both an image and a text - and can search within collections of multimodal documents, where images and text are interleaved. Our model, ReT, employs multi-level representations extracted from different layers of both visual and textual backbones, both at the query and document side. To allow for multi-level and cross-modal understanding and feature extraction, ReT employs a novel Transformer-based recurrent cell that integrates both textual and visual features at different layers, and leverages sigmoidal gates inspired by the classical design of LSTMs. Extensive experiments on M2KR and M-BEIR benchmarks show that ReT achieves state-of-the-art performance across diverse settings. Our source code and trained models are publicly available at: https://github.com/aimagelab/ReT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Caffagni_Recurrence-Enhanced_Vision-and-Language_Transformers_for_Robust_Multimodal_Document_Retrieval_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Caffagni_Recurrence-Enhanced_Vision-and-Language_Transformers_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Caffagni_2025_CVPR,
    author    = {Caffagni, Davide and Sarto, Sara and Cornia, Marcella and Baraldi, Lorenzo and Cucchiara, Rita},
    title     = {Recurrence-Enhanced Vision-and-Language Transformers for Robust Multimodal Document Retrieval},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9286-9295}
}

raw_affiliation:
[{"affiliation": "University of Modena and Reggio Emilia", "city": "Modena", "country": "Italy"}, {"affiliation": "IIT-CNR", "city": "Italy", "country": "Italy"}]


title: Camouflage Anything: Learning to Hide using Controlled Out-painting and Representation Engineering
authors: Biplab Das, Viswanath Gopalakrishnan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3603-3613
affiliations: ["IIIT Bangalore", "Samsung R&D Institute, Bangalore", "IIIT Bangalore"]
cities: ["Bangalore", "Bangalore", "Bangalore"]
countries: ["India", "India", "India"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Das_Camouflage_Anything_Learning_to_Hide_using_Controlled_Out-painting_and_Representation_CVPR_2025_paper.html

abstract:
In this work, we introduce Camouflage Anything, a novel and robust approach to generate camouflaged datasets. To the best of our knowledge, we are the first to apply Controlled Out-painting and Representation Engineering for generating realistic camouflaged images with an objective to hide any segmented object coming from a generic or salient database. Our proposed method uses a novel control design to out-paint a given segmented object, with a camouflaged background. We also uncover the role of representation engineering in enhancing the quality of generated camouflage datasets. We address the limitations of existing metrics FID and KID in capturing the 'camouflage quality' ,by proposing a novel metric namely, CamOT. CamOT uses Optimal Transport between foreground & background Gaussian Mixture Models (GMM) of concerned camouflaged object to assign an image quality score. Furthermore, we conduct LoRA-based fine-tuning of the robust BiRefNet baseline with our generated camouflaged datasets, leading to notable improvements in camouflaged object segmentation accuracy. The experimental results showcase the efficacy and potential of Camouflage Anything, outperforming existing methods in camouflaged generation tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Das_Camouflage_Anything_Learning_to_Hide_using_Controlled_Out-painting_and_Representation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Das_Camouflage_Anything_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Das_2025_CVPR,
    author    = {Das, Biplab and Gopalakrishnan, Viswanath},
    title     = {Camouflage Anything: Learning to Hide using Controlled Out-painting and Representation Engineering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3603-3613}
}

raw_affiliation:
[{"affiliation": "IIIT Bangalore", "city": "Bangalore", "country": "India"}, {"affiliation": "Samsung R&D Institute, Bangalore", "city": "Bangalore", "country": "India"}, {"affiliation": "IIIT Bangalore", "city": "Bangalore", "country": "India"}]


title: Test-Time Fine-Tuning of Image Compression Models for Multi-Task Adaptability
authors: Unki Park, Seongmoon Jeong, Youngchan Jang, Gyeong-Moon Park, Jong Hwan Ko
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4430-4440
cities: ["Seoul", "Seoul", "Seoul", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Park_Test-Time_Fine-Tuning_of_Image_Compression_Models_for_Multi-Task_Adaptability_CVPR_2025_paper.html

abstract:
The field of computer vision was initially inspired by the human visual system and has progressively expanded to include a broader range of machine vision applications. Consequently, image compressors should be designed to effectively accommodate not only human visual perception but also machine vision tasks, including closed-set scenarios that enable pre-training and open-set scenarios that involve previously unseen tasks at test time. Many recent studies effectively address both human visual perception and closed-set machine vision tasks simultaneously but struggle to handle open-set machine vision tasks. To address this issue, this paper proposes a fully instance-specific test time fine-tuning (TTFT) for adapting learned image compression (LIC) to both closed-set and open-set machine vision tasks effectively. With our method, a large-scale LIC model, originally trained for human perception, is adapted to the target task through TTFT using Singular Value Decomposition based Low Rank Adaptation (SVD-LoRA). During TTFT, the decoder adopts a modified learning scheme that focuses exclusively on training the singular values, which helps prevent excessive bitstream overhead. This enables fully instance-specific optimization for the target task, even for open-set tasks. Experimental results demonstrate that the proposed method effectively adapts the backbone compressor to diverse machine vision tasks, outperforming competing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Park_Test-Time_Fine-Tuning_of_Image_Compression_Models_for_Multi-Task_Adaptability_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Park_Test-Time_Fine-Tuning_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Park_2025_CVPR,
    author    = {Park, Unki and Jeong, Seongmoon and Jang, Youngchan and Park, Gyeong-Moon and Ko, Jong Hwan},
    title     = {Test-Time Fine-Tuning of Image Compression Models for Multi-Task Adaptability},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4430-4440}
}

raw_affiliation:
[{"affiliation": "Sungkyunkwan University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Samsung Institute of Technology", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Hanwha Systems Corporation", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Korea University", "city": "Seoul", "country": "South Korea"}]

affiliations:
["Sungkyunkwan University", "Samsung Institute of Technology", "Hanwha Systems Corporation", "Korea University"]


title: BASKET: A Large-Scale Video Dataset for Fine-Grained Skill Estimation
authors: Yulu Pan, Ce Zhang, Gedas Bertasius
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28952-28962
raw_affiliation: [{"affiliation": "UNC Chapel Hill", "city": "Chapel Hill", "country": "USA"}]
affiliations: ["UNC Chapel Hill"]
cities: ["Chapel Hill"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pan_BASKET_A_Large-Scale_Video_Dataset_for_Fine-Grained_Skill_Estimation_CVPR_2025_paper.html

abstract:
We present BASKET, a large-scale basketball video dataset for fine-grained skill estimation. BASKET contains 4,477 hours of video capturing 32,232 basketball players from all over the world. Compared to prior skill estimation datasets, our dataset includes a massive number of skilled participants with unprecedented diversity in terms of gender, age, skill level, geographical location, etc. BASKET includes 20 fine-grained basketball skills, challenging modern video recognition models to capture the intricate nuances of player skill through in-depth video analysis. Given a long highlight video (8-10 minutes) of a particular player, the model needs to predict the skill level (e.g., excellent, good, average, fair, poor) for each of the 20 basketball skills. Our empirical analysis reveals that the current state-of-the-art video models struggle with this task, significantly lagging behind the human baseline. We believe that BASKET could be a useful resource for developing new video models with advanced long-range, fine-grained recognition capabilities. In addition, we hope that our dataset will be useful for domain-specific applications such as fair basketball scouting, personalized player development, and many others. Dataset and code are available at https://github.com/yulupan00/BASKET.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pan_BASKET_A_Large-Scale_Video_Dataset_for_Fine-Grained_Skill_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pan_BASKET_A_Large-Scale_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Pan_2025_CVPR,
    author    = {Pan, Yulu and Zhang, Ce and Bertasius, Gedas},
    title     = {BASKET: A Large-Scale Video Dataset for Fine-Grained Skill Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28952-28962}
}


title: AniDoc: Animation Creation Made Easier
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18187-18197
affiliations: ["HKUST", "Ant Group", "NJU", "ZJU", "HKU"]
cities: ["Hong Kong", "Hangzhou", "Nanjing", "Hangzhou", "Hong Kong"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Meng_AniDoc_Animation_Creation_Made_Easier_CVPR_2025_paper.html

authors:
Yihao Meng, Hao Ouyang, Hanlin Wang, Qiuyu Wang, Wen Wang, Ka Leong Cheng, Zhiheng Liu, Yujun Shen, Huamin Qu

abstract:
The production of 2D animation follows an industry-standard workflow, encompassing four essential stages: character design, keyframe animation, in-betweening, and coloring. Our research focuses on reducing the labor costs in the above process by harnessing the potential of increasingly powerful generative AI. Using video diffusion models as the foundation, Anidoc emerges as a video line art colorization tool, which automatically converts sketch sequences into colored animations following the reference character specification. Our model exploits correspondence matching as an explicit guidance, yielding strong robustness to the variations (e.g., posture) between the reference character and each line art frame. In addition, our model could even automate the in-betweening process, such that users can easily create a temporally consistent animation by simply providing a character image as well as the start and end sketches. We will make the model public to facility the community.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Meng_AniDoc_Animation_Creation_Made_Easier_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Meng_AniDoc_Animation_Creation_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Meng_2025_CVPR,
    author    = {Meng, Yihao and Ouyang, Hao and Wang, Hanlin and Wang, Qiuyu and Wang, Wen and Cheng, Ka Leong and Liu, Zhiheng and Shen, Yujun and Qu, Huamin},
    title     = {AniDoc: Animation Creation Made Easier},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18187-18197}
}

raw_affiliation:
[{"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}, {"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "NJU", "city": "Nanjing", "country": "China"}, {"affiliation": "ZJU", "city": "Hangzhou", "country": "China"}, {"affiliation": "HKU", "city": "Hong Kong", "country": "China"}]


title: DynPose: Largely Improving the Efficiency of Human Pose Estimation by a Simple Dynamic Framework
authors: Yalong Xu, Lin Zhao, Chen Gong, Guangyu Li, Di Wang, Nannan Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1160-1169
cities: ["Nanjing", "Xi'an"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_DynPose_Largely_Improving_the_Efficiency_of_Human_Pose_Estimation_by_CVPR_2025_paper.html

abstract:
Top-down approaches for human pose estimation (HPE) have reached a high level of sophistication, exemplified by models such as HRNet and ViTPose. Nonetheless, the low efficiency of top-down methods is a recognized issue that has not been sufficiently explored in current research. Our analysis suggests that the primary cause of inefficiency stems from the substantial diversity found in pose samples. On one hand, simple poses can be accurately estimated without requiring the computational resources of larger models. On the other hand, a more prominent issue arises from the abundance of bounding boxes, which remain excessive even after NMS. In this paper, we present a straightforward yet effective dynamic framework called DynPose, designed to match diverse pose samples with the most appropriate models, thereby ensuring optimal performance and high efficiency. Specifically, the framework contains a lightweight router and two pre-trained HPE models: one small and one large. The router is optimized to classify samples and dynamically determine the appropriate inference paths. Extensive experiments demonstrate the effectiveness of the framework. For example, using ResNet-50 and HRNet-W32 as the pre-trained models, our DynPose achieves an almost 50% increase in speed over HRNet-W32 while maintaining the same-level accuracy. More importantly, the framework can be generalized to other pre-trained models and datasets without re-training or fine-tuning. Code is available at https://github.com/Aritoria/DynPose.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_DynPose_Largely_Improving_the_Efficiency_of_Human_Pose_Estimation_by_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Yalong and Zhao, Lin and Gong, Chen and Li, Guangyu and Wang, Di and Wang, Nannan},
    title     = {DynPose: Largely Improving the Efficiency of Human Pose Estimation by a Simple Dynamic Framework},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1160-1169}
}

raw_affiliation:
[{"affiliation": "PCA Lab, School of Computer Science and Engineering, Nanjing University of Science and Technology", "city": "Nanjing", "country": "China"}, {"affiliation": "State Key Laboratory of Integrated Services Networks, Xidian University", "city": "Xi'an", "country": "China"}]

affiliations:
["PCA Lab, School of Computer Science and Engineering, Nanjing University of Science and Technology", "State Key Laboratory of Integrated Services Networks, Xidian University"]


title: Arbitrary-steps Image Super-resolution via Diffusion Inversion
authors: Zongsheng Yue, Kang Liao, Chen Change Loy
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23153-23163
cities: ["Xi'an", "Singapore", "Singapore"]
countries: ["China", "Singapore", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yue_Arbitrary-steps_Image_Super-resolution_via_Diffusion_Inversion_CVPR_2025_paper.html

abstract:
This study presents a new image super-resolution (SR) technique based on diffusion inversion, aiming at harnessing the rich image priors encapsulated in large pre-trained diffusion models to improve SR performance. We design a Partial noise Prediction strategy to construct an intermediate state of the diffusion model, which serves as the starting sampling point. Central to our approach is a deep noise predictor to estimate the optimal noise maps for the forward diffusion process. Once trained, this noise predictor can be used to initialize the sampling process partially along the diffusion trajectory, generating the desirable high-resolution result. Compared to existing approaches, our method offers a flexible and efficient sampling mechanism that supports an arbitrary number of sampling steps, ranging from one to five. Even with a single sampling step, our method demonstrates superior or comparable performance to recent state-of-the-art approaches. The code and model will be made publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yue_Arbitrary-steps_Image_Super-resolution_via_Diffusion_Inversion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yue_Arbitrary-steps_Image_Super-resolution_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yue_2025_CVPR,
    author    = {Yue, Zongsheng and Liao, Kang and Loy, Chen Change},
    title     = {Arbitrary-steps Image Super-resolution via Diffusion Inversion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23153-23163}
}

raw_affiliation:
[{"affiliation": "Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "S-Lab, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]

affiliations:
["Xi\u2019an Jiaotong University", "Nanyang Technological University", "S-Lab, Nanyang Technological University"]


title: LiSu: A Dataset and Method for LiDAR Surface Normal Estimation
authors: Dušan Malić, Christian Fruhwirth-Reisinger, Samuel Schulter, Horst Possegger
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17039-17049
cities: ["Graz", "Graz", "Seattle"]
countries: ["Austria", "Austria", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Malic_LiSu_A_Dataset_and_Method_for_LiDAR_Surface_Normal_Estimation_CVPR_2025_paper.html

abstract:
While surface normals are widely used to analyse 3D scene geometry, surface normal estimation from LiDAR point clouds remains severely underexplored. This is caused by the lack of large-scale annotated datasets on the one hand, and lack of methods that can robustly handle the sparse and often noisy LiDAR data in a reasonable time on the other hand. We address these limitations using a traffic simulation engine and present LiSu, the first large-scale, synthetic LiDAR point cloud dataset with ground truth surface normal annotations, eliminating the need for tedious manual labeling. Additionally, we propose a novel method that exploits the spatiotemporal characteristics of autonomous driving data to enhance surface normal estimation accuracy. By incorporating two regularization terms, we enforce spatial consistency among neighboring points and temporal smoothness across consecutive LiDAR frames. These regularizers are particularly effective in self-training settings, where they mitigate the impact of noisy pseudo-labels, enabling robust real-world deployment. We demonstrate the effectiveness of our method on LiSu, achieving state-of-the-art performance in LiDAR surface normal estimation. Moreover, we showcase its full potential in addressing the challenging task of synthetic-to-real domain adaptation, leading to improved neural surface reconstruction on real-world data.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Malic_LiSu_A_Dataset_and_Method_for_LiDAR_Surface_Normal_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Malic_LiSu_A_Dataset_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Malic_2025_CVPR,
    author    = {Mali\'c, Du\v{s}an and Fruhwirth-Reisinger, Christian and Schulter, Samuel and Possegger, Horst},
    title     = {LiSu: A Dataset and Method for LiDAR Surface Normal Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17039-17049}
}

raw_affiliation:
[{"affiliation": "Christian Doppler Laboratory for Embedded Machine Learning", "city": "Graz", "country": "Austria"}, {"affiliation": "Institute of Visual Computing, Graz University of Technology", "city": "Graz", "country": "Austria"}, {"affiliation": "Amazon", "city": "Seattle", "country": "USA"}]

affiliations:
["Christian Doppler Laboratory for Embedded Machine Learning", "Institute of Visual Computing, Graz University of Technology", "Amazon"]


title: Dynamic Neural Surfaces for Elastic 4D Shape Representation and Analysis
authors: Awais Nizamani, Hamid Laga, Guanjin Wang, Farid Boussaid, Mohammed Bennamoun, Anuj Srivastava
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21783-21792
cities: ["Perth", "Perth", "Tallahassee"]
countries: ["Australia", "Australia", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nizamani_Dynamic_Neural_Surfaces_for_Elastic_4D_Shape_Representation_and_Analysis_CVPR_2025_paper.html

abstract:
We propose a novel framework for the statistical analysis of genus-zero 4D surfaces, i.e., 3D surfaces that deform and evolve overtime. This problem is particularly challenging due to the arbitrary parameterizations of these surfaces and their varying deformation speeds, necessitating effective spatiotemporal registration. Traditionally, 4D surfaces are discretized, in space and time, before computing their spatiotemporal registrations, geodesics and statistics. However, this approach may result in suboptimal solutions and, as we demonstrate in this paper, is not necessary. In contrast, we treat 4D surfaces as continuous functions in both space and time. We introduce  Dynamic Spherical Neural Surfaces (D-SNS), an efficient smooth and continuous spatiotemporal representation for  genus-0 4D  surfaces. We then demonstrate how to perform core  4D shape analysis tasks such as spatiotemporal registration, geodesics computation, and mean 4D shape estimation, directly on these continuous representations without upfront discretization and meshing. By integrating neural representations with classical Riemannian geometry and statistical shape analysis techniques, we provide the building blocks for enabling full functional shape analysis. We demonstrate the efficiency of the framework on 4D human and face datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nizamani_Dynamic_Neural_Surfaces_for_Elastic_4D_Shape_Representation_and_Analysis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nizamani_Dynamic_Neural_Surfaces_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nizamani_2025_CVPR,
    author    = {Nizamani, Awais and Laga, Hamid and Wang, Guanjin and Boussaid, Farid and Bennamoun, Mohammed and Srivastava, Anuj},
    title     = {Dynamic Neural Surfaces for Elastic 4D Shape Representation and Analysis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21783-21792}
}

raw_affiliation:
[{"affiliation": "Murdoch University (Australia)", "city": "Perth", "country": "Australia"}, {"affiliation": "The University of Western Australia", "city": "Perth", "country": "Australia"}, {"affiliation": "Florida State University", "city": "Tallahassee", "country": "USA"}]

affiliations:
["Murdoch University (Australia)", "The University of Western Australia", "Florida State University"]


authors: Yuanlin Wang, Yiyang Zhang, Ruiqin Xiong, Jing Zhao, Jian Zhang, Xiaopeng Fan, Tiejun Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11416-11426
cities: ["Beijing", "Beijing", "Shenzhen", "Harbin"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Spk2SRImgNet_Super-Resolve_Dynamic_Scene_from_Spike_Stream_via_Motion_Aligned_CVPR_2025_paper.html

title:
Spk2SRImgNet: Super-Resolve Dynamic Scene from Spike Stream via Motion Aligned Collaborative Filtering

abstract:
Spike camera is a kind of neuromorphic camera that records dynamic scenes by firing a stream of binary spikes with extremely high temporal resolution. It demonstrates great potential for vision tasks in high-speed scenarios. One limitation in its current implementation is the relatively low spatial resolution. This paper develops a network called Spk2SRImgNet to super-resolve high resolution images from low resolution spike stream. However, fluctuations in spike stream hinder the performance of spike camera super resolution. To address this issue, we propose a motion aligned collaborative filtering (MACF) module, which is motivated by key ideas in classic image restoration schemes to mitigate fluctuations in spike data. MACF leverages the temporal similarity of spike stream to acquire similar features from neighboring moments via motion alignment. To separate disturbances from features, MACF filters these similar features jointly in transform domain to exploit representation sparsity, and generates refinement features that will be used to update initial fluctuated features. Specifically, MACF designs an inverse motion alignment operation to map these refinement features back to their original positions. The initial features are aggregated with the repositioned refinement features to enhance reliability. Experimental results demonstrate that the proposed method achieves state-of-the-art performance compared with existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Spk2SRImgNet_Super-Resolve_Dynamic_Scene_from_Spike_Stream_via_Motion_Aligned_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Spk2SRImgNet_Super-Resolve_Dynamic_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yuanlin and Zhang, Yiyang and Xiong, Ruiqin and Zhao, Jing and Zhang, Jian and Fan, Xiaopeng and Huang, Tiejun},
    title     = {Spk2SRImgNet: Super-Resolve Dynamic Scene from Spike Stream via Motion Aligned Collaborative Filtering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11416-11426}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "National Key Laboratory for Multimedia Information Processing, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University", "city": "Shenzhen", "country": "China"}, {"affiliation": "School of Computer Science and T echnology , Harbin Institute of T echnology", "city": "Harbin", "country": "China"}]

affiliations:
["School of Computer Science, Peking University", "National Key Laboratory for Multimedia Information Processing, Peking University", "School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University", "School of Computer Science and T echnology , Harbin Institute of T echnology"]


authors: Xiangyuan Xue, Zeyu Lu, Di Huang, Zidong Wang, Wanli Ouyang, Lei Bai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24614-24624
cities: ["Shanghai", "Shanghai", "Sydney", "Hong Kong"]
countries: ["China", "China", "Australia", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xue_ComfyBench_Benchmarking_LLM-based_Agents_in_ComfyUI_for_Autonomously_Designing_Collaborative_CVPR_2025_paper.html

title:
ComfyBench: Benchmarking LLM-based Agents in ComfyUI for Autonomously Designing Collaborative AI Systems

abstract:
Much previous AI research has focused on developing monolithic models to maximize their intelligence, with the primary goal of enhancing performance on specific tasks. In contrast, this work attempts to study using LLM-based agents to design collaborative AI systems autonomously. To explore this problem, we first introduce ComfyBench to evaluate agents's ability to design collaborative AI systems in ComfyUI. ComfyBench is a comprehensive benchmark comprising 200 diverse tasks covering various instruction-following generation challenges, along with detailed annotations for 3,205 nodes and 20 workflows. Based on ComfyBench, we further develop ComfyAgent, a novel framework that empowers LLM-based agents to autonomously design collaborative AI systems by generating workflows. ComfyAgent is based on two core concepts. First, it represents workflows with code, which can be reversibly converted into workflows and executed as collaborative systems by the interpreter. Second, it constructs a multi-agent system that cooperates to learn from existing workflows and generate new workflows for a given task. While experimental results demonstrate that ComfyAgent achieves a comparable resolve rate to o1-preview and significantly surpasses other agents on ComfyBench, ComfyAgent has resolved only 15% of creative tasks. LLM-based agents still have a long way to go in autonomously designing collaborative AI systems. Progress with ComfyBench is paving the way for more intelligent and autonomous collaborative AI systems. Our code is available at: https://github.com/xxyQwQ/ComfyBench.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xue_ComfyBench_Benchmarking_LLM-based_Agents_in_ComfyUI_for_Autonomously_Designing_Collaborative_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xue_ComfyBench_Benchmarking_LLM-based_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xue_2025_CVPR,
    author    = {Xue, Xiangyuan and Lu, Zeyu and Huang, Di and Wang, Zidong and Ouyang, Wanli and Bai, Lei},
    title     = {ComfyBench: Benchmarking LLM-based Agents in ComfyUI for Autonomously Designing Collaborative AI Systems},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24614-24624}
}

raw_affiliation:
[{"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "The University of Sydney", "city": "Sydney", "country": "Australia"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["Shanghai Artificial Intelligence Laboratory", "Shanghai Jiao Tong University", "The University of Sydney", "The Chinese University of Hong Kong"]


title: VideoGLaMM : A Large Multimodal Model for Pixel-Level Visual Grounding in Videos
authors: Shehan Munasinghe, Hanan Gani, Wenqi Zhu, Jiale Cao, Eric Xing, Fahad Shahbaz Khan, Salman Khan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19036-19046
cities: ["Abu Dhabi", "Tianjin", "Pittsburgh", "Link\u00f6ping", "Canberra"]
countries: ["UAE", "China", "USA", "Sweden", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Munasinghe_VideoGLaMM__A_Large_Multimodal_Model_for_Pixel-Level_Visual_Grounding_CVPR_2025_paper.html

abstract:
Fine-grained alignment between videos and text is challenging due to complex spatial and temporal dynamics in videos. Existing video-based Large Multimodal Models (LMMs) handle basic conversations but struggle with precise pixel-level grounding in videos. To address this, we introduce VideoGLaMM, a LMM designed for fine-grained pixel-level grounding in videos based on user-provided textual inputs. Our design seamlessly connects three key components: a Large Language Model, a dual vision encoder that emphasizes both spatial and temporal details, and a spatio-temporal decoder for accurate mask generation. This connection is facilitated via tunable V-L and L-V adapters that enable close Vision-Language (VL) alignment. The architecture is trained to synchronize both spatial and temporal elements of video content with textual instructions. To enable fine-grained grounding, we curate a multimodal dataset featuring detailed visually-grounded conversations using a semiautomatic annotation pipeline, resulting in a diverse set of 38k video-QA triplets along with 83k objects and 671k masks. We evaluate VideoGLaMM on three challenging tasks: Grounded Conversation Generation, Visual Grounding, and Referring Video Segmentation. Experimental results show that our model consistently outperforms existing approaches across all three tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Munasinghe_VideoGLaMM__A_Large_Multimodal_Model_for_Pixel-Level_Visual_Grounding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Munasinghe_VideoGLaMM__A_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Munasinghe_2025_CVPR,
    author    = {Munasinghe, Shehan and Gani, Hanan and Zhu, Wenqi and Cao, Jiale and Xing, Eric and Khan, Fahad Shahbaz and Khan, Salman},
    title     = {VideoGLaMM : A Large Multimodal Model for Pixel-Level Visual Grounding in Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19036-19046}
}

raw_affiliation:
[{"affiliation": "Mohamed bin Zayed University of AI", "city": "Abu Dhabi", "country": "UAE"}, {"affiliation": "Tianjin University", "city": "Tianjin", "country": "China"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "Link\u00f6ping University", "city": "Link\u00f6ping", "country": "Sweden"}, {"affiliation": "Australian National University", "city": "Canberra", "country": "Australia"}]

affiliations:
["Mohamed bin Zayed University of AI", "Tianjin University", "Carnegie Mellon University", "Link\u00f6ping University", "Australian National University"]


authors: Xu Yan, Jun Yin, Jie Wen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30722-30731
affiliations: ["Shanghai Maritime University", "Harbin Institute of Technology, Shenzhen"]
cities: ["Shanghai", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yan_Incomplete_Multi-View_Multi-label_Learning_via_Disentangled_Representation_and_Label_Semantic_CVPR_2025_paper.html

title:
Incomplete Multi-View Multi-label Learning via Disentangled Representation and Label Semantic Embedding

abstract:
In incomplete multi-view multi-label learning scenarios, it is crucial to use the incomplete multi-view data to extract consistent and specific representations from different data sources and to fully exploit the missing label information. However, most previous approaches ignore the separation problem between view-shared and specific information. To address this problem, in this paper, we propose a method that can separate view-consistent features from view-specific features under the Variational Autoencoder (VAE) framework. Specifically, we first introduce cross-view reconstruction to capture view-consistent features and extract shared information from different views through unsupervised pre-training. Subsequently, we develop a disentangling module to learn specific features by minimizing the variational upper bound of mutual information between consistent and specific features. Finally, we utilize prior label relevance information derived from training data to guide the learning of the distribution of label semantic embeddings, aggregating relevant semantic embeddings and maintaining the label relevance topology in the semantic space. In extensive experiments, our model outperforms existing state-of-the-art algorithms on several real-world datasets, which fully validates its strong adaptability to missing views and labels.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Incomplete_Multi-View_Multi-label_Learning_via_Disentangled_Representation_and_Label_Semantic_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yan_Incomplete_Multi-View_Multi-label_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yan_2025_CVPR,
    author    = {Yan, Xu and Yin, Jun and Wen, Jie},
    title     = {Incomplete Multi-View Multi-label Learning via Disentangled Representation and Label Semantic Embedding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30722-30731}
}

raw_affiliation:
[{"affiliation": "Shanghai Maritime University", "city": "Shanghai", "country": "China"}, {"affiliation": "Harbin Institute of Technology, Shenzhen", "city": "Shenzhen", "country": "China"}]


title: AutoURDF: Unsupervised Robot Modeling from Point Cloud Frames Using Cluster Registration
authors: Jiong Lin, Lechen Zhang, Kwansoo Lee, Jialong Ning, Judah Goldfeder, Hod Lipson
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27628-27637
affiliations: ["Columbia University, Creative Machines Lab"]
cities: ["New York"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_AutoURDF_Unsupervised_Robot_Modeling_from_Point_Cloud_Frames_Using_Cluster_CVPR_2025_paper.html

abstract:
Robot description models are essential for simulation and control, yet their creation often requires significant manual effort. To streamline this modeling process, we introduce AutoURDF, an unsupervised approach for constructing description files for unseen robots from point cloud frames. Our method leverages a cluster-based point cloud registration model that tracks the 6-DoF transformations of point clusters. Through analyzing cluster movements, we hierarchically address the following challenges: (1) moving part segmentation, (2) body topology inference, and (3) joint parameter estimation. The complete pipeline produces robot description files that are fully compatible with existing simulators. We validate our method across a variety of robots, using both synthetic and real-world scan data. Results indicate that our approach outperforms previous methods in registration and body topology estimation accuracy, offering a scalable solution for automated robot modeling.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_AutoURDF_Unsupervised_Robot_Modeling_from_Point_Cloud_Frames_Using_Cluster_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_AutoURDF_Unsupervised_Robot_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Jiong and Zhang, Lechen and Lee, Kwansoo and Ning, Jialong and Goldfeder, Judah and Lipson, Hod},
    title     = {AutoURDF: Unsupervised Robot Modeling from Point Cloud Frames Using Cluster Registration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27628-27637}
}

raw_affiliation:
[{"affiliation": "Columbia University, Creative Machines Lab", "city": "New York", "country": "USA"}]


title: ZeroGrasp: Zero-Shot Shape Reconstruction Enabled Robotic Grasping
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17405-17415
affiliations: ["Carnegie Mellon University", "Toyota Research Institute", "Woven by Toyota"]
cities: ["Pittsburgh", "Los Altos", "Tokyo"]
countries: ["USA", "USA", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Iwase_ZeroGrasp_Zero-Shot_Shape_Reconstruction_Enabled_Robotic_Grasping_CVPR_2025_paper.html

authors:
Shun Iwase, Muhammad Zubair Irshad, Katherine Liu, Vitor Guizilini, Robert Lee, Takuya Ikeda, Ayako Amma, Koichi Nishiwaki, Kris Kitani, Rares Ambrus, Sergey Zakharov

abstract:
Robotic grasping is a cornerstone capability of embodied systems. Many methods directly output grasps from partial information without modeling the geometry of the scene, leading to suboptimal motion and even collisions. To address these issues, we introduce ZeroGrasp, a novel framework that simultaneously performs 3D reconstruction and grasp pose prediction in near real-time. A key insight of our method is that occlusion reasoning and modeling the spatial relationships between objects is beneficial for both accurate reconstruction and grasping. We couple our method with a novel large-scale synthetic dataset, which comprises 1M photo-realistic images, high-resolution 3D reconstructions and 11.3B physically-valid grasp pose annotations for 12K objects from the Objaverse-LVIS dataset. We evaluate ZeroGrasp on the GraspNet-1B benchmark as well as through real-world robot experiments. ZeroGrasp achieves state-of-the-art performance and generalizes to novel real-world objects by leveraging synthetic data.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Iwase_ZeroGrasp_Zero-Shot_Shape_Reconstruction_Enabled_Robotic_Grasping_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Iwase_ZeroGrasp_Zero-Shot_Shape_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Iwase_2025_CVPR,
    author    = {Iwase, Shun and Irshad, Muhammad Zubair and Liu, Katherine and Guizilini, Vitor and Lee, Robert and Ikeda, Takuya and Amma, Ayako and Nishiwaki, Koichi and Kitani, Kris and Ambrus, Rares and Zakharov, Sergey},
    title     = {ZeroGrasp: Zero-Shot Shape Reconstruction Enabled Robotic Grasping},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17405-17415}
}

raw_affiliation:
[{"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "Toyota Research Institute", "city": "Los Altos", "country": "USA"}, {"affiliation": "Woven by Toyota", "city": "Tokyo", "country": "Japan"}]


title: Golden Cudgel Network for Real-Time Semantic Segmentation
authors: Guoyu Yang, Yuan Wang, Daming Shi, Yanzhong Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25367-25376
affiliations: ["Shenzhen University", "Zhejiang University of Technology"]
cities: ["Shenzhen", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Golden_Cudgel_Network_for_Real-Time_Semantic_Segmentation_CVPR_2025_paper.html

abstract:
Recent real-time semantic segmentation models, whether single-branch or multi-branch, achieve good performance and speed. However, their speed is limited by multi-path blocks, and some depend on high-performance teacher models for training. To overcome these issues, we propose Golden Cudgel Network (GCNet). Specifically, GCNet uses vertical multi-convolutions and horizontal multi-paths for training, which are reparameterized into a single convolution for inference, optimizing both performance and speed. This design allows GCNet to self-enlarge during training and self-contract during inference, effectively becoming a "teacher model" without needing external ones. Experimental results show that GCNet outperforms existing state-of-the-art models in terms of performance and speed on the Cityscapes, CamVid, and Pascal VOC 2012 datasets. The code is available at https://github.com/gyyang23/GCNet.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Golden_Cudgel_Network_for_Real-Time_Semantic_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Golden_Cudgel_Network_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Guoyu and Wang, Yuan and Shi, Daming and Wang, Yanzhong},
    title     = {Golden Cudgel Network for Real-Time Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25367-25376}
}

raw_affiliation:
[{"affiliation": "Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Zhejiang University of Technology", "city": "Hangzhou", "country": "China"}]


title: PDFactor: Learning Tri-Perspective View Policy Diffusion Field for Multi-Task Robotic Manipulation
authors: Jingyi Tian, Le Wang, Sanping Zhou, Sen Wang, Jiayi Li, Haowen Sun, Wei Tang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15757-15767
cities: ["Xi'an", "Chicago"]
countries: ["China", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tian_PDFactor_Learning_Tri-Perspective_View_Policy_Diffusion_Field_for_Multi-Task_Robotic_CVPR_2025_paper.html

abstract:
Robotic manipulation based on visual observations and natural language instructions is a long-standing challenge in robotics. Yet prevailing approaches model action distribution by adopting explicit or implicit representations, which often struggle to achieve a trade-off between accuracy and efficiency. In response, we propose PDFactor, a novel framework that models action distribution with a hybrid triplane representation. In particular, PDFactor decomposes 3D point cloud into three orthogonal feature planes and leverages a tri-perspective view transformer to produce dense cubic features as a latent diffusion field aligned with observation space representing 6-DoF action probability distribution at an arbitrary location. We employ a small denoising network conceptually as both a parameterized loss function measuring the quality of the learned latent features and an action gradient decoder to sample actions from the latent diffusion field during inference. This design enables our PDFactor to benefit from spatial awareness of explicit representation and arbitrary resolution of implicit representation, rendering it with manipulation accuracy, inference efficiency, and model scalability. Experiments demonstrate that PDFactor outperforms state-of-the-art approaches across a diverse range of manipulation tasks in RLBench simulation. Moreover, PDFactor can effectively learn multi-task policies from a limited number of human demonstrations, achieving promising accuracy in a variety of real-world manipulation tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tian_PDFactor_Learning_Tri-Perspective_View_Policy_Diffusion_Field_for_Multi-Task_Robotic_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tian_PDFactor_Learning_Tri-Perspective_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tian_2025_CVPR,
    author    = {Tian, Jingyi and Wang, Le and Zhou, Sanping and Wang, Sen and Li, Jiayi and Sun, Haowen and Tang, Wei},
    title     = {PDFactor: Learning Tri-Perspective View Policy Diffusion Field for Multi-Task Robotic Manipulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15757-15767}
}

raw_affiliation:
[{"affiliation": "National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "University of Illinois at Chicago", "city": "Chicago", "country": "USA"}]

affiliations:
["National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, Institute of Artificial Intelligence and Robotics, Xi\u2019an Jiaotong University", "University of Illinois at Chicago"]


title: VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long Videos
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3272-3283
raw_affiliation: [{"affiliation": "UNC Chapel Hill", "city": "Chapel Hill", "country": "USA"}]
affiliations: ["UNC Chapel Hill"]
cities: ["Chapel Hill"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_VideoTree_Adaptive_Tree-based_Video_Representation_for_LLM_Reasoning_on_Long_CVPR_2025_paper.html

authors:
Ziyang Wang, Shoubin Yu, Elias Stengel-Eskin, Jaehong Yoon, Feng Cheng, Gedas Bertasius, Mohit Bansal

abstract:
Long-form video understanding has been a challenging task due to the high redundancy in video data and the abundance of query-irrelevant information. To tackle this challenge, we propose VideoTree, a training-free framework which builds a query-adaptive and hierarchical video representation for LLM reasoning over long-form videos. First, VideoTree extracts query-relevant information from the input video through an iterative process, progressively refining the selection of keyframes based on their relevance to the query. Furthermore, VideoTree leverages the inherent hierarchical structure of long video data, which is often overlooked by existing LLM-based methods. Specifically, we incorporate multi-granularity information into a tree-based representation, allowing VideoTree to extract query-relevant details from long videos in a coarse-to-fine manner. This enables the model to effectively handle a wide range of video queries with varying levels of detail. Finally, VideoTree aggregates the hierarchical query-relevant information within the tree structure and feeds it into an LLM reasoning model to answer the query. Our experiments show that VideoTree improves both reasoning accuracy and efficiency compared to existing methods. Specifically, VideoTree outperforms the existing training-free approaches on the popular EgoSchema and NExT-QA benchmarks with less inference time, achieving 61.1% and 75.6% accuracy on the test set without additional video-specific training. Moreover, on the long split of Video-MME benchmark (average 44 minutes), the training-free VideoTree framework achieves better performance than the strong proprietary GPT-4V model and many other MLLMs that were extensively trained on video data. Our code is provided in the supplementary materials and will be made public.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_VideoTree_Adaptive_Tree-based_Video_Representation_for_LLM_Reasoning_on_Long_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_VideoTree_Adaptive_Tree-based_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Ziyang and Yu, Shoubin and Stengel-Eskin, Elias and Yoon, Jaehong and Cheng, Feng and Bertasius, Gedas and Bansal, Mohit},
    title     = {VideoTree: Adaptive Tree-based Video Representation for LLM Reasoning on Long Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3272-3283}
}


title: Multi-modal Contrastive Learning with Negative Sampling Calibration for Phenotypic Drug Discovery
authors: Jiahua Rao, Hanjing Lin, Leyu Chen, Jiancong Xie, Shuangjia Zheng, Yuedong Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30752-30762
cities: ["Guangzhou", "Shanghai", "Guangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Rao_Multi-modal_Contrastive_Learning_with_Negative_Sampling_Calibration_for_Phenotypic_Drug_CVPR_2025_paper.html

abstract:
Phenotypic drug discovery presents a promising strategy for identifying first-in-class drugs by bypassing the need for specific drug targets. Recent advances in cell-based phenotypic screening tools, including Cell Painting and the LINCS L1000, provide essential cellular data that capture biological responses to compounds. While the integration of the multi-modal data enhances the use of contrastive learning (CL) methods for molecular phenotypic representation, these approaches treat all negative pairs equally, failing to discriminate molecules with similar phenotypes. To address these challenges, we introduce a foundational framework MINER that dynamically estimates the likelihoods of sample pairs as negative pairs based on uni-modal disentangled representations. In addition, our approach incorporates a mixture fusion strategy to effectively integrate multimodal data, even in cases where certain modalities are missing.  Extensive experiments demonstrate that our method enhances both molecular property prediction and molecule-phenotype retrieval accuracy. Moreover, it successfully recommends drug candidates from phenotype for complex diseases documented in the literature.  These findings underscore MINER's potential to advance drug discovery by enabling deeper insights into disease mechanisms and improving drug candidate recommendations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Rao_Multi-modal_Contrastive_Learning_with_Negative_Sampling_Calibration_for_Phenotypic_Drug_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Rao_Multi-modal_Contrastive_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Rao_2025_CVPR,
    author    = {Rao, Jiahua and Lin, Hanjing and Chen, Leyu and Xie, Jiancong and Zheng, Shuangjia and Yang, Yuedong},
    title     = {Multi-modal Contrastive Learning with Negative Sampling Calibration for Phenotypic Drug Discovery},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30752-30762}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Global Institute of Future Technology, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-sen University", "city": "Guangzhou", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, Sun Yat-sen University", "Global Institute of Future Technology, Shanghai Jiao Tong University", "Key Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-sen University"]


title: R-TPT: Improving Adversarial Robustness of Vision-Language Models through Test-Time Prompt Tuning
authors: Lijun Sheng, Jian Liang, Zilei Wang, Ran He
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29958-29967
cities: ["Hefei", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sheng_R-TPT_Improving_Adversarial_Robustness_of_Vision-Language_Models_through_Test-Time_Prompt_CVPR_2025_paper.html

abstract:
Vision-language models (VLMs), such as CLIP, have gained significant popularity as foundation models, with numerous fine-tuning methods developed to enhance performance on downstream tasks. However, due to their inherent vulnerability and the common practice of selecting from a limited set of open-source models, VLMs suffer from a higher risk of adversarial attacks than traditional vision models. Existing defense techniques typically rely on adversarial fine-tuning during training, which requires labeled data and lacks of flexibility for downstream tasks. To address these limitations, we propose robust test-time prompt tuning (R-TPT), which mitigates the impact of adversarial attacks during the inference stage. We first reformulate the classic marginal entropy objective by eliminating the term that introduces conflicts under adversarial conditions, retaining only the pointwise entropy minimization. Furthermore, we introduce a plug-and-play reliability-based weighted ensembling strategy, which aggregates useful information from reliable augmented views to strengthen the defense. R-TPT enhances defense against adversarial attacks without requiring labeled training data while offering high flexibility for inference tasks. Extensive experiments on widely used benchmarks with various attacks demonstrate the effectiveness of R-TPT.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sheng_R-TPT_Improving_Adversarial_Robustness_of_Vision-Language_Models_through_Test-Time_Prompt_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sheng_R-TPT_Improving_Adversarial_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sheng_2025_CVPR,
    author    = {Sheng, Lijun and Liang, Jian and Wang, Zilei and He, Ran},
    title     = {R-TPT: Improving Adversarial Robustness of Vision-Language Models through Test-Time Prompt Tuning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29958-29967}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "NLPR & MAIS, Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["University of Science and Technology of China", "NLPR & MAIS, Institute of Automation, Chinese Academy of Sciences", "University of Chinese Academy of Sciences"]


authors: Weiming Liu, Jun Dan, Fan Wang, Xinting Liao, Junhao Dong, Hua Yu, Shunjie Dong, Lianyong Qi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4927-4938
cities: ["Beijing", "Hangzhou", "Singapore", "Shanghai", "Qingdao", "Qingdao"]
countries: ["China", "China", "Singapore", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Distinguish_Then_Exploit_Source-free_Open_Set_Domain_Adaptation_via_Weight_CVPR_2025_paper.html

title:
Distinguish Then Exploit: Source-free Open Set Domain Adaptation via Weight Barcode Estimation and Sparse Label Assignment

abstract:
Nowadays, domain adaptation techniques have been widely investigated for knowledge sharing from labeled source domain to unlabeled target domain. However, target domain may include some data samples that belong to unknown categories in real-world scenarios. Moreover, the target domain cannot access the source data samples due to privacy-preserving restrictions. In this paper, we focus on the source-free open set domain adaptation problem which includes two main challenges, i.e.,how to distinguish known and unknown target samples and how to exploit useful source information to provide trustworthy pseudo labels for known target samples. Existing approaches that directly applying conventional domain alignment methods could lead to sample mismatch and misclassification in this scenario.To overcome these issues, we propose Distinguish Then Exploit model (DTE) with two components, i.e., weight barcode estimation and sparse label assignment. Weight barcode estimation first calculate marginal probability of target samples via partially unbalanced optimal transport then quantize barcode results to distinguish unknown target sample. Sparse label assignment utilizes sparse sample-label matching via proximal term to fully exploit useful source information. Our empirically study on several datasets shows that DTE outperforms the state-of-the-art models on tackling the source-free open set domain adaptation problem.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Distinguish_Then_Exploit_Source-free_Open_Set_Domain_Adaptation_via_Weight_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Distinguish_Then_Exploit_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Weiming and Dan, Jun and Wang, Fan and Liao, Xinting and Dong, Junhao and Yu, Hua and Dong, Shunjie and Qi, Lianyong},
    title     = {Distinguish Then Exploit: Source-free Open Set Domain Adaptation via Weight Barcode Estimation and Sparse Label Assignment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4927-4938}
}

raw_affiliation:
[{"affiliation": "Bytedance Inc.", "city": "Beijing", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "China University of Petroleum (East China)", "city": "Qingdao", "country": "China"}, {"affiliation": "Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software", "city": "Qingdao", "country": "China"}]

affiliations:
["Bytedance Inc.", "Zhejiang University", "Nanyang Technological University", "Shanghai Jiao Tong University", "China University of Petroleum (East China)", "Shandong Key Laboratory of Intelligent Oil & Gas Industrial Software"]


title: Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Properties
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9984-9993
affiliations: ["ShanghaiTech University", "University of Michigan, Ann Arbor", "The University of Hong Kong"]
cities: ["Shanghai", "Ann Arbor", "Hong Kong"]
countries: ["China", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Multi-Sensor_Object_Anomaly_Detection_Unifying_Appearance_Geometry_and_Internal_Properties_CVPR_2025_paper.html

authors:
Wenqiao Li, Bozhong Zheng, Xiaohao Xu, Jinye Gan, Fading Lu, Xiang Li, Na Ni, Zheng Tian, Xiaonan Huang, Shenghua Gao, Yingna Wu

abstract:
Object anomaly detection is essential for industrial quality inspection, yet traditional single-sensor methods face critical limitations. They fail to capture the wide range of anomaly types, as single sensors are often constrained to either external appearance, geometric structure, or internal properties. To overcome these challenges, we introduce MulSen-AD, the first high-resolution, multi-sensor anomaly detection dataset tailored for industrial applications. MulSen-AD unifies data from RGB cameras, laser scanners, and lock-in infrared thermography, effectively capturing external appearance, geometric deformations, and internal defects. The dataset spans 15 industrial products with diverse, real-world anomalies. We also present MulSen-AD Bench, a benchmark designed to evaluate multi-sensor methods, and propose MulSen-TripleAD, a decision-level fusion algorithm that integrates these three modalities for robust, unsupervised object anomaly detection. Our experiments demonstrate that multi-sensor fusion substantially outperforms single-sensor approaches, achieving 96.1% AUROC in object-level detection accuracy. These results highlight the importance of integrating multi-sensor data for comprehensive industrial anomaly detection. The dataset and code are available at https://github.com/ZZZBBBZZZ/MulSen-AD to support further research.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Multi-Sensor_Object_Anomaly_Detection_Unifying_Appearance_Geometry_and_Internal_Properties_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Multi-Sensor_Object_Anomaly_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Wenqiao and Zheng, Bozhong and Xu, Xiaohao and Gan, Jinye and Lu, Fading and Li, Xiang and Ni, Na and Tian, Zheng and Huang, Xiaonan and Gao, Shenghua and Wu, Yingna},
    title     = {Multi-Sensor Object Anomaly Detection: Unifying Appearance, Geometry, and Internal Properties},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9984-9993}
}

raw_affiliation:
[{"affiliation": "ShanghaiTech University", "city": "Shanghai", "country": "China"}, {"affiliation": "University of Michigan, Ann Arbor", "city": "Ann Arbor", "country": "USA"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}]


title: SplatFlow: Multi-View Rectified Flow Model for 3D Gaussian Splatting Synthesis
authors: Hyojun Go, Byeongjun Park, Jiho Jang, Jin-Young Kim, Soonwoo Kwon, Changick Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21524-21536
affiliations: ["EverEx", "KAIST"]
cities: ["Unknown", "Daejeon"]
countries: ["Unknown", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Go_SplatFlow_Multi-View_Rectified_Flow_Model_for_3D_Gaussian_Splatting_Synthesis_CVPR_2025_paper.html

abstract:
Text-based generation and editing of 3D scenes hold significant potential for streamlining content creation through intuitive user interactions. While recent advances leverage 3D Gaussian Splatting (3DGS) for high-fidelity and real-time rendering, existing methods are often specialized and task-focused, lacking a unified framework for both generation and editing. In this paper, we introduce SplatFlow, a comprehensive framework that addresses this gap by enabling direct 3DGS generation and editing. SplatFlow comprises two main components: a multi-view rectified flow (RF) model and a Gaussian Splatting Decoder (GSDecoder). The multi-view RF model operates in latent space, generating multi-view images, depths, and camera poses simultaneously, conditioned on text prompts--thus addressing challenges like diverse scene scales and complex camera trajectories in real-world settings. Then, the GSDecoder efficiently translates these latent outputs into 3DGS representations through a feed-forward 3DGS method.  Leveraging training-free inversion and inpainting techniques, SplatFlow enables seamless 3DGS editing and supports a broad range of 3D tasks--including object editing, novel view synthesis, and camera pose estimation--within a unified framework without requiring additional complex pipelines. We validate SplatFlow's capabilities on the MVImgNet and DL3DV-7K datasets, demonstrating its versatility and effectiveness in various 3D generation, editing, and inpainting-based tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Go_SplatFlow_Multi-View_Rectified_Flow_Model_for_3D_Gaussian_Splatting_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Go_SplatFlow_Multi-View_Rectified_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Go_2025_CVPR,
    author    = {Go, Hyojun and Park, Byeongjun and Jang, Jiho and Kim, Jin-Young and Kwon, Soonwoo and Kim, Changick},
    title     = {SplatFlow: Multi-View Rectified Flow Model for 3D Gaussian Splatting Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21524-21536}
}

raw_affiliation:
[{"affiliation": "EverEx", "city": "Unknown", "country": "Unknown"}, {"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}]


title: Fancy123: One Image to High-Quality 3D Mesh Generation via Plug-and-Play Deformation
authors: Qiao Yu, Xianzhi Li, Yuan Tang, Xu Han, Long Hu, Yixue Hao, Min Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 595-604
cities: ["Wuhan", "Guangzhou", "Guangzhou", "Guangzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_Fancy123_One_Image_to_High-Quality_3D_Mesh_Generation_via_Plug-and-Play_CVPR_2025_paper.html

abstract:
Generating 3D meshes from a single image is an important but ill-posed task. Existing methods mainly adopt 2D multiview diffusion models to generate intermediate multiview images, and use the Large Reconstruction Model (LRM) to create the final meshes. However, the multiview images exhibit local inconsistencies, and the meshes often lack fidelity to the input image or look blurry. We propose Fancy123, featuring two enhancement modules and an unprojection operation to address the above three issues, respectively. The appearance enhancement module deforms the 2D multiview images to realign misaligned pixels for better multiview consistency. The fidelity enhancement module deforms the 3D mesh to match the input image. The unprojection of the input image and deformed multiview images onto LRM's generated mesh ensures high clarity, discarding LRM's predicted blurry-looking mesh colors. Extensive qualitative and quantitative experiments verify Fancy123's SoTA performance with significant improvement. Also, the two enhancement modules are plug-and-play and work at inference time, allowing seamless integration into various existing single-image-to-3D methods. Project page: https://github.com/YuQiao0303/Fancy123.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Fancy123_One_Image_to_High-Quality_3D_Mesh_Generation_via_Plug-and-Play_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_Fancy123_One_Image_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Qiao and Li, Xianzhi and Tang, Yuan and Han, Xu and Hu, Long and Hao, Yixue and Chen, Min},
    title     = {Fancy123: One Image to High-Quality 3D Mesh Generation via Plug-and-Play Deformation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {595-604}
}

raw_affiliation:
[{"affiliation": "Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "Guangdong Intelligent Robotics Institute", "city": "Guangzhou", "country": "China"}, {"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Pazhou Laboratory", "city": "Guangzhou", "country": "China"}]

affiliations:
["Huazhong University of Science and Technology", "Guangdong Intelligent Robotics Institute", "South China University of Technology", "Pazhou Laboratory"]


title: Dense Dispersed Structured Light for Hyperspectral 3D Imaging of Dynamic Scenes
authors: Suhyun Shin, Seungwoo Yoon, Ryota Maeda, Seung-Hwan Baek
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16589-16598
affiliations: ["POSTECH", "POSTECH", "POSTECH, University of Hyogo", "POSTECH"]
cities: ["Pohang", "Pohang", "Pohang, Kobe", "Pohang"]
countries: ["South Korea", "South Korea", "South Korea, Japan", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shin_Dense_Dispersed_Structured_Light_for_Hyperspectral_3D_Imaging_of_Dynamic_CVPR_2025_paper.html

abstract:
Hyperspectral 3D imaging captures both depth maps and hyperspectral images, enabling comprehensive geometric and material analysis. Recent methods achieve high spectral and depth accuracy; however, they require long acquisition times--often over several minutes--or rely on large, expensive systems, restricting their use to static scenes. We present Dense Dispersed Structured Light (DDSL), an accurate hyperspectral 3D imaging method for dynamic scenes that utilizes stereo RGB cameras and an RGB projector equipped with an affordable diffraction grating film. We design spectrally multiplexed DDSL patterns that significantly reduce the number of required projector patterns, thereby accelerating acquisition speed. Additionally, we formulate an image formation model and a reconstruction method to estimate a hyperspectral image and depth map from captured stereo images. As the first practical and accurate hyperspectral 3D imaging method for dynamic scenes, we experimentally demonstrate that DDSL achieves a spectral resolution of 15.5 nm full width at half maximum (FWHM), 4 mm depth error, and 6.6 fps.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shin_Dense_Dispersed_Structured_Light_for_Hyperspectral_3D_Imaging_of_Dynamic_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shin_Dense_Dispersed_Structured_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Shin_2025_CVPR,
    author    = {Shin, Suhyun and Yoon, Seungwoo and Maeda, Ryota and Baek, Seung-Hwan},
    title     = {Dense Dispersed Structured Light for Hyperspectral 3D Imaging of Dynamic Scenes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16589-16598}
}

raw_affiliation:
[{"affiliation": "POSTECH", "city": "Pohang", "country": "South Korea"}, {"affiliation": "POSTECH", "city": "Pohang", "country": "South Korea"}, {"affiliation": "POSTECH, University of Hyogo", "city": "Pohang, Kobe", "country": "South Korea, Japan"}, {"affiliation": "POSTECH", "city": "Pohang", "country": "South Korea"}]


title: PlanarSplatting: Accurate Planar Surface Reconstruction in 3 Minutes
authors: Bin Tan, Rui Yu, Yujun Shen, Nan Xue
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1190-1199
affiliations: ["Ant Group", "University of Louisville"]
cities: ["Hangzhou", "Louisville"]
countries: ["China", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tan_PlanarSplatting_Accurate_Planar_Surface_Reconstruction_in_3_Minutes_CVPR_2025_paper.html

abstract:
This paper presents PlanarSplatting, an ultra-fast and accurate surface reconstruction approach for multi-view indoor images. We take the 3D planes as the main objective due to their compactness and structural expressiveness in indoor scenes, and develop an explicit optimization framework that learns to fit the expected surface of indoor scenes by splatting the 3D planes into 2.5D depth and normal maps. As our PlanarSplatting operates directly on the 3D plane primitives, it eliminates the dependencies on 2D/3D plane detection and plane matching/tracking for planar surface reconstruction. Furthermore, with the essential merits of plane-based representation coupled with CUDA-based implementation of planar splatting functions, PlanarSplatting reconstructs an indoor scene in 3 minutes while having significantly better geometric accuracy. Thanks to our ultra-fast reconstruction speed, the largest quantitative evaluation on the ScanNet and ScanNet++ datasets over hundreds of scenes clearly demonstrated the advantages of our method. We believe that our accurate and ultrafast planar surface reconstruction method will be applied in the structured data curation for surface reconstruction in the future. The code of our CUDA implementation will be publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tan_PlanarSplatting_Accurate_Planar_Surface_Reconstruction_in_3_Minutes_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tan_PlanarSplatting_Accurate_Planar_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Tan_2025_CVPR,
    author    = {Tan, Bin and Yu, Rui and Shen, Yujun and Xue, Nan},
    title     = {PlanarSplatting: Accurate Planar Surface Reconstruction in 3 Minutes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1190-1199}
}

raw_affiliation:
[{"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "University of Louisville", "city": "Louisville", "country": "USA"}]


title: Omni-ID: Holistic Identity Representation Designed for Generative Tasks
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8786-8795
raw_affiliation: [{"affiliation": "Snap Research", "city": "Menlo Park", "country": "USA"}]
affiliations: ["Snap Research"]
cities: ["Menlo Park"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qian_Omni-ID_Holistic_Identity_Representation_Designed_for_Generative_Tasks_CVPR_2025_paper.html

authors:
Guocheng Qian, Kuan-Chieh Wang, Or Patashnik, Negin Heravi, Daniil Ostashev, Sergey Tulyakov, Daniel Cohen-Or, Kfir Aberman

abstract:
We introduce Omni-ID, a novel facial representation designed specifically for generative tasks. Omni-ID encodes holistic information about an individual's appearance across diverse expressions and poses within a fixed-size representation. It consolidates information from a varied number of unstructured input images into a structured representation, where each entry represents certain global or local identity features. Our approach uses a few-to-many identity reconstruction training paradigm, where a limited set of input images is used to reconstruct multiple target images of the same individual in various poses and expressions. A multi-decoder framework is introduced to leverage the complementary strengths of diverse decoders during training. Unlike conventional representations, such as ArcFace and CLIP, which are typically learned through discriminative or contrastive objectives, Omni-ID is optimized with a generative objective, resulting in a more comprehensive and nuanced identity capture for generative tasks. Trained on our MFHQ dataset -- a multi-view facial image collection, Omni-ID demonstrates substantial improvements over conventional representations across various generative tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qian_Omni-ID_Holistic_Identity_Representation_Designed_for_Generative_Tasks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qian_Omni-ID_Holistic_Identity_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qian_2025_CVPR,
    author    = {Qian, Guocheng and Wang, Kuan-Chieh and Patashnik, Or and Heravi, Negin and Ostashev, Daniil and Tulyakov, Sergey and Cohen-Or, Daniel and Aberman, Kfir},
    title     = {Omni-ID: Holistic Identity Representation Designed for Generative Tasks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8786-8795}
}


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19378-19389
affiliations: ["Technical University of Munich", "Munich Center for Machine Learning"]
cities: ["Munich", "Munich"]
countries: ["Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ozsoy_MM-OR_A_Large_Multimodal_Operating_Room_Dataset_for_Semantic_Understanding_CVPR_2025_paper.html

title:
MM-OR: A Large Multimodal Operating Room Dataset for Semantic Understanding of High-Intensity Surgical Environments

authors:
Ege Özsoy, Chantal Pellegrini, Tobias Czempiel, Felix Tristram, Kun Yuan, David Bani-Harouni, Ulrich Eck, Benjamin Busam, Matthias Keicher, Nassir Navab

abstract:
Operating rooms (ORs) are complex, high-stakes environments requiring precise understanding of interactions among medical staff, tools, and equipment for enhancing surgical assistance, situational awareness, and patient safety. Current datasets fall short in scale, realism and do not capture the multimodal nature of OR scenes, limiting progress in OR modeling. To this end, we introduce MM-OR, a realistic and large-scale multimodal spatiotemporal OR dataset, and the first dataset to enable multimodal scene graph generation. MM-OR captures comprehensive OR scenes containing RGB-D data, detail views, audio, speech transcripts, robotic logs, and tracking data and is annotated with panoptic segmentations, semantic scene graphs, and downstream task labels. Further, we propose MM2SG, the first multimodal large vision-language model for scene graph generation, and through extensive experiments, demonstrate its ability to effectively leverage multimodal inputs. Together, MM-OR and MM2SG establish a new benchmark for holistic OR understanding, and open the path towards multimodal scene analysis in complex, high-stakes environments. We will publish all our code and dataset upon acceptance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ozsoy_MM-OR_A_Large_Multimodal_Operating_Room_Dataset_for_Semantic_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ozsoy_MM-OR_A_Large_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Ozsoy_2025_CVPR,
    author    = {\"Ozsoy, Ege and Pellegrini, Chantal and Czempiel, Tobias and Tristram, Felix and Yuan, Kun and Bani-Harouni, David and Eck, Ulrich and Busam, Benjamin and Keicher, Matthias and Navab, Nassir},
    title     = {MM-OR: A Large Multimodal Operating Room Dataset for Semantic Understanding of High-Intensity Surgical Environments},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19378-19389}
}

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


title: MIRE: Matched Implicit Neural Representations
authors: Dhananjaya Jayasundara, Heng Zhao, Demetrio Labate, Vishal M. Patel
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8279-8288
affiliations: ["Johns Hopkins University", "The Rockefeller University", "University of Houston"]
cities: ["Baltimore", "New York", "Houston"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jayasundara_MIRE_Matched_Implicit_Neural_Representations_CVPR_2025_paper.html

abstract:
Implicit Neural Representations (INRs) are continuous function learners for conventional digital signal representations. With the aid of positional embeddings and/or exhaustively fine-tuned activation functions, INRs have surpassed many limitations of traditional discrete representations. However, existing works only find a continuous representation for the digital signal by solely using a single, fixed activation function throughout the INR, and it has not yet been explored to match the INR to the given signal. As current INRs are not matched to the signal being represented by the INR, we hypothesize that this approach could restrict the representation power and generalization capabilities of INRs, limiting their broader applicability. A way to match the INR to the signal being represented is through matching the activation of each layer in the sense of minimizing the mean squared loss. In this paper, we introduce MIRE, a method to find the highly matched activation function for each layer in INR through dictionary learning. To showcase the effectiveness of the proposed method, we utilize a dictionary that includes seven activation atoms: Raised Cosines (RC), Root Raised Cosines (RRC), Prolate Spheroidal Wave Function (PSWF), Sinc, Gabor Wavelet, Gaussian, and Sinusoidal. Experimental results demonstrate that MIRE not only significantly improves INR performance across various tasks, such as image representation, image inpainting, 3D shape representation, novel view synthesis, super-resolution, and reliable edge detection, but also eliminates the need for the previously required exhaustive search for activation parameters, which had to be conducted even before INR training could begin.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jayasundara_MIRE_Matched_Implicit_Neural_Representations_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jayasundara_MIRE_Matched_Implicit_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jayasundara_2025_CVPR,
    author    = {Jayasundara, Dhananjaya and Zhao, Heng and Labate, Demetrio and Patel, Vishal M.},
    title     = {MIRE: Matched Implicit Neural Representations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8279-8288}
}

raw_affiliation:
[{"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}, {"affiliation": "The Rockefeller University", "city": "New York", "country": "USA"}, {"affiliation": "University of Houston", "city": "Houston", "country": "USA"}]


title: AeSPa : Attention-guided Self-supervised Parallel Imaging for MRI Reconstruction
authors: Jinho Joo, Hyeseong Kim, Hyeyeon Won, Deukhee Lee, Taejoon Eo, Dosik Hwang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5217-5226
affiliations: ["Yonsei University", "Korea Institute of Science and Technology"]
cities: ["Seoul", "Seoul"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Joo_AeSPa__Attention-guided_Self-supervised_Parallel_Imaging_for_MRI_Reconstruction_CVPR_2025_paper.html

abstract:
This study introduces a novel zero-shot scan-specific self-supervised reconstruction method for magnetic resonance imaging (MRI) to reduce scan times. Conventional supervised reconstruction methods require large amounts of fully-sampled reference data, which is often impractical to obtain and can lead to artifacts by overly emphasizing learned patterns. Existing zero-shot scan-specific methods have attempted to overcome this data dependency but show limited performance due to insufficient utilization of k-space information and constraints derived from MRI forward model. To address these limitations, we introduce a framework utilizing all acquired k-space measurements for both network inputs and training targets. While this framework suffers from training instability, we resolve these challenges through three key components: an Attention-guided K-space Selective Mechanism (AKSM) that provides indirect constraints for non-sampled k-space points, Iteration-wise K-space Masking (IKM) that enhances training stability, and a robust sensitivity map estimation model utilizing cross-channel constraint that performs effectively even at high reduction factors. Experimental results on the FastMRI knee and brain datasets with reduction factors of 4 and 8 demonstrate that the proposed method achieves superior reconstruction quality and faster convergence compared to existing zero-shot scan-specific methods, making it suitable for practical clinical applications. The implementation of our proposed method is publicly available at https://github.com/joojinho97/AeSPa.git.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Joo_AeSPa__Attention-guided_Self-supervised_Parallel_Imaging_for_MRI_Reconstruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Joo_AeSPa__Attention-guided_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Joo_2025_CVPR,
    author    = {Joo, Jinho and Kim, Hyeseong and Won, Hyeyeon and Lee, Deukhee and Eo, Taejoon and Hwang, Dosik},
    title     = {AeSPa : Attention-guided Self-supervised Parallel Imaging for MRI Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5217-5226}
}

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


title: Boltzmann Attention Sampling for Image Analysis with Small Objects
authors: Theodore Zhao, Sid Kiblawi, Naoto Usuyama, Ho Hin Lee, Sam Preston, Hoifung Poon, Mu Wei
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25950-25959
raw_affiliation: [{"affiliation": "Microsoft", "city": "Redmond", "country": "USA"}]
affiliations: ["Microsoft"]
cities: ["Redmond"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Boltzmann_Attention_Sampling_for_Image_Analysis_with_Small_Objects_CVPR_2025_paper.html

abstract:
Detecting and segmenting small objects, such as lung nodules and tumor lesions, remains a critical challenge in image analysis. These objects often occupy less than 0.1% of an image, making traditional transformer architectures inefficient and prone to performance degradation due to redundant attention computations on irrelevant regions. Existing sparse attention mechanisms rely on rigid hierarchical structures, which are poorly suited for detecting small, variable, and uncertain object locations. In this paper, we propose BoltzFormer, a novel transformer-based architecture designed to address these challenges through dynamic sparse attention. BoltzFormer identifies and focuses attention on relevant areas by modeling uncertainty using a Boltzmann distribution with an annealing schedule. Initially, a higher temperature allows broader area sampling in early layers, when object location uncertainty is greatest. As the temperature decreases in later layers, attention becomes more focused, enhancing efficiency and accuracy. BoltzFormer seamlessly integrates into existing transformer architectures via a modular Boltzmann attention sampling mechanism. Comprehensive evaluations on benchmark datasets demonstrate that BoltzFormer significantly improves segmentation performance for small objects while reducing attention computation by an order of magnitude compared to previous state-of-the-art methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Boltzmann_Attention_Sampling_for_Image_Analysis_with_Small_Objects_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_Boltzmann_Attention_Sampling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Theodore and Kiblawi, Sid and Usuyama, Naoto and Lee, Ho Hin and Preston, Sam and Poon, Hoifung and Wei, Mu},
    title     = {Boltzmann Attention Sampling for Image Analysis with Small Objects},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25950-25959}
}


title: Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16251-16261
cities: ["Hong Kong", "Beijing", "Hong Kong", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Dora_Sampling_and_Benchmarking_for_3D_Shape_Variational_Auto-Encoders_CVPR_2025_paper.html

authors:
Rui Chen, Jianfeng Zhang, Yixun Liang, Guan Luo, Weiyu Li, Jiarui Liu, Xiu Li, Xiaoxiao Long, Jiashi Feng, Ping Tan

abstract:
Recent 3D content generation pipelines commonly employ Variational Autoencoders (VAEs) to encode shapes into compact latent representations for diffusion-based generation. However, the widely adopted uniform point sampling strategy in Shape VAE training often leads to a significant loss of geometric details,  limiting the quality of shape reconstruction and downstream generation tasks. We present Dora-VAE, a novel approach that enhances VAE reconstruction through our proposed sharp edge sampling strategy and a dual cross-attention mechanism. By identifying and prioritizing regions with high geometric complexity during training, our method significantly improves the preservation of fine-grained shape features. Such sampling strategy and the dual attention mechanism enable the VAE to focus on crucial geometric details that are typically missed by uniform sampling approaches. To systematically evaluate VAE reconstruction quality, we additionally propose Dora-bench, a benchmark that quantifies shape complexity through the density of sharp edges, introducing a new metric focused on reconstruction accuracy at these salient geometric features. Extensive experiments on the Dora-bench demonstrate that Dora-VAE achieves comparable reconstruction quality to the state-of-the-art dense XCube-VAE while requiring a latent space at least 8xsmaller (1,280 vs. > 10,000 codes). Project page: https://aruichen.github.io/Dora/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Dora_Sampling_and_Benchmarking_for_3D_Shape_Variational_Auto-Encoders_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Dora_Sampling_and_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Rui and Zhang, Jianfeng and Liang, Yixun and Luo, Guan and Li, Weiyu and Liu, Jiarui and Li, Xiu and Long, Xiaoxiao and Feng, Jiashi and Tan, Ping},
    title     = {Dora: Sampling and Benchmarking for 3D Shape Variational Auto-Encoders},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16251-16261}
}

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

affiliations:
["The Hong Kong University of Science and Technology", "ByteDance Seed", "LightIllusions", "Tsinghua University"]


title: Generalized Recorrupted-to-Recorrupted: Self-Supervised Learning Beyond Gaussian Noise
authors: Brayan Monroy, Jorge Bacca, Julián Tachella
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28155-28164
affiliations: ["Universidad Industrial de Santander", "CNRS & ENS Lyon"]
cities: ["Bucaramanga", "Lyon"]
countries: ["Colombia", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Monroy_Generalized_Recorrupted-to-Recorrupted_Self-Supervised_Learning_Beyond_Gaussian_Noise_CVPR_2025_paper.html

abstract:
Recorrupted-to-Recorrupted (R2R) has emerged as a methodology for training deep networks for image restoration in a self-supervised manner from noisy measurement data alone, demonstrating equivalence in expectation to the supervised squared loss in the case of Gaussian noise. However, its effectiveness with non-Gaussian noise remains unexplored. In this paper, we propose Generalized R2R (GR2R), extending the R2R framework to handle a broader class of noise distribution as additive noise like log-Rayleigh and address the natural exponential family including Poisson, Gamma and Binomial noise distributions, which play a key role in many applications including low-photon imaging and synthetic aperture radar. We show that the GR2R loss is an unbiased estimator of the supervised loss and that the popular Stein's unbiased risk estimator can be seen as a special case. A series of experiments with Gaussian, Poisson, and Gamma noise validate GR2R's performance, showing its effectiveness compared to other self-supervised methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Monroy_Generalized_Recorrupted-to-Recorrupted_Self-Supervised_Learning_Beyond_Gaussian_Noise_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Monroy_Generalized_Recorrupted-to-Recorrupted_Self-Supervised_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Monroy_2025_CVPR,
    author    = {Monroy, Brayan and Bacca, Jorge and Tachella, Juli\'an},
    title     = {Generalized Recorrupted-to-Recorrupted: Self-Supervised Learning Beyond Gaussian Noise},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28155-28164}
}

raw_affiliation:
[{"affiliation": "Universidad Industrial de Santander", "city": "Bucaramanga", "country": "Colombia"}, {"affiliation": "CNRS & ENS Lyon", "city": "Lyon", "country": "France"}]


title: Once-Tuning-Multiple-Variants: Tuning Once and Expanded as Multiple Vision-Language Model Variants
authors: Chong Yu, Tao Chen, Zhongxue Gan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14712-14722
cities: ["Shanghai", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_Once-Tuning-Multiple-Variants_Tuning_Once_and_Expanded_as_Multiple_Vision-Language_Model_Variants_CVPR_2025_paper.html

abstract:
Vision-language model (VLM) is one of the most important models for multi-modal tasks. Real industrial applications often meet the challenge of adapting VLMs to different scenarios, such as varying hardware platforms or performance requirements. Traditional methods involve training or fine-tuning to adapt multiple unique VLMs or using model compression techniques to create multiple compact models. These approaches are complex and resource-intensive. This paper introduces a novel paradigm called Once-Tuning-Multiple-Variants (OTMV). OTMV requires only a single tuning process to inject dynamic weight expansion capacity into the VLM with dynamic expansion capacity. This tuned VLM can then be expanded into multiple variants tailored for different scenarios in inference. The tuning mechanism of OTMV is inspired by the mathematical series expansion theorem, which helps to reduce the parameter size and memory requirements while maintaining accuracy for VLM. Experiment results show that OTMV-tuned models achieve comparable accuracy to baseline VLMs across various visual-language tasks. The experiments also demonstrate the dynamic expansion capability of OTMV-tuned VLMs, outperforming traditional model compression and adaptation techniques in terms of accuracy and efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Once-Tuning-Multiple-Variants_Tuning_Once_and_Expanded_as_Multiple_Vision-Language_Model_Variants_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_Once-Tuning-Multiple-Variants_Tuning_Once_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Chong and Chen, Tao and Gan, Zhongxue},
    title     = {Once-Tuning-Multiple-Variants: Tuning Once and Expanded as Multiple Vision-Language Model Variants},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14712-14722}
}

raw_affiliation:
[{"affiliation": "Academy for Engineering and Technology, Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "School for Information Science and Technology, Fudan University", "city": "Shanghai", "country": "China"}]

affiliations:
["Academy for Engineering and Technology, Fudan University", "School for Information Science and Technology, Fudan University"]


title: Dynamic Motion Blending for Versatile Motion Editing
authors: Nan Jiang, Hongjie Li, Ziye Yuan, Zimo He, Yixin Chen, Tengyu Liu, Yixin Zhu, Siyuan Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22735-22745
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_Dynamic_Motion_Blending_for_Versatile_Motion_Editing_CVPR_2025_paper.html

abstract:
Text-guided motion editing enables high-level semantic control and iterative modifications beyond traditional keyframe animation. Existing methods rely on limited pre-collected training triplets (original motion, edited motion, and instruction), which severely hinders their versatility in diverse editing scenarios. We introduce MotionCutMix, an online data augmentation technique that dynamically generates training triplets by blending body part motions based on input text. While MotionCutMix effectively expands the training distribution, the compositional nature introduces increased randomness and potential body part incoordination. To model such a rich distribution, we present MotionReFit, an auto-regressive diffusion model with a motion coordinator. The auto-regressive architecture facilitates learning by decomposing long sequences, while the motion coordinator mitigates the artifacts of motion composition. Our method handles both spatial and temporal motion edits directly from high-level human instructions, without relying on additional specifications or LLM. Through extensive experiments, we show that MotionReFit achieves state-of-the-art performance in text-guided motion editing. Ablation studies further verify that MotionCutMix significantly improves the model's generalizability while maintaining training convergence.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_Dynamic_Motion_Blending_for_Versatile_Motion_Editing_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Nan and Li, Hongjie and Yuan, Ziye and He, Zimo and Chen, Yixin and Liu, Tengyu and Zhu, Yixin and Huang, Siyuan},
    title     = {Dynamic Motion Blending for Versatile Motion Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22735-22745}
}

raw_affiliation:
[{"affiliation": "Institute for AI, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Laboratory of General Arti\ufb01cial Intelligence, BIGAI", "city": "Beijing", "country": "China"}, {"affiliation": "Yuanpei College, Peking University", "city": "Beijing", "country": "China"}]

affiliations:
["Institute for AI, Peking University", "State Key Laboratory of General Arti\ufb01cial Intelligence, BIGAI", "Yuanpei College, Peking University"]


title: StdGEN: Semantic-Decomposed 3D Character Generation from Single Images
authors: Yuze He, Yanning Zhou, Wang Zhao, Zhongkai Wu, Kaiwen Xiao, Wei Yang, Yong-Jin Liu, Xiao Han
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26345-26355
affiliations: ["Tencent AI Lab", "Tsinghua University"]
cities: ["Shenzhen", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/He_StdGEN_Semantic-Decomposed_3D_Character_Generation_from_Single_Images_CVPR_2025_paper.html

abstract:
We present StdGEN, an innovative pipeline for generating semantically decomposed high-quality 3D characters from single images, enabling broad applications in virtual reality, gaming, and filmmaking, etc. Unlike previous methods which struggle with limited decomposability, unsatisfactory quality, and long optimization times, StdGEN features decomposability, effectiveness and efficiency; i.e., it generates intricately detailed 3D characters with separated semantic components such as the body, clothes, and hair, in three minutes. At the core of StdGEN is our proposed Semantic-aware Large Reconstruction Model (S-LRM), a transformer-based generalizable model that jointly reconstructs geometry, color and semantics from multi-view images in a feed-forward manner. A differentiable multi-layer semantic surface extraction scheme is introduced to acquire meshes from hybrid implicit fields reconstructed by our S-LRM. Additionally, a specialized efficient multi-view diffusion model and an iterative multi-layer surface refinement module are integrated into the pipeline to facilitate high-quality, decomposable 3D character generation. Extensive experiments demonstrate our state-of-the-art performance in 3D anime character generation, surpassing existing baselines by a significant margin in geometry, texture and decomposability. StdGEN offers ready-to-use semantic-decomposed 3D characters and enables flexible customization for a wide range of applications. Project page: https://stdgen.github.io

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/He_StdGEN_Semantic-Decomposed_3D_Character_Generation_from_Single_Images_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/He_StdGEN_Semantic-Decomposed_3D_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{He_2025_CVPR,
    author    = {He, Yuze and Zhou, Yanning and Zhao, Wang and Wu, Zhongkai and Xiao, Kaiwen and Yang, Wei and Liu, Yong-Jin and Han, Xiao},
    title     = {StdGEN: Semantic-Decomposed 3D Character Generation from Single Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26345-26355}
}

raw_affiliation:
[{"affiliation": "Tencent AI Lab", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]


title: Reconstructing Animals and the Wild
authors: Peter Kulits, Michael J. Black, Silvia Zuffi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16565-16577
affiliations: ["Max Planck Institute for Intelligent Systems", "IMATI-CNR"]
cities: ["T\u00fcbingen", "Milan"]
countries: ["Germany", "Italy"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kulits_Reconstructing_Animals_and_the_Wild_CVPR_2025_paper.html

abstract:
The notion of 3D reconstruction as scene understanding is foundational in computer vision. Reconstructing 3D scenes from 2D visual observations necessitates strong priors to disambiguate structure. Much work has been focused on the anthropocentric, which, characterized by smooth surfaces, coherent normals, and regular edges, allows for the integration of strong geometric inductive biases. Here, we consider a more challenging problem where such assumptions do not hold: the reconstruction of natural scenes containing trees, bushes, boulders, and animals. While numerous works have attempted to tackle the problem of reconstructing animals in the wild, they have focused solely on the animal, neglecting environmental context. This limits their usefulness for analysis tasks, as animals exist inherently within the 3D world, and information is lost when environmental factors are disregarded. We propose a method to reconstruct natural scenes from single images. We base our approach on recent advances leveraging the strong world priors ingrained in Large Language Models and train an autoregressive model to decode a CLIP embedding into a structured, compositional scene representation, encompassing both animals and the wild. To enable this, we propose a synthetic dataset comprising one million images and thousands of assets. Through our introduction of a CLIP-projection head, we demonstrate that our approach generalizes to the task of reconstructing animals and their environments in real-world images, despite having been trained solely on synthetic data. We release our dataset and code to encourage future research at https://raw.is.tue.mpg.de/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kulits_Reconstructing_Animals_and_the_Wild_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kulits_Reconstructing_Animals_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kulits_2025_CVPR,
    author    = {Kulits, Peter and Black, Michael J. and Zuffi, Silvia},
    title     = {Reconstructing Animals and the Wild},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16565-16577}
}

raw_affiliation:
[{"affiliation": "Max Planck Institute for Intelligent Systems", "city": "T\u00fcbingen", "country": "Germany"}, {"affiliation": "IMATI-CNR", "city": "Milan", "country": "Italy"}]


authors: Minh Kha Do, Kang Han, Phu Lai, Khoa T. Phan, Wei Xiang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7427-7436
raw_affiliation: [{"affiliation": "La Trobe University", "city": "Melbourne", "country": "Australia"}]
affiliations: ["La Trobe University"]
cities: ["Melbourne"]
countries: ["Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kha_RobSense_A_Robust_Multi-modal_Foundation_Model_for_Remote_Sensing_with_CVPR_2025_paper.html

title:
RobSense: A Robust Multi-modal Foundation Model for Remote Sensing with Static, Temporal, and Incomplete Data Adaptability

abstract:
Foundation models for remote sensing have garnered increasing attention for their strong performance across various observation tasks. However, current models lack robustness in managing diverse input types and handling incomplete data in downstream tasks. In this paper, we propose RobSense, a robust multi-modal foundation model for Multi-spectral and Synthetic Aperture Radar data. RobSense is designed with modular components and pre-trained by a combination of temporal multi-modal alignment and masked autoencoder strategies on a huge-scale dataset. Therefore, it can effectively support diverse input types, from static to temporal, uni-modal to multi-modal. To further handle the incomplete data, we incorporate two uni-modal latent reconstructors that recover rich representations from incomplete inputs, addressing variability in spectral bands and temporal sequence irregularities. Extensive experiments demonstrate that RobSense consistently outperforms state-of-the-art baselines on complete datasets across four input types for segmentation, classification, and change detection. On incomplete datasets, RobSense outperforms the baselines by considerably larger margins when the missing rate increases. Project page: https://ikhado.github.io/robsense/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kha_RobSense_A_Robust_Multi-modal_Foundation_Model_for_Remote_Sensing_with_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Do_2025_CVPR,
    author    = {Do, Minh Kha and Han, Kang and Lai, Phu and Phan, Khoa T. and Xiang, Wei},
    title     = {RobSense: A Robust Multi-modal Foundation Model for Remote Sensing with Static, Temporal, and Incomplete Data Adaptability},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7427-7436}
}


title: Spatiotemporal Decoupling for Efficient Vision-Based Occupancy Forecasting
authors: Jingyi Xu, Xieyuanli Chen, Junyi Ma, Jiawei Huang, Jintao Xu, Yue Wang, Ling Pei
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22338-22347
cities: ["Shanghai", "Changsha", "Shanghai", "Baoding", "Hangzhou"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Spatiotemporal_Decoupling_for_Efficient_Vision-Based_Occupancy_Forecasting_CVPR_2025_paper.html

abstract:
The task of occupancy forecasting (OCF) involves utilizing past and present perception data to predict future occupancy states of autonomous vehicle surrounding environments, which is critical for downstream tasks such as obstacle avoidance and path planning. Existing 3D OCF approaches struggle to predict plausible spatial details for movable objects and suffer from slow inference speeds due to neglecting the bias and uneven distribution of changing occupancy states in both space and time. In this paper, we propose a novel spatiotemporal decoupling vision-based paradigm to explicitly tackle the bias and achieve both effective and efficient 3D OCF. To tackle spatial bias in empty areas, we introduce a novel spatial representation that decouples the conventional dense 3D format into 2D bird's-eye view (BEV) occupancy with corresponding height values, enabling 3D OCF derived only from 2D predictions thus enhancing efficiency. To reduce temporal bias on static voxels, we design temporal decoupling to improve end-to-end OCF by temporally associating instances via predicted flows. We develop an efficient multi-head network EfficientOCF to achieve 3D OCF with our devised spatiotemporally decoupled representation. A new metric, conditional IoU (C-IoU), is also introduced to provide a robust 3D OCF performance assessment, especially in datasets with missing or incomplete annotations. The experimental results demonstrate that EfficientOCF surpasses existing baseline methods on accuracy and efficiency, achieving state-of-the-art performance with a fast inference time of 82.33ms with a single GPU. Our code is released at: https://github.com/BIT-XJY/EfficientOCF.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_Spatiotemporal_Decoupling_for_Efficient_Vision-Based_Occupancy_Forecasting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_Spatiotemporal_Decoupling_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Jingyi and Chen, Xieyuanli and Ma, Junyi and Huang, Jiawei and Xu, Jintao and Wang, Yue and Pei, Ling},
    title     = {Spatiotemporal Decoupling for Efficient Vision-Based Occupancy Forecasting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22338-22347}
}

raw_affiliation:
[{"affiliation": "Shanghai Key Laboratory of Navigation and Location Based Services, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "College of Intelligence Science and Technology, National University of Defense Technology", "city": "Changsha", "country": "China"}, {"affiliation": "Department of Automation, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "HAOMO.AI", "city": "Baoding", "country": "China"}, {"affiliation": "State Key Laboratory of Industrial Control and Technology, Zhejiang University", "city": "Hangzhou", "country": "China"}]

affiliations:
["Shanghai Key Laboratory of Navigation and Location Based Services, Shanghai Jiao Tong University", "College of Intelligence Science and Technology, National University of Defense Technology", "Department of Automation, Shanghai Jiao Tong University", "HAOMO.AI", "State Key Laboratory of Industrial Control and Technology, Zhejiang University"]


title: DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12037-12047
cities: ["Wuhan", "Wuhan", "Beijing"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liao_DiffusionDrive_Truncated_Diffusion_Model_for_End-to-End_Autonomous_Driving_CVPR_2025_paper.html

authors:
Bencheng Liao, Shaoyu Chen, Haoran Yin, Bo Jiang, Cheng Wang, Sixu Yan, Xinbang Zhang, Xiangyu Li, Ying Zhang, Qian Zhang, Xinggang Wang

abstract:
Recently, the diffusion model has emerged as a powerful generative technique for robotic policy learning, capable of modeling multi-mode action distributions. Leveraging its capability for end-to-end autonomous driving is a promising direction. However, the numerous denoising steps in the robotic diffusion policy and the more dynamic, open-world nature of traffic scenes pose substantial challenges for generating diverse driving actions at a real-time speed. To address these challenges, we propose a novel truncated diffusion policy that incorporates prior multi-mode anchors and truncates the diffusion schedule, enabling the model to learn denoising from anchored Gaussian distribution to the multi-mode driving action distribution. Additionally, we design an efficient cascade diffusion decoder for enhanced interaction with conditional scene context. The proposed model, DiffusionDrive, demonstrates 10x reduction in denoising steps compared to vanilla diffusion policy, delivering superior diversity and quality in just 2 steps. On the planning-oriented NAVSIM dataset, with the aligned ResNet-34 backbone, DiffusionDrive achieves 88.1 PDMS without bells and whistles, setting a new record, while running at a real-time speed of 45 FPS on an NVIDIA 4090. Qualitative results on challenging scenarios further confirm that DiffusionDrive can robustly generate diverse plausible driving actions.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liao_DiffusionDrive_Truncated_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liao_2025_CVPR,
    author    = {Liao, Bencheng and Chen, Shaoyu and Yin, Haoran and Jiang, Bo and Wang, Cheng and Yan, Sixu and Zhang, Xinbang and Li, Xiangyu and Zhang, Ying and Zhang, Qian and Wang, Xinggang},
    title     = {DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12037-12047}
}

raw_affiliation:
[{"affiliation": "Institute of Arti\ufb01cial Intelligence, Huazhong University of Science & T echnology", "city": "Wuhan", "country": "China"}, {"affiliation": "School of EIC, Huazhong University of Science & T echnology", "city": "Wuhan", "country": "China"}, {"affiliation": "Horizon Robotics", "city": "Beijing", "country": "China"}]

affiliations:
["Institute of Arti\ufb01cial Intelligence, Huazhong University of Science & T echnology", "School of EIC, Huazhong University of Science & T echnology", "Horizon Robotics"]


authors: Xiaohao Xu, Feng Xue, Shibo Zhao, Yike Pan, Sebastian Scherer, Xiaonan Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 854-863
affiliations: ["University of Michigan", "Carnegie Mellon University"]
cities: ["Ann Arbor", "Pittsburgh"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_MAC-Ego3D_Multi-Agent_Gaussian_Consensus_for_Real-Time_Collaborative_Ego-Motion_and_Photorealistic_CVPR_2025_paper.html

title:
MAC-Ego3D: Multi-Agent Gaussian Consensus for Real-Time Collaborative Ego-Motion and Photorealistic 3D Reconstruction

abstract:
Real-time multi-agent collaboration for ego-motion estimation and high-fidelity 3D reconstruction is vital for scalable spatial intelligence. However, traditional methods produce sparse, low-detail maps, while recent dense mapping approaches struggle with high latency.To overcome these challenges, we present MAC-Ego3D, a novel framework for real-time collaborative photorealistic 3D reconstruction via Multi-Agent Gaussian Consensus. MAC-Ego3D enables agents to independently construct, align, and iteratively refine local maps using a unified Gaussian splat representation. Through Intra-Agent Gaussian Consensus, it enforces spatial coherence among neighboring Gaussian splats within an agent. For global alignment, parallelized Inter-Agent Gaussian Consensus, which asynchronously aligns and optimizes local maps by regularizing multi-agent Gaussian splats, seamlessly integrates them into a high-fidelity 3D model. Leveraging Gaussian primitives, MAC-Ego3D supports efficient RGB-D rendering, enabling rapid inter-agent Gaussian association and alignment.MAC-Ego3D bridges local precision and global coherence, delivering higher efficiency, largely reducing localization error, and improving mapping fidelity. It establishes a new SOTA on synthetic and real-world benchmarks, achieving a 15xincrease in inference speed, order-of-magnitude reductions in ego-motion estimation error for partial cases, and RGB PSNR gains of 4 to 10 dB. Our code will be made publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_MAC-Ego3D_Multi-Agent_Gaussian_Consensus_for_Real-Time_Collaborative_Ego-Motion_and_Photorealistic_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_MAC-Ego3D_Multi-Agent_Gaussian_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Xiaohao and Xue, Feng and Zhao, Shibo and Pan, Yike and Scherer, Sebastian and Huang, Xiaonan},
    title     = {MAC-Ego3D: Multi-Agent Gaussian Consensus for Real-Time Collaborative Ego-Motion and Photorealistic 3D Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {854-863}
}

raw_affiliation:
[{"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}]


title: FFR: Frequency Feature Rectification for Weakly Supervised Semantic Segmentation
authors: Ziqian Yang, Xinqiao Zhao, Xiaolei Wang, Quan Zhang, Jimin Xiao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30261-30270
affiliations: ["XJTLU", "University of Liverpool"]
cities: ["Suzhou", "Liverpool"]
countries: ["China", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_FFR_Frequency_Feature_Rectification_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2025_paper.html

abstract:
Image-level Weakly Supervised Semantic Segmentation (WSSS) has garnered significant attention due to its low annotation costs. Current single-stage state-of-the-art WSSS methods mainly rely on  V ision  T ransformer (ViT) to extract features from input images, generating more complete segmentation results based on comprehensive semantic information. However, these ViT-based methods often suffer from over-smoothing issues in segmentation results. In this paper, we identify that attenuated high-frequency features mislead the decoder of ViT-based WSSS models, resulting in over-smoothed false segmentation. To address this, we propose a Frequency Feature Rectification (FFR) framework to rectify the false segmentations caused by attenuated high-frequency features and enhance the learning of high-frequency features in the decoder. Quantitative and qualitative experimental results demonstrate that our FFR framework can effectively address the attenuated high-frequency caused over-smoothed segmentation issue and achieve new state-of-the-art WSSS performances. Codes are available at https://github.com/yay97/FFR.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_FFR_Frequency_Feature_Rectification_for_Weakly_Supervised_Semantic_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_FFR_Frequency_Feature_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Ziqian and Zhao, Xinqiao and Wang, Xiaolei and Zhang, Quan and Xiao, Jimin},
    title     = {FFR: Frequency Feature Rectification for Weakly Supervised Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30261-30270}
}

raw_affiliation:
[{"affiliation": "XJTLU", "city": "Suzhou", "country": "China"}, {"affiliation": "University of Liverpool", "city": "Liverpool", "country": "UK"}]


title: DVHGNN: Multi-Scale Dilated Vision HGNN for Efficient Vision Recognition
authors: Caoshuo Li, Tanzhe Li, Xiaobin Hu, Donghao Luo, Taisong Jin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20158-20168
cities: ["Xiamen", "Xiamen", "Unknown"]
countries: ["China", "China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_DVHGNN_Multi-Scale_Dilated_Vision_HGNN_for_Efficient_Vision_Recognition_CVPR_2025_paper.html

abstract:
Recently, Vision Graph Neural Network (ViG) has gained considerable attention in computer vision. Despite its groundbreaking innovation, Vision Graph Neural Network encounters key issues including the quadratic computational complexity caused by its K-Nearest Neighbor (KNN) graph construction and the limitation of pairwise relations of normal graphs. To address the aforementioned challenges, we propose a novel vision architecture, termed Dilated Vision HyperGraph Neural Network (DVHGNN), which is designed to leverage multi-scale hypergraph to  efficiently capture high-order correlations among objects. Specifically, the proposed method tailors Clustering and Dilated HyperGraph Construction (DHGC) to adaptively capture multi-scale dependencies among the data samples. Furthermore,  a dynamic hypergraph convolution mechanism is proposed to facilitate adaptive feature exchange and fusion at the hypergraph level. Extensive qualitative and quantitative evaluations of the benchmark image datasets demonstrate that the proposed DVHGNN significantly outperforms the state-of-the-art vision backbones. For instance, our DVHGNN-S achieves an impressive top-1 accuracy of 83.1%  on ImageNet-1K, surpassing ViG-S by +1.0% and ViHGNN-S by +0.6%.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_DVHGNN_Multi-Scale_Dilated_Vision_HGNN_for_Efficient_Vision_Recognition_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_DVHGNN_Multi-Scale_Dilated_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Caoshuo and Li, Tanzhe and Hu, Xiaobin and Luo, Donghao and Jin, Taisong},
    title     = {DVHGNN: Multi-Scale Dilated Vision HGNN for Efficient Vision Recognition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20158-20168}
}

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": "Unknown"}]

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: Video-XL: Extra-Long Vision Language Model for Hour-Scale Video Understanding
authors: Yan Shu, Zheng Liu, Peitian Zhang, Minghao Qin, Junjie Zhou, Zhengyang Liang, Tiejun Huang, Bo Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26160-26169
affiliations: ["School of AI, Shanghai Jiao Tong University", "BAAI", "RUC", "CAS", "BUPT", "PolyU", "PKU"]
cities: ["Shanghai", "Beijing", "Beijing", "Beijing", "Beijing", "Hong Kong", "Beijing"]
countries: ["China", "China", "China", "China", "China", "China", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shu_Video-XL_Extra-Long_Vision_Language_Model_for_Hour-Scale_Video_Understanding_CVPR_2025_paper.html

abstract:
Long video understanding poses a significant challenge for current Multi-modal Large Language Models (MLLMs). Notably, the MLLMs are constrained by their limited context lengths and the substantial costs while processing long videos. Although several existing methods attempt to reduce visual tokens, their strategies encounter severe bottleneck, restricting MLLMs' ability to perceive fine-grained visual details. In this work, we propose Video-XL, a novel approach that leverages MLLMs' inherent key-value (KV) sparsification capacity to condense the visual input. Specifically, we introduce a new special token, the Visual Summarization Token (VST), for each interval of the video, which summarizes the visual information within the interval as its associated KV. The VST module is trained by instruction fine-tuning, where two optimizing strategies are offered. 1. Curriculum learning, where VST learns to make small (easy) and large compression (hard) progressively. 2. Composite data curation, which integrates single-image, multi-image, and synthetic data to overcome the scarcity of long-video instruction data. The compression quality is further improved by dynamic compression, which customizes compression granularity based on the information density of different video intervals. Video-XL's effectiveness is verified from three aspects. First, it achieves a superior long-video understanding capability, outperforming state-of-the-art models of comparable sizes across multiple popular benchmarks. Second, it effectively preserves video information, with minimal compression loss even at 16x compression ratio. Third, it realizes outstanding cost-effectiveness, enabling high-quality processing of thousands of frames on a single A100 GPU.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shu_Video-XL_Extra-Long_Vision_Language_Model_for_Hour-Scale_Video_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shu_Video-XL_Extra-Long_Vision_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shu_2025_CVPR,
    author    = {Shu, Yan and Liu, Zheng and Zhang, Peitian and Qin, Minghao and Zhou, Junjie and Liang, Zhengyang and Huang, Tiejun and Zhao, Bo},
    title     = {Video-XL: Extra-Long Vision Language Model for Hour-Scale Video Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26160-26169}
}

raw_affiliation:
[{"affiliation": "School of AI, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "BAAI", "city": "Beijing", "country": "China"}, {"affiliation": "RUC", "city": "Beijing", "country": "China"}, {"affiliation": "CAS", "city": "Beijing", "country": "China"}, {"affiliation": "BUPT", "city": "Beijing", "country": "China"}, {"affiliation": "PolyU", "city": "Hong Kong", "country": "China"}, {"affiliation": "PKU", "city": "Beijing", "country": "China"}]


title: Reconstructing In-the-Wild Open-Vocabulary Human-Object Interactions
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17426-17436
affiliations: ["Shanghai Jiao Tong University", "Shanghai Innovation Institute", "East China Normal University"]
cities: ["Shanghai", "Shanghai", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wen_Reconstructing_In-the-Wild_Open-Vocabulary_Human-Object_Interactions_CVPR_2025_paper.html

authors:
Boran Wen, Dingbang Huang, Zichen Zhang, Jiahong Zhou, Jianbin Deng, Jingyu Gong, Yulong Chen, Lizhuang Ma, Yong-Lu Li

abstract:
Reconstructing human-object interactions (HOI) from single images is fundamental in computer vision. Existing methods are primarily trained and tested on indoor scenes due to the lack of 3D data, particularly constrained by the object variety, making it challenging to generalize to real-world scenes with a wide range of objects. The limitations of previous 3D HOI datasets were primarily due to the difficulty in acquiring 3D object assets. However, with the development of 3D reconstruction from single images, recently it has become possible to reconstruct various objects from 2D HOI images.We therefore propose a pipeline for annotating fine-grained 3D humans, objects, and their interactions from single images. We annotated 2.5k+ 3D HOI assets from existing 2D HOI datasets and built the first open-vocabulary in-the-wild 3D HOI dataset Open3DHOI, to serve as a future test set. Moreover, we design a novel Gaussian-HOI optimizer, which efficiently reconstructs the spatial interactions between humans and objects while learning the contact regions.Besides the 3D HOI reconstruction, we also propose several new tasks for 3D HOI understanding to pave the way for future work.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wen_Reconstructing_In-the-Wild_Open-Vocabulary_Human-Object_Interactions_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wen_Reconstructing_In-the-Wild_Open-Vocabulary_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wen_2025_CVPR,
    author    = {Wen, Boran and Huang, Dingbang and Zhang, Zichen and Zhou, Jiahong and Deng, Jianbin and Gong, Jingyu and Chen, Yulong and Ma, Lizhuang and Li, Yong-Lu},
    title     = {Reconstructing In-the-Wild Open-Vocabulary Human-Object Interactions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17426-17436}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Innovation Institute", "city": "Shanghai", "country": "China"}, {"affiliation": "East China Normal University", "city": "Shanghai", "country": "China"}]


title: GROVE: A Generalized Reward for Learning Open-Vocabulary Physical Skill
authors: Jieming Cui, Tengyu Liu, Ziyu Meng, Jiale Yu, Ran Song, Wei Zhang, Yixin Zhu, Siyuan Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15781-15790
cities: ["Beijing", "Beijing", "Jinan", "Beijing"]
countries: ["China", "China", "China", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cui_GROVE_A_Generalized_Reward_for_Learning_Open-Vocabulary_Physical_Skill_CVPR_2025_paper.html

abstract:
Learning open-vocabulary physical skills for simulated agents presents a significant challenge in artificial intelligence. Current reinforcement learning approaches face critical limitations: manually designed rewards lack scalability across diverse tasks, while demonstration-based methods struggle to generalize beyond their training distribution. We introduce GROVE, a generalized reward framework that enables open-vocabulary physical skill learning without manual engineering or task-specific demonstrations. Our key insight is that Large Language Models (LLMs) and Vision Language Models (VLMs) provide complementary guidance---LLMs generate precise physical constraints capturing task requirements, while VLMs evaluate motion semantics and naturalness. Through an iterative design process, VLM-based feedback continuously refines LLM-generated constraints, creating a self-improving reward system. To bridge the domain gap between simulation and natural images, we develop Pose2CLIP, a lightweight mapper that efficiently projects agent poses directly into semantic feature space without computationally expensive rendering. Extensive experiments across diverse embodiments and learning paradigms demonstrate GROVE's effectiveness, achieving 22.2% higher motion naturalness and 25.7% better task completion scores while training 8.4x faster than previous methods. These results establish a new foundation for scalable physical skill acquisition in simulated environments.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cui_GROVE_A_Generalized_Reward_for_Learning_Open-Vocabulary_Physical_Skill_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cui_GROVE_A_Generalized_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cui_2025_CVPR,
    author    = {Cui, Jieming and Liu, Tengyu and Meng, Ziyu and Yu, Jiale and Song, Ran and Zhang, Wei and Zhu, Yixin and Huang, Siyuan},
    title     = {GROVE: A Generalized Reward for Learning Open-Vocabulary Physical Skill},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15781-15790}
}

raw_affiliation:
[{"affiliation": "Institute for Artificial Intelligence, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Laboratory of General Artificial Intelligence, BIGAI", "city": "Beijing", "country": "China"}, {"affiliation": "School of Control Science and Engineering, Shandong University", "city": "Jinan", "country": "China"}, {"affiliation": "Department of Automation, Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["Institute for Artificial Intelligence, Peking University", "State Key Laboratory of General Artificial Intelligence, BIGAI", "School of Control Science and Engineering, Shandong University", "Department of Automation, Tsinghua University"]


