# papers

authors: Jiarui Wang, Huiyu Duan, Guangtao Zhai, Juntong Wang, Xiongkuo Min
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18869-18880
cities: ["Shanghai", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_AIGV-Assessor_Benchmarking_and_Evaluating_the_Perceptual_Quality_of_Text-to-Video_Generation_CVPR_2025_paper.html

title:
AIGV-Assessor: Benchmarking and Evaluating the Perceptual Quality of Text-to-Video Generation with LMM

abstract:
The rapid advancement of large multimodal models (LMMs) has led to the rapid expansion of artificial intelligence generated videos (AIGVs), which highlights the pressing need for effective video quality assessment (VQA) models designed specifically for AIGVs. Current VQA models generally fall short in accurately assessing the perceptual quality of AIGVs due to the presence of unique distortions, such as unrealistic objects, unnatural movements, or inconsistent visual elements. To address this challenge, we first present AIGVQA-DB, a large-scale dataset comprising 36,576 AIGVs generated by 15 advanced text-to-video models using 1,048 diverse prompts. With these AIGVs, a systematic annotation pipeline including scoring and ranking processes is devised, which collects 370k expert ratings to date. Based on AIGVQA-DB, we further introduce AIGV-Assessor, a novel VQA model that leverages spatiotemporal features and LMM frameworks to capture the intricate quality attributes of AIGVs, thereby accurately predicting precise video quality scores and video pair preferences. Through comprehensive experiments on both AIGVQA-DB and existing AIGV databases, AIGV-Assessor demonstrates state-of-the-art performance, significantly surpassing existing scoring or evaluation methods in terms of multiple perceptual quality dimensions. The dataset and code are released at https://github.com/IntMeGroup/AIGV-Assessor.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_AIGV-Assessor_Benchmarking_and_Evaluating_the_Perceptual_Quality_of_Text-to-Video_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_AIGV-Assessor_Benchmarking_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Jiarui and Duan, Huiyu and Zhai, Guangtao and Wang, Juntong and Min, Xiongkuo},
    title     = {AIGV-Assessor: Benchmarking and Evaluating the Perceptual Quality of Text-to-Video Generation with LMM},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18869-18880}
}

raw_affiliation:
[{"affiliation": "Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]

affiliations:
["Institute of Image Communication and Network Engineering, Shanghai Jiao Tong University", "MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University"]


title: CoSpace: Benchmarking Continuous Space Perception Ability for Vision-Language Models
authors: Yiqi Zhu, Ziyue Wang, Can Zhang, Peng Li, Yang Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29569-29579
cities: ["Beijing", "Beijing", "Beijing", "Shanghai", "Jiangsu"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_CoSpace_Benchmarking_Continuous_Space_Perception_Ability_for_Vision-Language_Models_CVPR_2025_paper.html

abstract:
Vision-Language Models (VLMs) have recently witnessed significant progress in visual comprehension. As the permitting length of image context grows, VLMs can now comprehend a broader range of views and spaces. Current benchmarks provide insightful analysis of VLMs in tasks involving complex visual instructions following, multi-image understanding and spatial reasoning. However, they usually focus on spatially irrelevant images or discrete images captured from varied viewpoints. The compositional characteristic of images captured from a static viewpoint remains underestimated. We term this characteristic as Continuous Space Perception. When observing a scene from a static viewpoint while shifting orientations, it produces a series of spatially continuous images, enabling the reconstruction of the entire space. In this paper, we present CoSpace, a multi-image visual understanding benchmark designed to assess the Continuous Space perception ability for VLMs. CoSpace contains 2,918 images and 1,626 question-answer pairs, covering seven types of tasks. We conduct evaluation across 19 proprietary and open-source VLMs. Results reveal that there exist pitfalls on the continuous space perception ability for most of the evaluated models, including proprietary ones. Interestingly, we find that the main discrepancy between open-source and proprietary models lies not in accuracy but in the consistency of responses. We believe that enhancing the ability of continuous space perception is essential for VLMs to perform effectively in real-world tasks and encourage further research to advance this capability.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_CoSpace_Benchmarking_Continuous_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Yiqi and Wang, Ziyue and Zhang, Can and Li, Peng and Liu, Yang},
    title     = {CoSpace: Benchmarking Continuous Space Perception Ability for Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29569-29579}
}

raw_affiliation:
[{"affiliation": "Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Institute for AI Industry Research (AIR), Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer and Communication Engineering, University of Science and Technology Beijing", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Jiangsu Collaborative Innovation Center for Language Competence", "city": "Jiangsu", "country": "China"}]

affiliations:
["Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua University", "Institute for AI Industry Research (AIR), Tsinghua University", "School of Computer and Communication Engineering, University of Science and Technology Beijing", "Shanghai Artificial Intelligence Laboratory", "Jiangsu Collaborative Innovation Center for Language Competence"]


title: Autoregressive Distillation of Diffusion Transformers
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15745-15756
affiliations: ["unknown", "unknown", "unknown", "unknown", "unknown", "unknown", "unknown", "unknown"]
cities: ["unknown", "unknown", "unknown", "unknown", "unknown", "unknown", "unknown", "unknown"]
countries: ["unknown", "unknown", "unknown", "unknown", "unknown", "unknown", "unknown", "unknown"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Autoregressive_Distillation_of_Diffusion_Transformers_CVPR_2025_paper.html

authors:
Yeongmin Kim, Sotiris Anagnostidis, Yuming Du, Edgar Schönfeld, Jonas Kohler, Markos Georgopoulos, Albert Pumarola, Ali Thabet, Artsiom Sanakoyeu

abstract:
Diffusion models with transformer architectures have demonstrated promising capabilities in generating high-fidelity images and scalability for high resolution. However, iterative sampling process required for synthesis is very resource-intensive. A line of work  has focused on distilling solutions to probability flow ODEs into few-step student models. Nevertheless, existing methods have been limited by their reliance on the most recent denoised samples as input, rendering them susceptible to exposure bias. To address this limitation, we propose AutoRegressive Distillation (ARD), a novel approach that leverages the historical trajectory of the ODE to predict future steps. ARD offers two key benefits: 1) it mitigates exposure bias by utilizing a predicted historical trajectory that is less susceptible to accumulated errors, and 2) it leverages the previous history of the ODE trajectory as a more effective source of coarse-grained information. ARD modifies the teacher transformer architecture by adding token-wise time embedding to mark each input from the trajectory history and employs a block-wise causal attention mask for training. Furthermore, incorporating historical inputs only in lower transformer layers enhances performance and efficiency. We validate the effectiveness of ARD in a class-conditioned generation on ImageNet and T2I synthesis. Our model achieves a 5xreduction in FID degradation compared to the baseline methods while requiring only 1.1% extra FLOPs on ImageNet-256. Moreover, ARD reaches FID of 1.84 on ImageNet-256 in merely 4 steps and outperforms the publicly available 1024p text-to-image distilled models in prompt adherence score with a minimal drop in FID compared to the teacher. Our code is available at https://github.com/alsdudrla10/ARD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Autoregressive_Distillation_of_Diffusion_Transformers_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_Autoregressive_Distillation_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Yeongmin and Anagnostidis, Sotiris and Du, Yuming and Sch\"onfeld, Edgar and Kohler, Jonas and Georgopoulos, Markos and Pumarola, Albert and Thabet, Ali and Sanakoyeu, Artsiom},
    title     = {Autoregressive Distillation of Diffusion Transformers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15745-15756}
}

raw_affiliation:
[{"affiliation": "unknown", "city": "unknown", "country": "unknown"}, {"affiliation": "unknown", "city": "unknown", "country": "unknown"}, {"affiliation": "unknown", "city": "unknown", "country": "unknown"}, {"affiliation": "unknown", "city": "unknown", "country": "unknown"}, {"affiliation": "unknown", "city": "unknown", "country": "unknown"}, {"affiliation": "unknown", "city": "unknown", "country": "unknown"}, {"affiliation": "unknown", "city": "unknown", "country": "unknown"}, {"affiliation": "unknown", "city": "unknown", "country": "unknown"}]


title: RivuletMLP: An MLP-based Architecture for Efficient Compressed Video Quality Enhancement
authors: Gang He, Weiran Wang, Guancheng Quan, Shihao Wang, Dajiang Zhou, Yunsong Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7342-7352
affiliations: ["School of Telecommunications Engineering, Xidian University", "Ant Group"]
cities: ["Xi'an", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/He_RivuletMLP_An_MLP-based_Architecture_for_Efficient_Compressed_Video_Quality_Enhancement_CVPR_2025_paper.html

abstract:
Quality degradation from video compression manifests both spatially along texture edges and temporally with continuous motion changes. Despite recent advances, extracting aligned spatiotemporal information from adjacent frames remains challenging. This is mainly due to limitations in receptive field size and computational complexity, which makes existing methods struggle to efficiently enhance video quality. To address this issue, we propose RivuletMLP, an MLP-based network architecture. Specifically, our framework first employs a Dynamically Guided Deformable Alignment (DDA) module to adaptively explore and align multi-frame feature information. Subsequently, we introduce two modules for feature reconstruction: a Spatiotemporal Feature Flow (SFF) Module  and a Benign Selection Compensation (BSC) module. The SFF module establishes non-local dependencies through an innovative feature permutation mechanism. Additionally, the BSC module utilizes a collaborative strategy of deep feature extraction and local region refinement to alleviate inter frame motion discontinuity caused by compression. Experimental results demonstrate that RivuletMLP achieves superior computational efficiency while maintaining powerful reconstruction capabilities.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/He_RivuletMLP_An_MLP-based_Architecture_for_Efficient_Compressed_Video_Quality_Enhancement_CVPR_2025_paper.pdf

bibtex:
@InProceedings{He_2025_CVPR,
    author    = {He, Gang and Wang, Weiran and Quan, Guancheng and Wang, Shihao and Zhou, Dajiang and Li, Yunsong},
    title     = {RivuletMLP: An MLP-based Architecture for Efficient Compressed Video Quality Enhancement},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7342-7352}
}

raw_affiliation:
[{"affiliation": "School of Telecommunications Engineering, Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}]


authors: Mingjie Pan, Jiyao Zhang, Tianshu Wu, Yinghao Zhao, Wenlong Gao, Hao Dong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17359-17369
affiliations: ["CFCS, School of CS, Peking University", "PKU-AgiBot Lab", "AgiBot"]
cities: ["Beijing", "Beijing", "Unknown"]
countries: ["China", "China", "Unknown"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pan_OmniManip_Towards_General_Robotic_Manipulation_via_Object-Centric_Interaction_Primitives_as_CVPR_2025_paper.html

title:
OmniManip: Towards General Robotic Manipulation via Object-Centric Interaction Primitives as Spatial Constraints

abstract:
The development of general robotic systems capable of manipulating in unstructured environments is a significant challenge. While Vision-Language Models(VLM)  excel in high-level commonsense reasoning, they lack the fine-grained 3D spatial understanding required for precise manipulation tasks. Fine-tuning VLM on robotic datasets to create Vision-Language-Action Models(VLA) is a potential solution, but it is hindered by high data collection costs and generalization issues. To address these challenges, we propose a novel object-centric representation that bridges the gap between VLM's high-level reasoning and the low-level precision required for manipulation. Our key insight is that an object's canonical space, defined by its functional affordances, provides a structured and semantically meaningful way to describe interaction primitives, such as points and directions. These primitives act as a bridge, translating VLM's commonsense reasoning into actionable 3D spatial constraints. In this context, we introduce a dual closed-loop, open-vocabulary robotic manipulation system: one loop for high-level planning through primitive resampling, interaction rendering and VLM checking, and another for low-level execution via 6D pose tracking. This design ensures robust, real-time control without requiring VLM fine-tuning. Extensive experiments demonstrate strong zero-shot generalization across diverse robotic manipulation tasks, highlighting the potential of this approach for automating large-scale data generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pan_OmniManip_Towards_General_Robotic_Manipulation_via_Object-Centric_Interaction_Primitives_as_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pan_OmniManip_Towards_General_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Pan_2025_CVPR,
    author    = {Pan, Mingjie and Zhang, Jiyao and Wu, Tianshu and Zhao, Yinghao and Gao, Wenlong and Dong, Hao},
    title     = {OmniManip: Towards General Robotic Manipulation via Object-Centric Interaction Primitives as Spatial Constraints},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17359-17369}
}

raw_affiliation:
[{"affiliation": "CFCS, School of CS, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "PKU-AgiBot Lab", "city": "Beijing", "country": "China"}, {"affiliation": "AgiBot", "city": "Unknown", "country": "Unknown"}]


title: FreeTimeGS: Free Gaussian Primitives at Anytime Anywhere for Dynamic Scene Reconstruction
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21750-21760
affiliations: ["Zhejiang University", "Geely Automobile Research Institute"]
cities: ["Hangzhou", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_FreeTimeGS_Free_Gaussian_Primitives_at_Anytime_Anywhere_for_Dynamic_Scene_CVPR_2025_paper.html

authors:
Yifan Wang, Peishan Yang, Zhen Xu, Jiaming Sun, Zhanhua Zhang, Yong Chen, Hujun Bao, Sida Peng, Xiaowei Zhou

abstract:
This paper addresses the challenge of reconstructing dynamic 3D scenes with complex motions. Some recent works define 3D Gaussian primitives in the canonical space and use deformation fields to map canonical primitives to observation spaces, achieving real-time dynamic view synthesis. However, these methods often struggle to handle scenes with complex motions due to the difficulty of optimizing deformation fields. To overcome this problem, we propose FreeTimeGS, a novel 4D representation that allows Gaussian primitives to appear at arbitrary time and locations. In contrast to canonical Gaussian primitives, our representation possesses the strong flexibility, thus improving the ability to model dynamic 3D scenes. In addition, we endow each Gaussian primitive with an motion function, allowing it to move to neighboring regions over time, which reduces the temporal redundancy. Experiments results on several datasets show that the rendering quality of our method outperforms recent methods by a large margin. The code will be released for reproducibility.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_FreeTimeGS_Free_Gaussian_Primitives_at_Anytime_Anywhere_for_Dynamic_Scene_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yifan and Yang, Peishan and Xu, Zhen and Sun, Jiaming and Zhang, Zhanhua and Chen, Yong and Bao, Hujun and Peng, Sida and Zhou, Xiaowei},
    title     = {FreeTimeGS: Free Gaussian Primitives at Anytime Anywhere for Dynamic Scene Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21750-21760}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Geely Automobile Research Institute", "city": "Hangzhou", "country": "China"}]


title: Show and Tell: Visually Explainable Deep Neural Nets via Spatially-Aware Concept Bottleneck Models
authors: Itay Benou, Tammy Riklin Raviv
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30063-30072
cities: ["Beersheba", "Beersheba"]
countries: ["Israel", "Israel"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Benou_Show_and_Tell_Visually_Explainable_Deep_Neural_Nets_via_Spatially-Aware_CVPR_2025_paper.html

abstract:
Modern deep neural networks have now reached human-level performance across a variety of tasks. However, unlike humans they lack the ability to explain their decisions by showing where and telling what concepts guided them. In this work, we present a unified framework for transforming any vision neural network into a spatially and conceptually interpretable model. We introduce a spatially-aware concept bottleneck layer that projects "black-box" features of pre-trained backbone models into interpretable concept maps, without requiring human labels. By training a classification layer over this bottleneck, we obtain a self-explaining model that articulates which concepts most influenced its prediction, along with heatmaps that ground them in the input image. Accordingly, we name this method "Spatially-Aware and Label-Free Concept Bottleneck Model" (SALF-CBM). Our results show that the proposed SALF-CBM: (1) Outperforms non-spatial CBM methods, as well as the original backbone, on a variety of classification tasks; (2) Produces high-quality spatial explanations, outperforming widely used heatmap-based methods on a zero-shot segmentation task; (3) Facilitates model exploration and debugging, enabling users to query specific image regions and refine the model's decisions by locally editing its concept maps.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Benou_Show_and_Tell_Visually_Explainable_Deep_Neural_Nets_via_Spatially-Aware_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Benou_Show_and_Tell_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Benou_2025_CVPR,
    author    = {Benou, Itay and Raviv, Tammy Riklin},
    title     = {Show and Tell: Visually Explainable Deep Neural Nets via Spatially-Aware Concept Bottleneck Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30063-30072}
}

raw_affiliation:
[{"affiliation": "Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev", "city": "Beersheba", "country": "Israel"}, {"affiliation": "Data Science Research Center, Ben-Gurion University of the Negev", "city": "Beersheba", "country": "Israel"}]

affiliations:
["Department of Electrical and Computer Engineering, Ben-Gurion University of the Negev", "Data Science Research Center, Ben-Gurion University of the Negev"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19702-19712
cities: ["Beijing", "Beijing", "Beijing", "Hangzhou", "Beijing", "Guangzhou", "Guangzhou"]
countries: ["China", "China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shen_DiscoVLA_Discrepancy_Reduction_in_Vision_Language_and_Alignment_for_Parameter-Efficient_CVPR_2025_paper.html

title:
DiscoVLA: Discrepancy Reduction in Vision, Language, and Alignment for Parameter-Efficient Video-Text Retrieval

authors:
Leqi Shen, Guoqiang Gong, Tianxiang Hao, Tao He, Yifeng Zhang, Pengzhang Liu, Sicheng Zhao, Jungong Han, Guiguang Ding

abstract:
The parameter-efficient adaptation of the image-text pretraining model CLIP for video-text retrieval is a prominent area of research. While CLIP is focused on image-level vision-language matching, video-text retrieval demands comprehensive understanding at the video level. Three key discrepancies emerge in the transfer from image-level to video-level: vision, language, and alignment. However, existing methods mainly focus on vision while neglecting language and alignment. In this paper, we propose Discrepancy Reduction in Vision, Language, and Alignment (DiscoVLA), which simultaneously mitigates all three discrepancies. Specifically, we introduce Image-Video Features Fusion to integrate image-level and video-level features, effectively tackling both vision and language discrepancies. Additionally, we generate pseudo image captions to learn fine-grained image-level alignment. To mitigate alignment discrepancies, we propose Image-to-Video Alignment Distillation, which leverages image-level alignment knowledge to enhance video-level alignment. Extensive experiments demonstrate the superiority of our DiscoVLA. In particular, on MSRVTT with CLIP (ViT-B/16), DiscoVLA outperforms previous methods by 2.2% R@1 and 7.5% R@sum. The code is available at https://github.com/LunarShen/DsicoVLA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_DiscoVLA_Discrepancy_Reduction_in_Vision_Language_and_Alignment_for_Parameter-Efficient_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shen_DiscoVLA_Discrepancy_Reduction_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shen_2025_CVPR,
    author    = {Shen, Leqi and Gong, Guoqiang and Hao, Tianxiang and He, Tao and Zhang, Yifeng and Liu, Pengzhang and Zhao, Sicheng and Han, Jungong and Ding, Guiguang},
    title     = {DiscoVLA: Discrepancy Reduction in Vision, Language, and Alignment for Parameter-Efficient Video-Text Retrieval},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19702-19712}
}

raw_affiliation:
[{"affiliation": "School of Software", "city": "Beijing", "country": "China"}, {"affiliation": "BNRist", "city": "Beijing", "country": "China"}, {"affiliation": "Department of Automation, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Hangzhou Zhuoxi Institute of Brain and Intelligence", "city": "Hangzhou", "country": "China"}, {"affiliation": "JD.com", "city": "Beijing", "country": "China"}, {"affiliation": "GRG Banking Equipment Co., Ltd.", "city": "Guangzhou", "country": "China"}, {"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}]

affiliations:
["School of Software", "BNRist", "Department of Automation, Tsinghua University", "Hangzhou Zhuoxi Institute of Brain and Intelligence", "JD.com", "GRG Banking Equipment Co., Ltd.", "South China University of Technology"]


title: Reanimating Images using Neural Representations of Dynamic Stimuli
authors: Jacob Yeung, Andrew F. Luo, Gabriel Sarch, Margaret M. Henderson, Deva Ramanan, Michael J. Tarr
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5331-5343
raw_affiliation: [{"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}]
affiliations: ["Carnegie Mellon University"]
cities: ["Pittsburgh"]
countries: ["USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yeung_Reanimating_Images_using_Neural_Representations_of_Dynamic_Stimuli_CVPR_2025_paper.html

abstract:
While computer vision models have made incredible strides in static image recognition, they still do not match human performance in tasks that require the understanding of complex, dynamic motion. This is notably true for real-world scenarios where embodied agents face complex and motion-rich environments. Our approach, BrainNRDS (Brain-Neural Representations of Dynamic Stimuli), leverages state-of-the-art video diffusion models to decouple static image representation from motion generation, enabling us to utilize fMRI brain activity for a deeper understanding of human responses to dynamic visual stimuli. Conversely, we also demonstrate that information about the brain's representation of motion can enhance the prediction of optical flow in artificial systems. Our novel approach leads to four main findings: (1) Visual motion, represented as fine-grained, object-level resolution optical flow, can be decoded from brain activity generated by participants viewing video stimuli; (2) Video encoders outperform image-based models in predicting video-driven brain activity; (3) Brain-decoded motion signals enable realistic video reanimation based only on the initial frame of the video; and (4) We extend prior work to achieve full video decoding from video-driven brain activity. BrainNRDS advances our understanding of how the brain represents spatial and temporal information in dynamic visual scenes. Our findings demonstrate the potential of combining brain imaging with video diffusion models for developing more robust and biologically-inspired computer vision systems. We show additional decoding and encoding examples on this site: https://brain-nrds.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yeung_Reanimating_Images_using_Neural_Representations_of_Dynamic_Stimuli_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yeung_Reanimating_Images_using_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Yeung_2025_CVPR,
    author    = {Yeung, Jacob and Luo, Andrew F. and Sarch, Gabriel and Henderson, Margaret M. and Ramanan, Deva and Tarr, Michael J.},
    title     = {Reanimating Images using Neural Representations of Dynamic Stimuli},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5331-5343}
}


title: Visual-Instructed Degradation Diffusion for All-in-One Image Restoration
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12764-12777
cities: ["Beijing", "Beijing", "Hangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Luo_Visual-Instructed_Degradation_Diffusion_for_All-in-One_Image_Restoration_CVPR_2025_paper.html

authors:
Wenyang Luo, Haina Qin, Zewen Chen, Libin Wang, Dandan Zheng, Yuming Li, Yufan Liu, Bing Li, Weiming Hu

abstract:
Image restoration tasks like deblurring, denoising, and dehazing usually need distinct models for each degradation type, restricting their generalization in real-world scenarios with mixed or unknown degradations. In this work, we propose Defusion, a novel all-in-one image restoration framework that utilizes visual instruction-guided degradation diffusion. Unlike existing methods that rely on task-specific models or ambiguous text-based priors, Defusion constructs explicit visual instructions that align with the visual degradation patterns. These instructions are grounded by applying degradations to standardized visual elements, capturing intrinsic degradation features while agnostic to image semantics. Defusion then uses these visual instructions to guide a diffusion-based model that operates directly in the degradation space, where it reconstructs high-quality images by denoising the degradation effects with enhanced stability and generalizability. Comprehensive experiments demonstrate that Defusion outperforms state-of-the-art methods across diverse image restoration tasks, including complex and real-world degradations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Luo_Visual-Instructed_Degradation_Diffusion_for_All-in-One_Image_Restoration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Luo_Visual-Instructed_Degradation_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Luo_2025_CVPR,
    author    = {Luo, Wenyang and Qin, Haina and Chen, Zewen and Wang, Libin and Zheng, Dandan and Li, Yuming and Liu, Yufan and Li, Bing and Hu, Weiming},
    title     = {Visual-Instructed Degradation Diffusion for All-in-One Image Restoration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12764-12777}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), CASIA", "city": "Beijing", "country": "China"}, {"affiliation": "School of Artificial Intelligence, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}]

affiliations:
["State Key Laboratory of Multimodal Artificial Intelligence Systems (MAIS), CASIA", "School of Artificial Intelligence, University of Chinese Academy of Sciences", "Ant Group"]


title: Insightful Instance Features for 3D Instance Segmentation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14057-14067
affiliations: ["Korea University", "Meta", "KAIST", "UCLA"]
cities: ["Seoul", "Menlo Park", "Daejeon", "Los Angeles"]
countries: ["South Korea", "USA", "South Korea", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Roh_Insightful_Instance_Features_for_3D_Instance_Segmentation_CVPR_2025_paper.html

authors:
Wonseok Roh, Hwanhee Jung, Giljoo Nam, Dong In Lee, Hyeongcheol Park, Sang Ho Yoon, Jungseock Joo, Sangpil Kim

abstract:
Recent 3D Instance Segmentation methods typically encode hundreds of instance-wise candidates with instance-specific information in various ways and refine them into final masks. However, they have yet to fully explore the benefit of these candidates. They overlook the valuable cues encoded in multiple candidates that represent different parts of the same instance, resulting in fragments. Also, they often fail to capture the precise spatial range of 3D instances, primarily due to inherent noises from sparse and unordered point clouds. In this work, to address these challenges, we propose IKNE, a novel instance-wise knowledge enhancement approach. We first introduce an Instance-wise Knowledge Aggregation (IKA) to associate scattered single instance details by optimizing correlations among candidates representing the same instance. Moreover, we present an Instance-wise Structural Guidance (ISG) to enhance the spatial understanding of candidates using structural cues from ambiguity-reduced features. Here, we utilize a simple yet effective truncated singular value decomposition algorithm to minimize inherent noises of 3D features. In our extensive experiments on large-scale datasets, ScanNetV2, ScanNet200, S3DIS, and STPLS3D, IKNE outperforms existing works. We validate the effectiveness of our modules in both kernel-based and transformer-based architectures.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Roh_Insightful_Instance_Features_for_3D_Instance_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Roh_Insightful_Instance_Features_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Roh_2025_CVPR,
    author    = {Roh, Wonseok and Jung, Hwanhee and Nam, Giljoo and Lee, Dong In and Park, Hyeongcheol and Yoon, Sang Ho and Joo, Jungseock and Kim, Sangpil},
    title     = {Insightful Instance Features for 3D Instance Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14057-14067}
}

raw_affiliation:
[{"affiliation": "Korea University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Meta", "city": "Menlo Park", "country": "USA"}, {"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "UCLA", "city": "Los Angeles", "country": "USA"}]


title: Learning 4D Panoptic Scene Graph Generation from Rich 2D Visual Scene
authors: Shengqiong Wu, Hao Fei, Jingkang Yang, Xiangtai Li, Juncheng Li, Hanwang Zhang, Tat-seng Chua
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24539-24549
affiliations: ["National University of Singapore", "Nanyang Technological University", "Zhejiang University"]
cities: ["Singapore", "Singapore", "Hangzhou"]
countries: ["Singapore", "Singapore", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Learning_4D_Panoptic_Scene_Graph_Generation_from_Rich_2D_Visual_CVPR_2025_paper.html

abstract:
The latest emerged 4D Panoptic Scene Graph (4D-PSG) provides an advanced-ever representation for comprehensively modeling the dynamic 4D visual real world. Unfortunately, current pioneering 4D-PSG research can largely suffer from data scarcity issues severely, as well as the resulting out-of-vocabulary problems; also, the pipeline nature of the benchmark generation method can lead to suboptimal performance. To address these challenges, this paper investigates a novel framework for 4D-PSG generation that leverages rich 2D visual scene annotations to enhance 4D scene learning. First, we introduce a 4D Large Language Model (4D-LLM) integrated with a 3D mask decoder for end-to-end generation of 4D-PSG. A chained SG inference mechanism is further designed to exploit LLMs' open-vocabulary capabilities to infer accurate and comprehensive object and relation labels iteratively. Most importantly, we propose a 2D-to-4D visual scene transfer learning framework, where a spatial-temporal scene transcending strategy effectively transfers dimension-invariant features from abundant 2D SG annotations to 4D scenes, effectively compensating for data scarcity in 4D-PSG. Extensive experiments on the benchmark data demonstrate that we strikingly outperform baseline models by a large margin, highlighting the effectiveness of our method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Learning_4D_Panoptic_Scene_Graph_Generation_from_Rich_2D_Visual_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Learning_4D_Panoptic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Shengqiong and Fei, Hao and Yang, Jingkang and Li, Xiangtai and Li, Juncheng and Zhang, Hanwang and Chua, Tat-seng},
    title     = {Learning 4D Panoptic Scene Graph Generation from Rich 2D Visual Scene},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24539-24549}
}

raw_affiliation:
[{"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]


title: Knowledge Bridger: Towards Training-Free Missing Modality Completion
authors: Guanzhou Ke, Shengfeng He, Xiaoli Wang, Bo Wang, Guoqing Chao, Yuanyang Zhang, Yi Xie, Hexing Su
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25864-25873
cities: ["Beijing", "Singapore", "Nanjing", "Beijing", "Harbin", "Nanjing", "Guangzhou", "Xiamen"]
countries: ["China", "Singapore", "China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ke_Knowledge_Bridger_Towards_Training-Free_Missing_Modality_Completion_CVPR_2025_paper.html

abstract:
Previous successful approaches to missing modality completion rely on carefully designed fusion techniques and extensive pre-training on complete data, which can limit their generalizability in out-of-domain (OOD) scenarios. In this study, we pose a new challenge: can we develop a missing modality completion model that is both resource-efficient and robust to OOD generalization? To address this, we present a training-free framework for missing modality completion that leverages large multimodal models (LMMs). Our approach, termed the "Knowledge Bridger", is modality-agnostic and integrates generation and ranking of missing modalities. By defining domain-specific priors, our method automatically extracts structured information from available modalities to construct knowledge graphs. These extracted graphs connect the missing modality generation and ranking modules through the LMM, resulting in high-quality imputations of missing modalities. Experimental results across both general and medical domains show that our approach consistently outperforms competing methods, including in OOD generalization. Additionally, our knowledge-driven generation and ranking techniques demonstrate superiority over variants that directly employ LMMs for generation and ranking, offering insights that may be valuable for applications in other domains.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ke_Knowledge_Bridger_Towards_Training-Free_Missing_Modality_Completion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ke_Knowledge_Bridger_Towards_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ke_2025_CVPR,
    author    = {Ke, Guanzhou and He, Shengfeng and Wang, Xiaoli and Wang, Bo and Chao, Guoqing and Zhang, Yuanyang and Xie, Yi and Su, Hexing},
    title     = {Knowledge Bridger: Towards Training-Free Missing Modality Completion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25864-25873}
}

raw_affiliation:
[{"affiliation": "Beijing Jiaotong University", "city": "Beijing", "country": "China"}, {"affiliation": "Singapore Management University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Southeast University", "city": "Nanjing", "country": "China"}, {"affiliation": "Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Harbin Institute of Technology", "city": "Harbin", "country": "China"}, {"affiliation": "Nanjing University of Science and Technology", "city": "Nanjing", "country": "China"}, {"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Xiamen Institute of Technology", "city": "Xiamen", "country": "China"}]

affiliations:
["Beijing Jiaotong University", "Singapore Management University", "Southeast University", "Institute of Automation, Chinese Academy of Sciences", "Harbin Institute of Technology", "Nanjing University of Science and Technology", "South China University of Technology", "Xiamen Institute of Technology"]


title: EmoDubber: Towards High Quality and Emotion Controllable Movie Dubbing
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15863-15873
cities: ["Beijing", "Beijing", "Sydney", "Beijing", "Adelaide"]
countries: ["China", "China", "Australia", "China", "Australia"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cong_EmoDubber_Towards_High_Quality_and_Emotion_Controllable_Movie_Dubbing__CVPR_2025_paper.html

authors:
Gaoxiang Cong, Jiadong Pan, Liang Li, Yuankai Qi, Yuxin Peng, Anton van den Hengel, Jian Yang, Qingming Huang

abstract:
Given a piece of text, a video clip, and a reference audio, the movie dubbing task aims to generate speech that aligns with the video while cloning the desired voice. The existing methods have two primary deficiencies: (1) They struggle to simultaneously hold audio-visual sync and achieve clear pronunciation; (2) They lack the capacity to express user-defined emotions. To address these problems, we propose EmoDubber, an emotion-controllable dubbing architecture that allows users to specify emotion type and emotional intensity while satisfying high-quality lip sync and pronunciation. Specifically, we first design Lip-related Prosody Aligning (LPA), which focuses on learning the inherent consistency between lip motion and prosody variation by duration level contrastive learning to incorporate reasonable alignment. Then, we design Pronunciation Enhancing (PE) strategy to fuse the video-level phoneme sequences by efficient conformer to improve speech intelligibility. Next, the speaker identity adapting module decodes acoustics prior and inject the speaker style embedding. After that, the proposed Flow-based User Emotion Controlling (FUEC) is used to synthesize waveform by flow matching prediction network conditioned on acoustics prior. In this process, the FUEC determines the gradient direction and guidance scale based on the user's emotion instructions by the positive and negative guidance mechanism, which focuses on amplifying the desired emotion while suppressing others. Extensive experimental results demonstrate favorable performance compared to several state-of-the-art methods. The code and trained models will be made available at https://github.com/GalaxyCong/DubFlow.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cong_EmoDubber_Towards_High_Quality_and_Emotion_Controllable_Movie_Dubbing__CVPR_2025_paper.pdf

bibtex:
@InProceedings{Cong_2025_CVPR,
    author    = {Cong, Gaoxiang and Pan, Jiadong and Li, Liang and Qi, Yuankai and Peng, Yuxin and van den Hengel, Anton and Yang, Jian and Huang, Qingming},
    title     = {EmoDubber: Towards High Quality and Emotion Controllable Movie Dubbing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15863-15873}
}

raw_affiliation:
[{"affiliation": "Institute of Computing Technology, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Macquarie University", "city": "Sydney", "country": "Australia"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "University of Adelaide", "city": "Adelaide", "country": "Australia"}]

affiliations:
["Institute of Computing Technology, Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "Macquarie University", "Peking University", "University of Adelaide"]


title: DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos
authors: Wenbo Hu, Xiangjun Gao, Xiaoyu Li, Sijie Zhao, Xiaodong Cun, Yong Zhang, Long Quan, Ying Shan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2005-2015
affiliations: ["Tencent AI Lab", "The Hong Kong University of Science and Technology", "ARC Lab, Tencent PCG"]
cities: ["Shenzhen", "Hong Kong", "Shenzhen"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hu_DepthCrafter_Generating_Consistent_Long_Depth_Sequences_for_Open-world_Videos_CVPR_2025_paper.html

abstract:
Estimating video depth in open-world scenarios is challenging due to the diversity of videos in appearance, content motion, camera movement, and length. We present DepthCrafter, an innovative method for generating temporally consistent long depth sequences with intricate details for open-world videos, without requiring any supplementary information such as camera poses or optical flow. The generalization ability to open-world videos is achieved by training the video-to-depth model from a pre-trained image-to-video diffusion model, through our meticulously designed three-stage training strategy. Our training approach enables the model to generate depth sequences with variable lengths at one time, up to 110 frames, and harvest both precise depth details and rich content diversity from realistic and synthetic datasets. We also propose an inference strategy that can process extremely long videos through segment-wise estimation and seamless stitching. Comprehensive evaluations on multiple datasets reveal that DepthCrafter achieves state-of-the-art performance in open-world video depth estimation under zero-shot settings. Furthermore, DepthCrafter facilitates various downstream applications, including depth-based visual effects and conditional video generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hu_DepthCrafter_Generating_Consistent_Long_Depth_Sequences_for_Open-world_Videos_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hu_DepthCrafter_Generating_Consistent_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hu_2025_CVPR,
    author    = {Hu, Wenbo and Gao, Xiangjun and Li, Xiaoyu and Zhao, Sijie and Cun, Xiaodong and Zhang, Yong and Quan, Long and Shan, Ying},
    title     = {DepthCrafter: Generating Consistent Long Depth Sequences for Open-world Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2005-2015}
}

raw_affiliation:
[{"affiliation": "Tencent AI Lab", "city": "Shenzhen", "country": "China"}, {"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "ARC Lab, Tencent PCG", "city": "Shenzhen", "country": "China"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26566-26575
cities: ["Beijing", "Beijing", "Xi'an"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_TexGarment_Consistent_Garment_UV_Texture_Generation_via_Efficient_3D_Structure-Guided_CVPR_2025_paper.html

title:
TexGarment: Consistent Garment UV Texture Generation via Efficient 3D Structure-Guided Diffusion Transformer

authors:
Jialun Liu, Jinbo Wu, Xiaobo Gao, Jiakui Hu, Bojun Xiong, Xing Liu, Chen Zhao, Hongbin Pei, Haocheng Feng, Yingying Li, Errui Ding, Jingdong Wang

abstract:
This paper introduces TexGarment, an efficient method for synthesizing high-quality, 3D-consistent garment textures in UV space. Traditional approaches based on 2D-to-3D mapping often suffer from 3D inconsistency, while methods learning from limited 3D data lack sufficient texture diversity. These limitations are particularly problematic in garment texture generation, where high demands exist for both detail and variety. To address these challenges, TexGarment leverages a pre-trained text-to-image diffusion Transformer model with robust generalization capabilities, introducing structural information to guide the model in generating 3D-consistent garment textures in a single inference step. Specifically, We utilize the 2D UV position map to guide the layout during the UV texture generation process, ensuring a coherent texture arrangement and enhancing it by integrating global 3D structural information from the mesh surface point cloud. This combined guidance effectively aligns 3D structural integrity with 2D layout. Our method efficiently generates high-quality, diverse UV textures in a single inference step while maintaining 3D consistency. Experimental results validate the effectiveness of TexGarment, achieving state-of-the-art performance in 3D garment texture generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_TexGarment_Consistent_Garment_UV_Texture_Generation_via_Efficient_3D_Structure-Guided_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_TexGarment_Consistent_Garment_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Jialun and Wu, Jinbo and Gao, Xiaobo and Hu, Jiakui and Xiong, Bojun and Liu, Xing and Zhao, Chen and Pei, Hongbin and Feng, Haocheng and Li, Yingying and Ding, Errui and Wang, Jingdong},
    title     = {TexGarment: Consistent Garment UV Texture Generation via Efficient 3D Structure-Guided Diffusion Transformer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26566-26575}
}

raw_affiliation:
[{"affiliation": "Department of Computer Vision Technology(VIS), Baidu Inc.", "city": "Beijing", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}]

affiliations:
["Department of Computer Vision Technology(VIS), Baidu Inc.", "Peking University", "Xi\u2019an Jiaotong University"]


title: A Hubness Perspective on Representation Learning for Graph-Based Multi-View Clustering
authors: Zheming Xu, He Liu, Congyan Lang, Tao Wang, Yidong Li, Michael C. Kampffmeyer
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15528-15537
cities: ["Beijing", "Beijing", "Troms\u00f8"]
countries: ["China", "China", "Norway"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_A_Hubness_Perspective_on_Representation_Learning_for_Graph-Based_Multi-View_Clustering_CVPR_2025_paper.html

abstract:
Recent graph-based multi-view clustering (GMVC) methods typically encode view features into high-dimensional spaces and construct graphs based on distance similarity. However, the high dimensionality of the embeddings often leads to the hubness problem, where a few points repeatedly appear in the nearest neighbor lists of other points. We show that this negatively impacts the extracted graph structures and message passing, thus degrading clustering performance. To the best of our knowledge, we are the first to highlight the detrimental effect of hubness in GMVC methods and introduce the hubREP (hub-aware Representation Embedding and Pairing) framework. Specifically, we propose a simple yet effective encoder that reduces hubness while preserving neighborhood topology within each view. Additionally, we propose a hub-aware pairing module to maintain structure consistency across views, efficiently enhancing the view-specific representations. The proposed hubREP is lightweight compared to the conventional autoencoders used in state-of-the-art GMVC methods and can be integrated into existing GMVC methods that mostly focus on novel fusion mechanisms, further boosting their performance. Comprehensive experiments performed on eight benchmarks confirm the superiority of our method. The code is available at https://github.com/zmxu196/hubREP.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_A_Hubness_Perspective_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Zheming and Liu, He and Lang, Congyan and Wang, Tao and Li, Yidong and Kampffmeyer, Michael C.},
    title     = {A Hubness Perspective on Representation Learning for Graph-Based Multi-View Clustering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15528-15537}
}

raw_affiliation:
[{"affiliation": "School of Computer Science & Technology, Beijing Jiaotong University", "city": "Beijing", "country": "China"}, {"affiliation": "Key Laboratory of Big Data & Artificial Intelligence in Transportation, Ministry of Education, China", "city": "Beijing", "country": "China"}, {"affiliation": "Department of Physics and Technology, UiT The Arctic University of Norway", "city": "Troms\u00f8", "country": "Norway"}]

affiliations:
["School of Computer Science & Technology, Beijing Jiaotong University", "Key Laboratory of Big Data & Artificial Intelligence in Transportation, Ministry of Education, China", "Department of Physics and Technology, UiT The Arctic University of Norway"]


title: Spatial-Temporal Graph Diffusion Policy with Kinematic Modeling for Bimanual Robotic Manipulation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17394-17404
cities: ["Shenzhen", "Dongguan", "Shenzhen", "Jinan"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lv_Spatial-Temporal_Graph_Diffusion_Policy_with_Kinematic_Modeling_for_Bimanual_Robotic_CVPR_2025_paper.html

authors:
Qi Lv, Hao Li, Xiang Deng, Rui Shao, Yinchuan Li, Jianye Hao, Longxiang Gao, Michael Yu Wang, Liqiang Nie

abstract:
Despite the significant success of imitation learning in robotic manipulation, its application to bimanual tasks remains highly challenging. Existing approaches mainly learn a policy to predict a distant next-best end-effector pose (NBP) and then compute the corresponding joint rotation angles for motion using inverse kinematics. However, they suffer from two important issues: (1) rarely considering the physical robotic structure , which may cause self-collisions or interferences, and (2) overlooking the kinematics constraint , which may result in the predicted poses not conforming to the actual limitations of the robot joints. In this paper, we propose Kinematics enhanced Spatial-TemporAl gRaph Diffuser (KStar Diffuser). Specifically, (1)to incorporate the physical robot structure information into action prediction, KStar Diffuser maintains a dynamic spatial-temporal graph according to the physical bimanual joint motions at continuous timesteps. This dynamic graph serves as the robot-structure condition for denoising the actions; (2) to make the NBP learning objective consistent with kinematics, we introduce the differentiable kinematics to provide the reference for optimizing KStar Diffuser. This module regularizes the policy to predict more reliable and kinematics-aware next end-effector poses. Experimental results show that our method effectively leverages the physical structural information and generates kinematics-aware actions in both simulation and real-world.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lv_Spatial-Temporal_Graph_Diffusion_Policy_with_Kinematic_Modeling_for_Bimanual_Robotic_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lv_Spatial-Temporal_Graph_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lv_2025_CVPR,
    author    = {Lv, Qi and Li, Hao and Deng, Xiang and Shao, Rui and Li, Yinchuan and Hao, Jianye and Gao, Longxiang and Wang, Michael Yu and Nie, Liqiang},
    title     = {Spatial-Temporal Graph Diffusion Policy with Kinematic Modeling for Bimanual Robotic Manipulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17394-17404}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)", "city": "Shenzhen", "country": "China"}, {"affiliation": "School of Engineering, Greate Bay University", "city": "Dongguan", "country": "China"}, {"affiliation": "Huawei Noah\u2019s Ark Lab", "city": "Shenzhen", "country": "China"}, {"affiliation": "Qilu University of Technology (Shandong Academy of Sciences)", "city": "Jinan", "country": "China"}]

affiliations:
["School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)", "School of Engineering, Greate Bay University", "Huawei Noah\u2019s Ark Lab", "Qilu University of Technology (Shandong Academy of Sciences)"]


title: Semi-Supervised State-Space Model with Dynamic Stacking Filter for Real-World Video Deraining
authors: Shangquan Sun, Wenqi Ren, Juxiang Zhou, Shu Wang, Jianhou Gan, Xiaochun Cao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26114-26124
cities: ["Beijing", "Beijing", "Shenzhen", "Shenzhen", "Shenzhen", "Kunming", "Fuzhou"]
countries: ["China", "China", "China", "China", "China", "China", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_Semi-Supervised_State-Space_Model_with_Dynamic_Stacking_Filter_for_Real-World_Video_CVPR_2025_paper.html

abstract:
Significant progress has been made in video restoration under rainy conditions over the past decade, largely propelled by advancements in deep learning. Nevertheless, existing methods that depend on paired data struggle to generalize effectively to real-world scenarios, primarily due to the disparity between synthetic and authentic rain effects. To address these limitations, we propose a dual-branch spatio-temporal state-space model to enhance rain streak removal in video sequences. Specifically, we design spatial and temporal state-space model layers to extract spatial features and incorporate temporal dependencies across frames, respectively. To improve multi-frame feature fusion, we derive a dynamic stacking filter, which adaptively approximates statistical filters for superior pixel-wise feature refinement. Moreover, we integrate a median stacking loss to enable semi-supervised learning by generating pseudo-clean patches based on the sparsity prior of rain. To further explore the capacity of deraining models in supporting other vision-based tasks in rainy environments, we introduce a novel real-world benchmark focused on object detection and tracking in rainy conditions. Our method is extensively evaluated across multiple benchmarks containing numerous synthetic and real-world rainy videos, consistently demonstrating its superiority in quantitative metrics, visual quality, efficiency, and its utility for downstream tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_Semi-Supervised_State-Space_Model_with_Dynamic_Stacking_Filter_for_Real-World_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_Semi-Supervised_State-Space_Model_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Shangquan and Ren, Wenqi and Zhou, Juxiang and Wang, Shu and Gan, Jianhou and Cao, Xiaochun},
    title     = {Semi-Supervised State-Space Model with Dynamic Stacking Filter for Real-World Video Deraining},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26114-26124}
}

raw_affiliation:
[{"affiliation": "Institute of Information Engineering, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Cyber Security, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "MoE Key Laboratory of Information Technology", "city": "Shenzhen", "country": "China"}, {"affiliation": "Guangdong Provincial Key Laboratory of Information Security Technology", "city": "Shenzhen", "country": "China"}, {"affiliation": "Key Laboratory of Educational Information for Nationalities, Yunnan Normal University", "city": "Kunming", "country": "China"}, {"affiliation": "School of Mechanical Engineering and Automation, Fuzhou University", "city": "Fuzhou", "country": "China"}]

affiliations:
["Institute of Information Engineering, Chinese Academy of Sciences", "School of Cyber Security, University of Chinese Academy of Sciences", "School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University", "MoE Key Laboratory of Information Technology", "Guangdong Provincial Key Laboratory of Information Security Technology", "Key Laboratory of Educational Information for Nationalities, Yunnan Normal University", "School of Mechanical Engineering and Automation, Fuzhou University"]


title: Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detector
authors: Xiao Guo, Xiufeng Song, Yue Zhang, Xiaohong Liu, Xiaoming Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 105-116
affiliations: ["Michigan State University", "Shanghai Jiao Tong University"]
cities: ["East Lansing", "Shanghai"]
countries: ["USA", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_Rethinking_Vision-Language_Model_in_Face_Forensics_Multi-Modal_Interpretable_Forged_Face_CVPR_2025_paper.html

abstract:
Deepfake detection is a long-established research topic vital for mitigating the spread of malicious misinformation. Unlike prior methods that provide either binary classification results or textual explanations separately, we introduce a novel method capable of generating both simultaneously. Our method harnesses the multi-modal learning capability of the pre-trained CLIP and the unprecedented interpretability of large language models (LLMs) to enhance both the generalization and explainability of deepfake detection. Specifically, we introduce a multi-modal face forgery detector (M2F2-Det) that employs tailored face forgery prompt learning, incorporating the pre-trained CLIP to improve generalization to unseen forgeries. Also, M2F2-Det incorporates an LLM to provide detailed textual explanations of its detection decisions, enhancing interpretability by bridging the gap between natural language and subtle cues of facial forgeries. Empirically, we evaluate M2F2-Det on both detection and explanation generation tasks, where it achieves state-of-the-art performance, demonstrating its effectiveness in identifying and explaining diverse forgeries. Source code and models are available at this link.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_Rethinking_Vision-Language_Model_in_Face_Forensics_Multi-Modal_Interpretable_Forged_Face_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_Rethinking_Vision-Language_Model_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Xiao and Song, Xiufeng and Zhang, Yue and Liu, Xiaohong and Liu, Xiaoming},
    title     = {Rethinking Vision-Language Model in Face Forensics: Multi-Modal Interpretable Forged Face Detector},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {105-116}
}

raw_affiliation:
[{"affiliation": "Michigan State University", "city": "East Lansing", "country": "USA"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]


title: TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction
authors: Aishwarya Agarwal, Srikrishna Karanam, Vineet Gandhi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30210-30220
cities: ["Hyderabad", "Bengaluru"]
countries: ["India", "India"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Agarwal_TIDE_Training_Locally_Interpretable_Domain_Generalization_Models_Enables_Test-time_Correction_CVPR_2025_paper.html

abstract:
We consider the problem of single-source domain generalization. Existing methods typically rely on extensive augmentations to synthetically cover diverse domains during training. However, they struggle with semantic shifts (e.g., background and viewpoint changes), as they often learn global features instead of local concepts that tend to be domain invariant. To address this gap, we propose an approach that compels models to leverage such local concepts during prediction. Given no suitable dataset with per-class concepts and localization maps exists, we first develop a novel pipeline to generate annotations by exploiting the rich features of diffusion and large-language models. Our next innovation is TIDE, a novel training scheme with a concept saliency alignment loss that ensures model focus on the right per-concept regions and a local concept contrastive loss that promotes learning domain-invariant concept representations. This not only gives a robust model but also can be visually interpreted using the predicted concept saliency maps. Given these maps at test time, our final contribution is a new correction algorithm that uses the corresponding local concept representations to iteratively refine the prediction until it aligns with prototypical concept representations that we store at the end of model training. We evaluate our approach extensively on four standard DG benchmark datasets and substantially outperform the current state-of-the-art (12% improvement on average) while also demonstrating that our predictions can be visually interpreted.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Agarwal_TIDE_Training_Locally_Interpretable_Domain_Generalization_Models_Enables_Test-time_Correction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Agarwal_TIDE_Training_Locally_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Agarwal_2025_CVPR,
    author    = {Agarwal, Aishwarya and Karanam, Srikrishna and Gandhi, Vineet},
    title     = {TIDE: Training Locally Interpretable Domain Generalization Models Enables Test-time Correction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30210-30220}
}

raw_affiliation:
[{"affiliation": "CVIT, Kohli Centre for Intelligent Systems, IIIT Hyderabad, India", "city": "Hyderabad", "country": "India"}, {"affiliation": "Adobe Research, Bengaluru, India", "city": "Bengaluru", "country": "India"}]

affiliations:
["CVIT, Kohli Centre for Intelligent Systems, IIIT Hyderabad, India", "Adobe Research, Bengaluru, India"]


title: VSNet: Focusing on the Linguistic Characteristics of Sign Language
authors: Yuhao Li, Xinyue Chen, Hongkai Li, Xiaorong Pu, Peng Jin, Yazhou Ren
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24320-24330
cities: ["Chengdu", "Leshan", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_VSNet_Focusing_on_the_Linguistic_Characteristics_of_Sign_Language_CVPR_2025_paper.html

abstract:
Sign language is a visual language expressed through complex movements of the upper body. The human skeleton plays a critical role in sign language recognition due to its good separation from the video background. However, mainstream skeleton-based sign language recognition models often overly focus on the natural connections between joints, treating sign language as ordinary human movements, which neglects its linguistic characteristics. We believe that just as letters form words, each sign language gloss can also be decomposed into smaller visual symbols. To fully harness the potential of skeleton data, this paper proposes a novel joint fusion strategy and a visual symbol attention model. Specifically, we first input the complete set of skeletal joints, and after dynamically exchanging joint information, we discard the parts with the weakest connections to other joints, resulting in a fused, simplified skeleton. Then, we group the joints most likely to express the same visual symbol and discuss the joint movements within each group separately. To validate the superiority of our method, we conduct extensive experiments on multiple public benchmark datasets. The results show that, without complex pre-training, we still achieve new state-of-the-art performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_VSNet_Focusing_on_the_Linguistic_Characteristics_of_Sign_Language_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_VSNet_Focusing_on_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Yuhao and Chen, Xinyue and Li, Hongkai and Pu, Xiaorong and Jin, Peng and Ren, Yazhou},
    title     = {VSNet: Focusing on the Linguistic Characteristics of Sign Language},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24320-24330}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, University of Electronic Science and Technology of China", "city": "Chengdu", "country": "China"}, {"affiliation": "Sichuan Provinical Key Lab of Philosophy and Social Science for Language Intelligence in Special Education, Leshan Normal University", "city": "Leshan", "country": "China"}, {"affiliation": "Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China", "city": "Shenzhen", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, University of Electronic Science and Technology of China", "Sichuan Provinical Key Lab of Philosophy and Social Science for Language Intelligence in Special Education, Leshan Normal University", "Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China"]


title: Active Hyperspectral Imaging Using an Event Camera
authors: Bohan Yu, Jinxiu Liang, Zhuofeng Wang, Bin Fan, Art Subpa-asa, Boxin Shi, Imari Sato
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 929-939
cities: ["Beijing", "Beijing", "Beijing", "Tokyo"]
countries: ["China", "China", "China", "Japan"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_Active_Hyperspectral_Imaging_Using_an_Event_Camera_CVPR_2025_paper.html

abstract:
Hyperspectral imaging plays a critical role in numerous scientific and industrial fields. Conventional hyperspectral imaging systems often struggle with the trade-off between capture speed, spectral resolution, and bandwidth, particularly in dynamic environments. In this work, we present a novel event-based active hyperspectral imaging system designed for real-time capture with low bandwidth in dynamic scenes. By combining an event camera with a dynamic illumination strategy, our system achieves unprecedented temporal resolution while maintaining high spectral fidelity, all at a fraction of the bandwidth requirements of traditional systems. Unlike basis-based methods that sacrifice spectral resolution for efficiency, our approach enables continuous spectral sampling through an innovative "sweeping rainbow" illumination pattern synchronized with a rotating mirror array. The key insight is leveraging the sparse, asynchronous nature of event cameras to encode spectral variations as temporal contrasts, effectively transforming the spectral reconstruction problem into a series of geometric constraints. Extensive evaluations of both synthetic and real data demonstrate that our system outperforms state-of-the-art methods in temporal resolution while maintaining competitive spectral reconstruction quality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Active_Hyperspectral_Imaging_Using_an_Event_Camera_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_Active_Hyperspectral_Imaging_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Bohan and Liang, Jinxiu and Wang, Zhuofeng and Fan, Bin and Subpa-asa, Art and Shi, Boxin and Sato, Imari},
    title     = {Active Hyperspectral Imaging Using an Event Camera},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {929-939}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "National Engineering Research Center of V isual T echnology , School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Electronics Engineering and Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "National Institute of Informatics", "city": "Tokyo", "country": "Japan"}]

affiliations:
["State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", "National Engineering Research Center of V isual T echnology , School of Computer Science, Peking University", "School of Electronics Engineering and Computer Science, Peking University", "National Institute of Informatics"]


title: Bridging the Gap between Gaussian Diffusion Models and Universal Quantization for Image Compression
authors: Lucas Relic, Roberto Azevedo, Yang Zhang, Markus Gross, Christopher Schroers
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2449-2458
affiliations: ["ETH Z\u00a8urich", "DisneyResearch|Studios"]
cities: ["Zurich", "Unknown"]
countries: ["Switzerland", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Relic_Bridging_the_Gap_between_Gaussian_Diffusion_Models_and_Universal_Quantization_CVPR_2025_paper.html

abstract:
Generative neural image compression supports data representation with extremely low bitrate, allowing clients to synthesize details and consistently producing highly realistic images. By leveraging the similarities between quantization error and additive noise, diffusion-based generative image compression codecs can be built using a latent diffusion model to "denoise" the artifacts introduced by quantization. However, we identify three critical gaps in previous approaches following this paradigm (namely, the noise level, noise type, and discretization gaps) that result in the quantized data falling out of the data distribution known by the diffusion model. In this work, we propose a novel quantization-based forward diffusion process with theoretical foundations that tackles all three aforementioned gaps. We achieve this through universal quantization with a carefully tailored quantization schedule and a diffusion model trained with uniform noise. Compared to previous work, our proposal produces consistently realistic and detailed reconstructions, even at very low bitrates. In such a regime, we achieve the best rate-distortion-realism performance, outperforming previous related works.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Relic_Bridging_the_Gap_between_Gaussian_Diffusion_Models_and_Universal_Quantization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Relic_Bridging_the_Gap_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Relic_2025_CVPR,
    author    = {Relic, Lucas and Azevedo, Roberto and Zhang, Yang and Gross, Markus and Schroers, Christopher},
    title     = {Bridging the Gap between Gaussian Diffusion Models and Universal Quantization for Image Compression},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2449-2458}
}

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


title: ZeroVO: Visual Odometry with Minimal Assumptions
authors: Lei Lai, Zekai Yin, Eshed Ohn-Bar
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17092-17102
raw_affiliation: [{"affiliation": "Boston University", "city": "Boston", "country": "USA"}]
affiliations: ["Boston University"]
cities: ["Boston"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lai_ZeroVO_Visual_Odometry_with_Minimal_Assumptions_CVPR_2025_paper.html

abstract:
We introduce ZeroVO, a novel visual odometry (VO) algorithm that achieves zero-shot generalization across diverse cameras and environments, overcoming limitations in existing methods that depend on predefined or static camera calibration setups. Our approach incorporates three main innovations. First, we design a calibration-free, geometry-aware network structure capable of handling noise in estimated depth and camera parameters. Second, we introduce a language-based prior that infuses semantic information to enhance robust feature extraction and generalization to previously unseen domains. Third, we develop a flexible, semi-supervised training paradigm that iteratively adapts to new scenes using unlabeled data, further boosting the models' ability to generalize across diverse real-world scenarios. We analyze complex autonomous driving contexts, demonstrating over 30% improvement against prior methods on three standard benchmarks--KITTI, nuScenes, and Argoverse 2--as well as a newly introduced, high-fidelity synthetic dataset derived from Grand Theft Auto (GTA). By not requiring fine-tuning or camera calibration, our work broadens the applicability of VO, providing a versatile solution for real-world deployment at scale.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lai_ZeroVO_Visual_Odometry_with_Minimal_Assumptions_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Lai_2025_CVPR,
    author    = {Lai, Lei and Yin, Zekai and Ohn-Bar, Eshed},
    title     = {ZeroVO: Visual Odometry with Minimal Assumptions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17092-17102}
}


title: VideoRefer Suite: Advancing Spatial-Temporal Object Understanding with Video LLM
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18970-18980
affiliations: ["Zhejiang University", "DAMO Academy, Alibaba Group", "Shanda AI Research Institute"]
cities: ["Hangzhou", "Hangzhou", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yuan_VideoRefer_Suite_Advancing_Spatial-Temporal_Object_Understanding_with_Video_LLM_CVPR_2025_paper.html

authors:
Yuqian Yuan, Hang Zhang, Wentong Li, Zesen Cheng, Boqiang Zhang, Long Li, Xin Li, Deli Zhao, Wenqiao Zhang, Yueting Zhuang, Jianke Zhu, Lidong Bing

abstract:
Video Large Language Models (Video LLMs) have recently exhibited remarkable capabilities in general video understanding.However, they mainly focus on holistic comprehension and struggle with capturing fine-grained spatial and temporal details. Besides, the lack of high-quality object-level video instruction data and a comprehensive benchmark further hinders their advancements. To tackle these challenges, we introduce the VideoRefer Suite to empower Video LLM for finer-level spatial-temporal video understanding, i.e., enabling perception and reasoning on any objects throughout the video. Specially, we thoroughly develop VideoRefer Suite across three essential aspects: dataset, model, and benchmark. Firstly, we introduce a multi-agent data engine to meticulously curate a large-scale, high-quality object-level video instruction dataset, termed VideoRefer-700K. Next, we present the VideoRefer model, which equips a versatile spatial-temporal object encoder to capture precise regional and sequential representations. Finally, we meticulously create a VideoRefer-Bench to comprehensively assess the spatial-temporal understanding capability of a Video LLM, evaluating it across various aspects. Extensive experiments and analyses demonstrate that our VideoRefer model not only achieves promising performance on video referring benchmarks but also facilitates general video understanding capabilities. Our codes, models, and dataset will be made publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yuan_VideoRefer_Suite_Advancing_Spatial-Temporal_Object_Understanding_with_Video_LLM_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yuan_VideoRefer_Suite_Advancing_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yuan_2025_CVPR,
    author    = {Yuan, Yuqian and Zhang, Hang and Li, Wentong and Cheng, Zesen and Zhang, Boqiang and Li, Long and Li, Xin and Zhao, Deli and Zhang, Wenqiao and Zhuang, Yueting and Zhu, Jianke and Bing, Lidong},
    title     = {VideoRefer Suite: Advancing Spatial-Temporal Object Understanding with Video LLM},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18970-18980}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "DAMO Academy, Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Shanda AI Research Institute", "city": "Shanghai", "country": "China"}]


title: Learning to Sample Effective and Diverse Prompts for Text-to-Image Generation
authors: Taeyoung Yun, Dinghuai Zhang, Jinkyoo Park, Ling Pan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23625-23635
affiliations: ["KAIST", "Microsoft Research", "Hong Kong University of Science and Technology"]
cities: ["Daejeon", "Redmond", "Hong Kong"]
countries: ["South Korea", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yun_Learning_to_Sample_Effective_and_Diverse_Prompts_for_Text-to-Image_Generation_CVPR_2025_paper.html

abstract:
Recent advances in text-to-image diffusion models have demonstrated impressive image generation capabilities. However, it remains challenging to control the generation process with desired properties (e.g., aesthetic quality, user intention), which can be expressed as black-box reward functions. Recent advances in text-to-image diffusion models have achieved impressive image generation capabilities. However, it remains challenging to control the generation process with desired properties (e.g., aesthetic quality, user intention), which can be expressed as black-box reward functions. In this paper, we focus on prompt adaptation, which refines the original prompt into model-preferred prompts to generate desired images. While prior work uses reinforcement learning (RL) to optimize prompts, we observe that applying RL often results in generating similar postfixes and deterministic behaviors. To this end, we introduce Prompt Adaptation with GFlowNets (PAG), a novel approach that frames prompt adaptation as a probabilistic inference problem. Our key insight is that leveraging Generative Flow Networks (GFlowNets) allows us to shift from reward maximization to sampling from an unnormalized density function, enabling both high-quality and diverse prompt generation. However, we identify that a naive application of GFlowNets suffers from mode collapse and uncovers a previously overlooked phenomenon: the progressive loss of neural plasticity in the model, which is compounded by inefficient credit assignment in sequential prompt generation. To address this critical challenge, we develop a systematic approach in PAG with flow reactivation, reward-prioritized sampling, and reward decomposition for prompt adaptation. Extensive experiments validate that PAG successfully learns to sample effective and diverse prompts for text-to-image generation. We also show that PAG exhibits strong robustness across various reward functions and transferability to different text-to-image models.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yun_Learning_to_Sample_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yun_2025_CVPR,
    author    = {Yun, Taeyoung and Zhang, Dinghuai and Park, Jinkyoo and Pan, Ling},
    title     = {Learning to Sample Effective and Diverse Prompts for Text-to-Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23625-23635}
}

raw_affiliation:
[{"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "Microsoft Research", "city": "Redmond", "country": "USA"}, {"affiliation": "Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}]


title: Multi-modal Medical Diagnosis via Large-small Model Collaboration
authors: Wanyi Chen, Zihua Zhao, Jiangchao Yao, Ya Zhang, Jiajun Bu, Haishuai Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30763-30773
cities: ["Hangzhou", "Shanghai", "Shanghai", "Shanghai"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Multi-modal_Medical_Diagnosis_via_Large-small_Model_Collaboration_CVPR_2025_paper.html

abstract:
Recent advances in medical AI have shown a clear trend towards large models in healthcare. However, developing large models for multi-modal medical diagnosis remains challenging due to a lack of sufficient modal-complete medical data. Most existing multi-modal diagnostic models are relatively small and struggle with limited feature extraction capabilities. To bridge this gap, we propose **AdaCoMed**, an **ada**ptive **co**llaborative-learning framework that synergistically integrates the off-the-shelf **med**ical single-modal large models with multi-modal small models. Our framework first employs a mixture-of-modality-experts (MoME) architecture to combine features extracted from multiple single-modal medical large models, and then introduces a novel adaptive co-learning mechanism to collaborate with a multi-modal small model. This co-learning mechanism, guided by an adaptive weighting strategy, dynamically balances the complementary strengths between the MoME-fused large model features and the cross-modal reasoning capabilities of the small model. Extensive experiments on two representative multi-modal medical datasets (MIMIC-IV-MM and MMIST ccRCC) across six modalities and four diagnostic tasks demonstrate consistent improvements over state-of-the-art baselines, making it a promising solution for real-world medical diagnosis applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Multi-modal_Medical_Diagnosis_via_Large-small_Model_Collaboration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Multi-modal_Medical_Diagnosis_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Wanyi and Zhao, Zihua and Yao, Jiangchao and Zhang, Ya and Bu, Jiajun and Wang, Haishuai},
    title     = {Multi-modal Medical Diagnosis via Large-small Model Collaboration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30763-30773}
}

raw_affiliation:
[{"affiliation": "Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, College of Computer Science, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Cooperative Medianet Innovation Center, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "School of Artificial Intelligence, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}]

affiliations:
["Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, College of Computer Science, Zhejiang University", "Cooperative Medianet Innovation Center, Shanghai Jiao Tong University", "School of Artificial Intelligence, Shanghai Jiao Tong University", "Shanghai Artificial Intelligence Laboratory"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1342-1352
affiliations: ["Karlsruhe Institute of Technology", "Fraunhofer IOSB"]
cities: ["Karlsruhe", "Karlsruhe"]
countries: ["Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_SAMBLE_Shape-Specific_Point_Cloud_Sampling_for_an_Optimal_Trade-Off_Between_CVPR_2025_paper.html

title:
SAMBLE: Shape-Specific Point Cloud Sampling for an Optimal Trade-Off Between Local Detail and Global Uniformity

authors:
Chengzhi Wu, Yuxin Wan, Hao Fu, Julius Pfrommer, Zeyun Zhong, Junwei Zheng, Jiaming Zhang, Jürgen Beyerer

abstract:
Driven by the increasing demand for accurate and efficient representation of 3D data in various domains, point cloud sampling has emerged as a pivotal research topic in 3D computer vision. Recently, learning-to-sample methods have garnered growing interest from the community, particularly for their ability to be jointly trained with downstream tasks. However, previous learning-based sampling methods either lead to unrecognizable sampling patterns by generating a new point cloud or biased sampled results by focusing excessively on sharp edge details. Moreover, they all overlook the natural variations in point distribution across different shapes, applying a similar sampling strategy to all point clouds. In this paper, we propose a Sparse Attention Map and Bin-based Learning method (termed SAMBLE) to learn shape-specific sampling strategies for point cloud shapes. SAMBLE effectively achieves an improved balance between sampling edge points for local details and preserving uniformity in the global shape, resulting in superior performance across multiple common point cloud downstream tasks, even in scenarios with few-point sampling.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_SAMBLE_Shape-Specific_Point_Cloud_Sampling_for_an_Optimal_Trade-Off_Between_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_SAMBLE_Shape-Specific_Point_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Chengzhi and Wan, Yuxin and Fu, Hao and Pfrommer, Julius and Zhong, Zeyun and Zheng, Junwei and Zhang, Jiaming and Beyerer, J\"urgen},
    title     = {SAMBLE: Shape-Specific Point Cloud Sampling for an Optimal Trade-Off Between Local Detail and Global Uniformity},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1342-1352}
}

raw_affiliation:
[{"affiliation": "Karlsruhe Institute of Technology", "city": "Karlsruhe", "country": "Germany"}, {"affiliation": "Fraunhofer IOSB", "city": "Karlsruhe", "country": "Germany"}]


title: Image Referenced Sketch Colorization Based on Animation Creation Workflow
authors: Dingkun Yan, Xinrui Wang, Zhuoru Li, Suguru Saito, Yusuke Iwasawa, Yutaka Matsuo, Jiaxian Guo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23391-23400
cities: ["Tokyo", "Tokyo", "Unknown", "Tokyo"]
countries: ["Japan", "Japan", "Unknown", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yan_Image_Referenced_Sketch_Colorization_Based_on_Animation_Creation_Workflow_CVPR_2025_paper.html

abstract:
Sketch colorization plays an important role in animation and digital illustration production tasks. However, existing methods still meet problems in that text-guided methods fail to provide accurate color and style reference, hint-guided methods still involve manual operation, and image-referenced methods are prone to cause artifacts. To address these limitations, we propose a diffusion-based framework inspired by real-world animation production workflows. Our approach leverages the sketch as the spatial guidance and an RGB image as the color reference, and separately extracts foreground and background from the reference image with spatial masks. Particularly, we introduce a split cross-attention mechanism with LoRA (Low-Rank Adaptation) modules. They are trained separately with foreground and background regions to control the corresponding embeddings for keys and values in cross-attention. This design allows the diffusion model to integrate information from foreground and background independently, preventing interference and eliminating the spatial artifacts. During inference, we design switchable inference modes for diverse use scenarios by changing modules activated in the framework. Extensive qualitative and quantitative experiments, along with user studies, demonstrate our advantages over existing methods in generating high-qualigy artifact-free results with geometric mismatched references. Ablation studies further confirm the effectiveness of each component. Codes are available at https://github.com/tellurion-kanata/colorizeDiffusion.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_Image_Referenced_Sketch_Colorization_Based_on_Animation_Creation_Workflow_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yan_Image_Referenced_Sketch_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yan_2025_CVPR,
    author    = {Yan, Dingkun and Wang, Xinrui and Li, Zhuoru and Saito, Suguru and Iwasawa, Yusuke and Matsuo, Yutaka and Guo, Jiaxian},
    title     = {Image Referenced Sketch Colorization Based on Animation Creation Workflow},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23391-23400}
}

raw_affiliation:
[{"affiliation": "Institute of Science", "city": "Tokyo", "country": "Japan"}, {"affiliation": "The University of Tokyo", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Project HAT", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Tokyo AI research institute, Shanda Group", "city": "Tokyo", "country": "Japan"}]

affiliations:
["Institute of Science", "The University of Tokyo", "Project HAT", "Tokyo AI research institute, Shanda Group"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14559-14569
cities: ["Beijing", "Shanghai", "Hong Kong", "Nanjing", "Hong Kong", "Baltimore"]
countries: ["China", "China", "China", "China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tao_HoVLE_Unleashing_the_Power_of_Monolithic_Vision-Language_Models_with_Holistic_CVPR_2025_paper.html

title:
HoVLE: Unleashing the Power of Monolithic Vision-Language Models with Holistic Vision-Language Embedding

authors:
Chenxin Tao, Shiqian Su, Xizhou Zhu, Chenyu Zhang, Zhe Chen, Jiawen Liu, Wenhai Wang, Lewei Lu, Gao Huang, Yu Qiao, Jifeng Dai

abstract:
The rapid advance of Large Language Models (LLMs) has catalyzed the development of Vision-Language Models (VLMs). Monolithic VLMs, which avoid modality-specific encoders, offer a promising alternative to the compositional ones but face the challenge of inferior performance. Most existing monolithic VLMs require tuning pre-trained LLMs to acquire vision abilities, which may degrade their language capabilities. To address this dilemma, this paper presents a novel high-performance monolithic VLM named HoVLE. We note that LLMs have been shown to be capable of interpreting images when image embeddings are aligned with text embeddings. The challenge for current monolithic VLMs actually lies in the lack of a holistic embedding module for both vision and language inputs. Therefore, HoVLE introduces a holistic embedding module that converts visual and textual inputs into a shared space, allowing LLMs to process images in the same way as texts. Furthermore, a multi-stage training strategy is carefully designed to empower the holistic embedding module. It is first trained to distill visual features from a pre-trained vision encoder and text embeddings from the LLM, enabling large-scale training with unpaired random images and text tokens. The whole model further undergoes next-token prediction on multi-modal data to align the embeddings. Finally, an instruction-tuning stage is incorporated. Our experiments show that HoVLE achieves performance close to leading compositional models on various benchmarks, outperforming previous monolithic models by a large margin.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tao_HoVLE_Unleashing_the_Power_of_Monolithic_Vision-Language_Models_with_Holistic_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tao_HoVLE_Unleashing_the_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tao_2025_CVPR,
    author    = {Tao, Chenxin and Su, Shiqian and Zhu, Xizhou and Zhang, Chenyu and Chen, Zhe and Liu, Jiawen and Wang, Wenhai and Lu, Lewei and Huang, Gao and Qiao, Yu and Dai, Jifeng},
    title     = {HoVLE: Unleashing the Power of Monolithic Vision-Language Models with Holistic Vision-Language Embedding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14559-14569}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "SenseTime Research", "city": "Hong Kong", "country": "China"}, {"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}]

affiliations:
["Tsinghua University", "Shanghai Artificial Intelligence Laboratory", "SenseTime Research", "Nanjing University", "The Chinese University of Hong Kong", "Johns Hopkins University"]


title: Gen3DEval: Using vLLMs for Automatic Evaluation of Generated 3D Objects
authors: Shalini Maiti, Lourdes Agapito, Filippos Kokkinos
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18552-18562
affiliations: ["Meta AI", "University College London"]
cities: ["Menlo Park", "London"]
countries: ["USA", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Maiti_Gen3DEval_Using_vLLMs_for_Automatic_Evaluation_of_Generated_3D_Objects_CVPR_2025_paper.html

abstract:
Rapid advancements in text-to-3D generation require robust and scalable evaluation metrics that align closely with human judgment, a need unmet by current metrics such as PSNR and CLIP, which require ground-truth data or focus only on prompt fidelity. To address this, we introduce Gen3DEval, a novel evaluation framework that leverages vision large language models (vLLMs) specifically fine-tuned for 3D object quality assessment. Gen3DEval evaluates text fidelity, appearance, and surface quality by analyzing 3D surface normals, without requiring ground-truth comparisons, bridging the gap between automated metrics and user preferences. Compared to state-of-the-art task-agnostic models, Gen3DEval demonstrates superior performance in user-aligned evaluations, placing it as a comprehensive and accessible benchmark for future research on text-to-3D generation. The project page can be found here: https://shalini-maiti.github.io/gen3deval.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Maiti_Gen3DEval_Using_vLLMs_for_Automatic_Evaluation_of_Generated_3D_Objects_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Maiti_Gen3DEval_Using_vLLMs_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Maiti_2025_CVPR,
    author    = {Maiti, Shalini and Agapito, Lourdes and Kokkinos, Filippos},
    title     = {Gen3DEval: Using vLLMs for Automatic Evaluation of Generated 3D Objects},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18552-18562}
}

raw_affiliation:
[{"affiliation": "Meta AI", "city": "Menlo Park", "country": "USA"}, {"affiliation": "University College London", "city": "London", "country": "UK"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14733-14744
cities: ["Hefei", "Shanghai", "Wuhan", "Beijing", "Wuhan", "Evanston"]
countries: ["China", "China", "China", "China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhu_SkySense-O_Towards_Open-World_Remote_Sensing_Interpretation_with_Vision-Centric_Visual-Language_Modeling_CVPR_2025_paper.html

title:
SkySense-O: Towards Open-World Remote Sensing Interpretation with Vision-Centric Visual-Language Modeling

authors:
Qi Zhu, Jiangwei Lao, Deyi Ji, Junwei Luo, Kang Wu, Yingying Zhang, Lixiang Ru, Jian Wang, Jingdong Chen, Ming Yang, Dong Liu, Feng Zhao

abstract:
Open-world interpretation aims to accurately localize and recognize all objects within images by vision-language models (VLMs). While substantial progress has been made in this task for natural images, the advancements for remote sensing (RS) images still remain limited, primarily due to these two challenges. 1) Existing RS semantic categories are limited, particularly for pixel-level interpretation datasets. 2) Distinguishing among diverse RS spatial regions solely by language space is challenging due to the dense and intricate spatial distribution in open-world RS imagery. To address the first issue, we develop a fine-grained RS interpretation dataset, Sky-SA, which contains 183,375 high-quality local image-text pairs with full-pixel manual annotations, covering 1,763 category labels, exhibiting richer semantics and higher density than previous datasets. Afterwards, to solve the second issue, we introduce the vision-centric principle for vision-language modeling. Specifically, in the pre-training stage, the visual self-supervised paradigm is incorporated into image-text alignment, reducing the degradation of general visual representation capabilities of existing paradigms. Then, we construct a visual-relevance knowledge graph across open-category texts and further develop a novel vision-centric image-text contrastive loss for fine-tuning with text prompts. This new model, denoted as SkySense-O, demonstrates impressive zero-shot capabilities on a thorough evaluation encompassing 14 datasets over 4 tasks, from recognizing to reasoning and classification to localization. Specifically, it outperforms the latest models such as SegEarth-OV, GeoRSCLIP, and VHM by a large margin, i.e., 11.95%, 8.04% and 3.55% on average respectively.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhu_SkySense-O_Towards_Open-World_Remote_Sensing_Interpretation_with_Vision-Centric_Visual-Language_Modeling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhu_SkySense-O_Towards_Open-World_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhu_2025_CVPR,
    author    = {Zhu, Qi and Lao, Jiangwei and Ji, Deyi and Luo, Junwei and Wu, Kang and Zhang, Yingying and Ru, Lixiang and Wang, Jian and Chen, Jingdong and Yang, Ming and Liu, Dong and Zhao, Feng},
    title     = {SkySense-O: Towards Open-World Remote Sensing Interpretation with Vision-Centric Visual-Language Modeling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14733-14744}
}

raw_affiliation:
[{"affiliation": "MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, USTC", "city": "Hefei", "country": "China"}, {"affiliation": "Shanghai Jiaotong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "UCAS", "city": "Beijing", "country": "China"}, {"affiliation": "HUST", "city": "Wuhan", "country": "China"}, {"affiliation": "Northwestern University", "city": "Evanston", "country": "USA"}]

affiliations:
["MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition, USTC", "Shanghai Jiaotong University", "Wuhan University", "UCAS", "HUST", "Northwestern University"]


title: AdaDARE-gamma: Balancing Stability and Plasticity in Multi-modal LLMs through Efficient Adaptation
authors: Jingyi Xie, Jintao Yang, Zhunchen Luo, Yunbo Cao, Qiang Gao, Mengyuan Zhang, Wenpeng Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19758-19768
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_AdaDARE-gamma_Balancing_Stability_and_Plasticity_in_Multi-modal_LLMs_through_Efficient_CVPR_2025_paper.html

abstract:
Adapting Multi-modal Large Language Models (MLLMs) to target tasks often suffers from catastrophic forgetting, where acquiring new task-specific knowledge compromises performance on pre-trained tasks. In this paper, we introduce AdaDARE-\gamma, an efficient approach that alleviates catastrophic forgetting by controllably injecting new task-specific knowledge through adaptive parameter selection from fine-tuned models without requiring retraining procedures. This approach consists two key innovations: (1) an adaptive parameter selection mechanism that identifies and retains the most task-relevant parameters from fine-tuned models, and (2) a controlled task-specific information injection strategy that precisely balances the preservation of pre-trained knowledge with the acquisition of new capabilities. Theoretical analysis proves the optimality of our parameter selection strategy and establishes bounds for the task-specific information injection factor. Extensive experiments on InstructBLIP and LLaVA-1.5 across image captioning and visual question answering tasks demonstrate that AdaDARE-\gamma establishes new state-of-the-art results in balancing model performance. Specifically, it maintains 98.2% of pre-training effectiveness on original tasks while achieving 98.7% of standard fine-tuning performance on target tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_AdaDARE-gamma_Balancing_Stability_and_Plasticity_in_Multi-modal_LLMs_through_Efficient_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_AdaDARE-gamma_Balancing_Stability_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Jingyi and Yang, Jintao and Luo, Zhunchen and Cao, Yunbo and Gao, Qiang and Zhang, Mengyuan and Hu, Wenpeng},
    title     = {AdaDARE-gamma: Balancing Stability and Plasticity in Multi-modal LLMs through Efficient Adaptation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19758-19768}
}

raw_affiliation:
[{"affiliation": "Information Research Center of Military Science, AMS", "city": "Beijing", "country": "China"}, {"affiliation": "School of Cyber Science and Technology, Beihang University", "city": "Beijing", "country": "China"}]

affiliations:
["Information Research Center of Military Science, AMS", "School of Cyber Science and Technology, Beihang University"]


title: Driving by the Rules: A Benchmark for Integrating Traffic Sign Regulations into Vectorized HD Map
authors: Xinyuan Chang, Maixuan Xue, Xinran Liu, Zheng Pan, Xing Wei
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6823-6833
affiliations: ["Amap, Alibaba Group", "Xi\u2019an Jiaotong University"]
cities: ["Beijing", "Xi'an"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chang_Driving_by_the_Rules_A_Benchmark_for_Integrating_Traffic_Sign_CVPR_2025_paper.html

abstract:
Ensuring adherence to traffic sign regulations is essential for both human and autonomous vehicle navigation. While current online mapping solutions often prioritize the construction of the geometric and connectivity layers of HD maps, overlooking the construction of the traffic regulation layer within HD maps. Addressing this gap, we introduce MapDR, a novel dataset designed for the extraction of Driving Rules from traffic signs and their association with vectorized, locally perceived HD Maps. MapDR features over 10,000 annotated video clips that capture the intricate correlation between traffic sign regulations and lanes. Built upon this benchmark and the newly defined task of integrating traffic regulations into online HD maps, we provide modular and end-to-end solutions: VLE-MEE and RuleVLM, offering a strong baseline for advancing autonomous driving technology. It fills a critical gap in the integration of traffic sign rules, contributing to the development of reliable autonomous driving systems.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chang_Driving_by_the_Rules_A_Benchmark_for_Integrating_Traffic_Sign_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chang_Driving_by_the_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chang_2025_CVPR,
    author    = {Chang, Xinyuan and Xue, Maixuan and Liu, Xinran and Pan, Zheng and Wei, Xing},
    title     = {Driving by the Rules: A Benchmark for Integrating Traffic Sign Regulations into Vectorized HD Map},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6823-6833}
}

raw_affiliation:
[{"affiliation": "Amap, Alibaba Group", "city": "Beijing", "country": "China"}, {"affiliation": "Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}]


title: LeviTor: 3D Trajectory Oriented Image-to-Video Synthesis
authors: Hanlin Wang, Hao Ouyang, Qiuyu Wang, Wen Wang, Ka Leong Cheng, Qifeng Chen, Yujun Shen, Limin Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12490-12500
cities: ["Nanjing", "Hangzhou", "Hangzhou", "Hong Kong"]
countries: ["China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_LeviTor_3D_Trajectory_Oriented_Image-to-Video_Synthesis_CVPR_2025_paper.html

abstract:
The intuitive nature of drag-based interaction has led to its growing adoption for controlling object trajectories in image-to-video synthesis. Still, existing methods that perform dragging in the 2D space usually face ambiguity when handling out-of-plane movements. In this work, we augment the interaction with a new dimension, i.e., the depth dimension, such that users are allowed to assign a relative depth for each point on the trajectory. That way, our new interaction paradigm not only inherits the convenience from 2D dragging, but facilitates trajectory control in the 3D space, broadening the scope of creativity. We propose a pioneering method for 3D trajectory control in image-to-video synthesis by abstracting object masks into a few cluster points. These points, accompanied by the depth information and the instance information, are finally fed into a video diffusion model as the control signal. Extensive experiments validate the effectiveness of our approach, dubbed LeviTor, in precisely manipulating the object movements when producing photo-realistic videos from static images. Our code is available at: https://github.com/ant-research/LeviTor.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_LeviTor_3D_Trajectory_Oriented_Image-to-Video_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_LeviTor_3D_Trajectory_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Hanlin and Ouyang, Hao and Wang, Qiuyu and Wang, Wen and Cheng, Ka Leong and Chen, Qifeng and Shen, Yujun and Wang, Limin},
    title     = {LeviTor: 3D Trajectory Oriented Image-to-Video Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12490-12500}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory for Novel Software Technology, Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}]

affiliations:
["State Key Laboratory for Novel Software Technology, Nanjing University", "Ant Group", "Zhejiang University", "The Hong Kong University of Science and Technology"]


title: GaPT-DAR: Category-level Garments Pose Tracking via Integrated 2D Deformation and 3D Reconstruction
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22638-22647
cities: ["Hefei", "Hefei", "Shenzhen", "Shanghai", "Hefei"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_GaPT-DAR_Category-level_Garments_Pose_Tracking_via_Integrated_2D_Deformation_and_CVPR_2025_paper.html

authors:
Li Zhang, Mingliang Xu, Jianan Wang, Qiaojun Yu, Lixin Yang, Yonglu Li, Cewu Lu, Rujing Wang, Liu Liu

abstract:
Garments are common in daily life and are important for embodied intelligence community. Current category-level garments pose tracking works focus on predicting point-wise canonical correspondence and learning a shape deformation in point cloud sequences. In this paper, motivated by the 2D warping space and shape prior, we propose GaPT-DAR, a novel category-level Garments Pose Tracking framework with integrated 2D Deformation And 3D Reconstruction function, which fully utilize 3D-2D projection and 2D-3D reconstruction to transform the 3D point-wise learning into 2D warping deformation learning. Specifically, GaPT-DAR firstly builds a Voting-based Project module that learns the optimal 3D-2D projection plane for maintaining the maximum orthogonal entropy during point projection. Next, a Garments Deformation module is designed in 2D space to explicitly model the garments warping procedure with deformation parameters. Finally, we build a Depth Reconstruction module to recover the 2D images into 3D warp field. We provide extensive experiments on VR-Folding dataset to evaluate our GaPT-DAR and the results show obvious improvements on most of the metrics compared to state-of-the-arts ( GarmentNets and GarmentTracking). More details are available at https://sites.google.com/view/gapt-dar.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_GaPT-DAR_Category-level_Garments_Pose_Tracking_via_Integrated_2D_Deformation_and_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Li and Xu, Mingliang and Wang, Jianan and Yu, Qiaojun and Yang, Lixin and Li, Yonglu and Lu, Cewu and Wang, Rujing and Liu, Liu},
    title     = {GaPT-DAR: Category-level Garments Pose Tracking via Integrated 2D Deformation and 3D Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22638-22647}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Hefei Institute of Physical Science, Chinese Academy of Sciences", "city": "Hefei", "country": "China"}, {"affiliation": "Astribot", "city": "Shenzhen", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Hefei University of Technology", "city": "Hefei", "country": "China"}]

affiliations:
["University of Science and Technology of China", "Hefei Institute of Physical Science, Chinese Academy of Sciences", "Astribot", "Shanghai Jiao Tong University", "Hefei University of Technology"]


title: ProbPose: A Probabilistic Approach to 2D Human Pose Estimation
authors: Miroslav Purkrabek, Jiri Matas
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27124-27133
affiliations: ["Czech Technical University in Prague"]
cities: ["Prague"]
countries: ["Czech Republic"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Purkrabek_ProbPose_A_Probabilistic_Approach_to_2D_Human_Pose_Estimation_CVPR_2025_paper.html

abstract:
Current state-of-the-art Human Pose Estimation methods ignore out-of-image keypoints in both training and evaluation and use uncalibrated heatmaps as keypoint location representations. We propose ProbPose, which predicts for each keypoint: a calibrated probability of keypoint presence at each location in the activation window, the probability of being outside of it, and its predicted visibility. To address the lack of evaluation protocols for out-of-image keypoints, we introduce the CropCOCO dataset and the Extended OKS (Ex-OKS) metric, which extends OKS to out-of-image points. Tested on COCO, CropCOCO, and OCHuman, ProbPose shows significant gains in out-of-image keypoint localization while also improving in-image localization through data augmentation. Additionally, the model improves robustness along the edges of the bounding box and offers better flexibility in keypoint evaluation. The code and weights are available on the MiraPurkrabek.github.io/ProbPose.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Purkrabek_ProbPose_A_Probabilistic_Approach_to_2D_Human_Pose_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Purkrabek_ProbPose_A_Probabilistic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Purkrabek_2025_CVPR,
    author    = {Purkrabek, Miroslav and Matas, Jiri},
    title     = {ProbPose: A Probabilistic Approach to 2D Human Pose Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27124-27133}
}

raw_affiliation:
[{"affiliation": "Czech Technical University in Prague", "city": "Prague", "country": "Czech Republic"}]


title: SapiensID: Foundation for Human Recognition
authors: Minchul Kim, Dingqiang Ye, Yiyang Su, Feng Liu, Xiaoming Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13937-13947
cities: ["East Lansing", "Philadelphia"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_SapiensID_Foundation_for_Human_Recognition_CVPR_2025_paper.html

abstract:
Existing human recognition systems often rely on separate, specialized models for face and body analysis, limiting their effectiveness in real-world scenarios where pose, visibility, and context vary widely. This paper introduces SapiensID, a unified model that bridges this gap, achieving robust performance across diverse settings. SapiensID introduces (i) Retina Patch (RP), a dynamic patch generation scheme that adapts to subject scale and ensures consistent tokenization of regions of interest; (ii) Semantic Attention Head (SAH), an attention mechanism that learns pose-invariant representations by pooling features around key body parts; and (iii) a masked recognition model (MRM) that learns from variable token length. To facilitate training, we introduce WebBody4M, a large-scale dataset capturing diverse poses and scale variations. Extensive experiments demonstrate that SapiensID achieves state-of-the-art results on various body ReID benchmarks, outperforming specialized models in both short-term and long-term scenarios while remaining competitive with dedicated face recognition systems. Furthermore, SapiensID establishes a strong baseline for the newly introduced challenge of Cross Pose-Scale ReID, demonstrating its ability to generalize to complex, real-world conditions.The dataset, code and models will be released.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_SapiensID_Foundation_for_Human_Recognition_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_SapiensID_Foundation_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Minchul and Ye, Dingqiang and Su, Yiyang and Liu, Feng and Liu, Xiaoming},
    title     = {SapiensID: Foundation for Human Recognition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13937-13947}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science and Engineering, Michigan State University", "city": "East Lansing", "country": "USA"}, {"affiliation": "Department of Computer Science, Drexel University", "city": "Philadelphia", "country": "USA"}]

affiliations:
["Department of Computer Science and Engineering, Michigan State University", "Department of Computer Science, Drexel University"]


title: MIDI: Multi-Instance Diffusion for Single Image to 3D Scene Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23646-23657
affiliations: ["Beihang University", "V AST", "Tsinghua University", "The University of Hong Kong"]
cities: ["Beijing", "Unknown", "Beijing", "Hong Kong"]
countries: ["China", "Unknown", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_MIDI_Multi-Instance_Diffusion_for_Single_Image_to_3D_Scene_Generation_CVPR_2025_paper.html

authors:
Zehuan Huang, Yuan-Chen Guo, Xingqiao An, Yunhan Yang, Yangguang Li, Zi-Xin Zou, Ding Liang, Xihui Liu, Yan-Pei Cao, Lu Sheng

abstract:
This paper introduces MIDI, a novel paradigm for compositional 3D scene generation from a single image. Unlike existing methods that rely on reconstruction or retrieval techniques or recent approaches that employ multi-stage object-by-object generation, MIDI extends pre-trained image-to-3D object generation models to multi-instance diffusion models, enabling the simultaneous generation of multiple 3D instances with accurate spatial relationships and high generalizability. At its core, MIDI incorporates a novel multi-instance attention mechanism, that effectively captures inter-object interactions and spatial coherence directly within the generation process, without the need for complex multi-step processes. The method utilizes partial object images and global scene context as inputs, directly modeling object completion during 3D generation. During training, we effectively supervise the interactions between 3D instances using a limited amount of scene-level data, while incorporating single-object data for regularization, thereby maintaining the pre-trained generalization ability. MIDI demonstrates state-of-the-art performance in image-to-scene generation, validated through evaluations on synthetic data, real-world scene data, and stylized scene images generated by text-to-image diffusion models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_MIDI_Multi-Instance_Diffusion_for_Single_Image_to_3D_Scene_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_MIDI_Multi-Instance_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Zehuan and Guo, Yuan-Chen and An, Xingqiao and Yang, Yunhan and Li, Yangguang and Zou, Zi-Xin and Liang, Ding and Liu, Xihui and Cao, Yan-Pei and Sheng, Lu},
    title     = {MIDI: Multi-Instance Diffusion for Single Image to 3D Scene Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23646-23657}
}

raw_affiliation:
[{"affiliation": "Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "V AST", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1622-1632
affiliations: ["UC Berkeley", "Waymo LLC", "Cornell University", "Georgia Institute of Technology"]
cities: ["Berkeley", "Mountain View", "Ithaca", "Atlanta"]
countries: ["USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_S4-Driver_Scalable_Self-Supervised_Driving_Multimodal_Large_Language_Model_with_Spatio-Temporal_CVPR_2025_paper.html

title:
S4-Driver: Scalable Self-Supervised Driving Multimodal Large Language Model with Spatio-Temporal Visual Representation

authors:
Yichen Xie, Runsheng Xu, Tong He, Jyh-Jing Hwang, Katie Luo, Jingwei Ji, Hubert Lin, Letian Chen, Yiren Lu, Zhaoqi Leng, Dragomir Anguelov, Mingxing Tan

abstract:
The latest advancements in multi-modal large language models (MLLMs) have spurred a strong renewed interest in end-to-end motion planning approaches for autonomous driving. Many end-to-end approaches rely on human annotations to learn intermediate perception and prediction tasks, while purely self-supervised approaches--which directly learn from sensor inputs to generate planning trajectories without human annotations--often underperform the state of the art. We observe a key gap in the input representation space: end-to-end approaches built on MLLMs are often pretrained with reasoning tasks in 2D image space rather than the native 3D space in which autonomous vehicles plan. To this end, we propose S4-Driver, a scalable self-supervised motion planning algorithm with spatio-temporal visual representation,  based on the popular PaLI multimodal large language model. S4-Driver uses a novel sparse volume strategy to seamlessly transform the strong visual representation of MLLMs from perspective view to 3D space without the need to finetune the vision encoder. This representation aggregates multi-view and multi-frame visual inputs and enables better prediction of planning trajectories in 3D space. To validate our method, we run experiments on both nuScenes and Waymo Open Motion Dataset (with in-house camera data). Results show that S4-Driver performs favorably against existing supervised multi-task approaches while requiring no human annotations. It also demonstrates great scalability when pretrained on large volumes of unannotated driving logs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_S4-Driver_Scalable_Self-Supervised_Driving_Multimodal_Large_Language_Model_with_Spatio-Temporal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_S4-Driver_Scalable_Self-Supervised_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Yichen and Xu, Runsheng and He, Tong and Hwang, Jyh-Jing and Luo, Katie and Ji, Jingwei and Lin, Hubert and Chen, Letian and Lu, Yiren and Leng, Zhaoqi and Anguelov, Dragomir and Tan, Mingxing},
    title     = {S4-Driver: Scalable Self-Supervised Driving Multimodal Large Language Model with Spatio-Temporal Visual Representation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1622-1632}
}

raw_affiliation:
[{"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "Waymo LLC", "city": "Mountain View", "country": "USA"}, {"affiliation": "Cornell University", "city": "Ithaca", "country": "USA"}, {"affiliation": "Georgia Institute of Technology", "city": "Atlanta", "country": "USA"}]


authors: Jie Tian, Xiaoye Qu, Zhenyi Lu, Wei Wei, Sichen Liu, Yu Cheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12512-12521
cities: ["Wuhan", "Hong Kong"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tian_Extrapolating_and_Decoupling_Image-to-Video_Generation_Models_Motion_Modeling_is_Easier_CVPR_2025_paper.html

title:
Extrapolating and Decoupling Image-to-Video Generation Models: Motion Modeling is Easier Than You Think

abstract:
Image-to-Video (I2V) generation aims to synthesize a video clip according to a given image and condition (e.g., text). The key challenge of this task lies in simultaneously generating natural motions while preserving the original appearance of the images. However, current I2V diffusion models (I2V-DMs) often produce videos with limited motion degrees or exhibit uncontrollable motion that conflicts with the textual condition. To address these limitations, we propose a novel Extrapolating and Decoupling framework, which introduces model merging techniques to the I2V domain for the first time. Specifically, our framework consists of three separate stages: (1) Starting with a base I2V-DM, we explicitly inject the textual condition into the temporal module using a lightweight, learnable adapter and fine-tune the integrated model to improve motion controllability. (2) We introduce a training-free extrapolation strategy to amplify the dynamic range of the motion, effectively reversing the fine-tuning process to enhance the motion degree significantly. (3) With the above two-stage models excelling in motion controllability and degree, we decouple the relevant parameters associated with each type of motion ability and inject them into the base I2V-DM. Since the I2V-DM handles different levels of motion controllability and dynamics at various denoising time steps, we adjust the motion-aware parameters accordingly over time. Extensive qualitative and quantitative experiments have been conducted to demonstrate the superiority of our framework over existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tian_Extrapolating_and_Decoupling_Image-to-Video_Generation_Models_Motion_Modeling_is_Easier_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tian_Extrapolating_and_Decoupling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tian_2025_CVPR,
    author    = {Tian, Jie and Qu, Xiaoye and Lu, Zhenyi and Wei, Wei and Liu, Sichen and Cheng, Yu},
    title     = {Extrapolating and Decoupling Image-to-Video Generation Models: Motion Modeling is Easier Than You Think},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12512-12521}
}

raw_affiliation:
[{"affiliation": "School of Computer Science & Technology, Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}]

affiliations:
["School of Computer Science & Technology, Huazhong University of Science and Technology", "The Chinese University of Hong Kong"]


title: FreeCloth: Free-form Generation Enhances Challenging Clothed Human Modeling
authors: Hang Ye, Xiaoxuan Ma, Hai Ci, Wentao Zhu, Yizhou Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15987-15997
cities: ["Beijing", "Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ye_FreeCloth_Free-form_Generation_Enhances_Challenging_Clothed_Human_Modeling_CVPR_2025_paper.html

abstract:
Achieving realistic animated human avatars requires accurate modeling of pose-dependent clothing deformations. Existing learning-based methods heavily rely on the Linear Blend Skinning (LBS) of minimally-clothed human models like SMPL to model deformation. However, they struggle to handle loose clothing, such as long dresses, where the canonicalization process becomes ill-defined when the clothing is far from the body, leading to disjointed and fragmented results. To overcome this limitation, we propose FreeCloth, a novel hybrid framework to model challenging clothed humans. Our core idea is to use dedicated strategies to model different regions, depending on whether they are close to or distant from the body. Specifically, we segment the human body into three categories: unclothed, deformed, and generated. We simply replicate unclothed regions that require no deformation. For deformed regions close to the body, we leverage LBS to handle the deformation. As for the generated regions, which correspond to loose clothing areas, we introduce a novel free-form, part-aware generator to model them, as they are less affected by movements. This free-form generation paradigm brings enhanced flexibility and expressiveness to our hybrid framework, enabling it to capture the intricate geometric details of challenging loose clothing, such as skirts and dresses. Experimental results on the benchmark dataset featuring loose clothing demonstrate that FreeCloth achieves state-of-the-art performance with superior visual fidelity and realism, particularly in the most challenging cases.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ye_FreeCloth_Free-form_Generation_Enhances_Challenging_Clothed_Human_Modeling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ye_FreeCloth_Free-form_Generation_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Ye_2025_CVPR,
    author    = {Ye, Hang and Ma, Xiaoxuan and Ci, Hai and Zhu, Wentao and Wang, Yizhou},
    title     = {FreeCloth: Free-form Generation Enhances Challenging Clothed Human Modeling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15987-15997}
}

raw_affiliation:
[{"affiliation": "Center on Frontiers of Computing Studies, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Inst. for Artificial Intelligence, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Nat\u2019l Eng. Research Center of Visual Technology", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Laboratory of General Artificial Intelligence, Peking University", "city": "Beijing", "country": "China"}]

affiliations:
["Center on Frontiers of Computing Studies, School of Computer Science, Peking University", "Inst. for Artificial Intelligence, Peking University", "Nat\u2019l Eng. Research Center of Visual Technology", "State Key Laboratory of General Artificial Intelligence, Peking University"]


authors: Yu-Cheng Chiu, Guan-Rong Chen, Zihao Chen, Yan-Tsung Peng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21258-21266
raw_affiliation: [{"affiliation": "National Chengchi University", "city": "Taipei City", "country": "Taiwan"}]
affiliations: ["National Chengchi University"]
cities: ["Taipei City"]
countries: ["Taiwan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chiu_ABC-Former_Auxiliary_Bimodal_Cross-domain_Transformer_with_Interactive_Channel_Attention_for_CVPR_2025_paper.html

title:
ABC-Former: Auxiliary Bimodal Cross-domain Transformer with Interactive Channel Attention for White Balance

abstract:
The primary goal of white balance (WB) for sRGB images is to correct inaccurate color temperatures, ensuring that images display natural, neutral colors. While existing WB methods yield reasonable results, their effectiveness is limited. They either focus solely on global color adjustments applied before the camera-specific image signal processing pipeline or rely on end-to-end models that generate WB outputs without accounting for global color trends, leading to suboptimal correction. To address these limitations, we propose an Auxiliary Bimodal Cross-domain Transformer (ABC-Former) that enhances WB correction by leveraging complementary knowledge from global color information from CIELab and RGB histograms alongside sRGB inputs. By introducing an Interactive Channel Attention (ICA) module to facilitate cross-modality global knowledge transfer, ABC-Former achieves more precise WB correction. Experimental results on benchmark WB datasets show that ABC-Former performs favorably against state-of-the-art WB methods. The source code is available at https://github.com/ytpeng-aimlab/ABC-Former.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chiu_ABC-Former_Auxiliary_Bimodal_Cross-domain_Transformer_with_Interactive_Channel_Attention_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chiu_ABC-Former_Auxiliary_Bimodal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chiu_2025_CVPR,
    author    = {Chiu, Yu-Cheng and Chen, Guan-Rong and Chen, Zihao and Peng, Yan-Tsung},
    title     = {ABC-Former: Auxiliary Bimodal Cross-domain Transformer with Interactive Channel Attention for White Balance},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21258-21266}
}


title: Science-T2I: Addressing Scientific Illusions in Image Synthesis
authors: Jialuo Li, Wenhao Chai, Xingyu Fu, Haiyang Xu, Saining Xie
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2734-2744
cities: ["New York", "Seattle", "Philadelphia", "San Diego"]
countries: ["USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Science-T2I_Addressing_Scientific_Illusions_in_Image_Synthesis_CVPR_2025_paper.html

abstract:
We present a novel approach to integrating scientific knowledge into generative models, enhancing their realism and consistency in image synthesis. First, we introduce Science-T2I, an expert-annotated adversarial dataset comprising adversarial 20k image pairs with 9k prompts, covering wide distinct scientific knowledge categories. Leveraging Science-T2I, we present SciScore, an end-to-end reward model that refines the assessment of generated images based on scientific knowledge, which is achieved by augmenting both the scientific comprehension and visual capabilities of pre-trained CLIP model. Additionally, based on Science-T2I, we propose a two-stage training framework, comprising a supervised fine-tuning phase and a masked online fine-tuning phase, to incorporate scientific knowledge into existing generative models. Through comprehensive experiments, we demonstrate the effectiveness of our framework in establishing new standards for evaluating the scientific realism of generated content. Specifically, SciScore attains performance comparable to human-level, demonstrating a 5% improvement similar to evaluations conducted by experienced human evaluators. Furthermore, by applying our proposed fine-tuning method to FLUX, we achieve a performance enhancement exceeding 50% on SciScore.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Science-T2I_Addressing_Scientific_Illusions_in_Image_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Science-T2I_Addressing_Scientific_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Jialuo and Chai, Wenhao and Fu, Xingyu and Xu, Haiyang and Xie, Saining},
    title     = {Science-T2I: Addressing Scientific Illusions in Image Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2734-2744}
}

raw_affiliation:
[{"affiliation": "New York University", "city": "New York", "country": "USA"}, {"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}, {"affiliation": "University of Pennsylvania", "city": "Philadelphia", "country": "USA"}, {"affiliation": "University of California, San Diego", "city": "San Diego", "country": "USA"}]

affiliations:
["New York University", "University of Washington", "University of Pennsylvania", "University of California, San Diego"]


title: Fingerprinting Denoising Diffusion Probabilistic Models
authors: Huan Teng, Yuhui Quan, Chengyu Wang, Jun Huang, Hui Ji
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28811-28820
cities: ["Guangzhou", "Hangzhou", "Singapore"]
countries: ["China", "China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Teng_Fingerprinting_Denoising_Diffusion_Probabilistic_Models_CVPR_2025_paper.html

abstract:
Diffusion models, especially denoising diffusion probabilistic models (DDPMs), are prevalent tools in generative AI, making their intellectual property (IP) protection increasingly important. Most existing IP protection methods for DDPMs are invasive, e.g., model watermarking, which alter model parameters and raise concerns about performance degradation, also with requirement for extra computational resources for retraining or fine-tuning. In this paper, we propose the first non-invasive fingerprinting scheme for DDPMs, requiring no parameter changes or fine-tuning, and keeping generation quality intact. We introduce a discriminative and robust fingerprint latent space based on the well-designed "crossing route" of noisy samples that span the performance border-zone of DDPMs, with only black-box access required for the diffusion denoiser in ownership verification. Extensive experiments demonstrate that our fingerprinting approach enjoys both robustness against the often-seen attacks and distinctiveness on various DDPMs, providing an alternative for protecting DDPMs' IP rights without compromising their performance or integrity.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Teng_Fingerprinting_Denoising_Diffusion_Probabilistic_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Teng_Fingerprinting_Denoising_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Teng_2025_CVPR,
    author    = {Teng, Huan and Quan, Yuhui and Wang, Chengyu and Huang, Jun and Ji, Hui},
    title     = {Fingerprinting Denoising Diffusion Probabilistic Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28811-28820}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Alibaba Cloud Computing", "city": "Hangzhou", "country": "China"}, {"affiliation": "Department of Mathematics, National University of Singapore", "city": "Singapore", "country": "Singapore"}]

affiliations:
["School of Computer Science and Engineering, South China University of Technology", "Alibaba Cloud Computing", "Department of Mathematics, National University of Singapore"]


title: MoST: Efficient Monarch Sparse Tuning for 3D Representation Learning
authors: Xu Han, Yuan Tang, Jinfeng Xu, Xianzhi Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6584-6594
affiliations: ["Huazhong University of Science and Technology"]
cities: ["Wuhan"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Han_MoST_Efficient_Monarch_Sparse_Tuning_for_3D_Representation_Learning_CVPR_2025_paper.html

abstract:
We introduce Monarch Sparse Tuning (MoST), the first reparameterization-based parameter-efficient fine-tuning (PEFT) method tailored for 3D representation learning. Unlike existing adapter-based and prompt-tuning 3D PEFT methods, MoST introduces no additional inference overhead and is compatible with many 3D representation learning backbones. At its core, we present a new family of structured matrices for 3D point clouds, Point Monarch, which can capture local geometric features of irregular points while offering high expressiveness. MoST reparameterizes the dense update weight matrices as our sparse Point Monarch matrices, significantly reducing parameters while retaining strong performance. Experiments on various backbones show that MoST is simple, effective, and highly generalizable. It captures local features in point clouds, achieving state-of-the-art results on multiple benchmarks, e.g., 97.5% acc. on ScanObjectNN (PB_50_RS) and 96.2% on ModelNet40 classification, while it can also combine with other matrix decompositions (e.g., Low-rank, Kronecker) to further reduce parameters.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Han_MoST_Efficient_Monarch_Sparse_Tuning_for_3D_Representation_Learning_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Han_2025_CVPR,
    author    = {Han, Xu and Tang, Yuan and Xu, Jinfeng and Li, Xianzhi},
    title     = {MoST: Efficient Monarch Sparse Tuning for 3D Representation Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6584-6594}
}


title: Re-thinking Temporal Search for Long-Form Video Understanding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8579-8591
affiliations: ["Stanford University", "Northwestern University", "Carnegie Mellon University"]
cities: ["Stanford", "Evanston", "Pittsburgh"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ye_Re-thinking_Temporal_Search_for_Long-Form_Video_Understanding_CVPR_2025_paper.html

authors:
Jinhui Ye, Zihan Wang, Haosen Sun, Keshigeyan Chandrasegaran, Zane Durante, Cristobal Eyzaguirre, Yonatan Bisk, Juan Carlos Niebles, Ehsan Adeli, Li Fei-Fei, Jiajun Wu, Manling Li

abstract:
Efficient understanding of long-form videos remains a significant challenge in computer vision. In this work, we revisit temporal search paradigms for long-form video understanding, studying a fundamental issue pertaining to all state-of-the-art (SOTA) long-context vision-language models (VLMs). In particular, our contributions are two-fold: **First**, we formulate temporal search as a **Long Video Haystack** problem, i.e., finding a minimal set of relevant frames (typically one to five) among tens of thousands of frames from real-world long videos given specific queries. To validate our formulation, we create **LV-Haystack**, the first benchmark containing 3,874 human-annotated instances with fine-grained evaluation metrics for assessing keyframe search quality and computational efficiency. Experimental results on LV-Haystack highlight a significant research gap in temporal search capabilities, with SOTA keyframe selection methods achieving only 2.1% temporal F1 score on the LVBench subset.**Next**, inspired by visual search in images, we re-think temporal searching and propose a lightweight keyframe searching framework, T^* , which casts the expensive temporal search as a spatial search problem. T^*  leverages superior visual localization capabilities typically used in images and introduces an adaptive zooming-in mechanism that operates across both temporal and spatial dimensions. Our extensive experiments show that when integrated with existing methods, T^*  significantly improves SOTA long-form video understanding performance. Specifically, under an inference budget of 32 frames, T^*  improves GPT-4o's performance from 50.5% to **53.1%** and LLaVA-OneVision-72B's performance from 56.5% to **62.4%** on LongVideoBench XL subset. Our PyTorch code, benchmark dataset and models are included in the Supplementary material.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ye_Re-thinking_Temporal_Search_for_Long-Form_Video_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ye_Re-thinking_Temporal_Search_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ye_2025_CVPR,
    author    = {Ye, Jinhui and Wang, Zihan and Sun, Haosen and Chandrasegaran, Keshigeyan and Durante, Zane and Eyzaguirre, Cristobal and Bisk, Yonatan and Niebles, Juan Carlos and Adeli, Ehsan and Fei-Fei, Li and Wu, Jiajun and Li, Manling},
    title     = {Re-thinking Temporal Search for Long-Form Video Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8579-8591}
}

raw_affiliation:
[{"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "Northwestern University", "city": "Evanston", "country": "USA"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}]


authors: Haijie Li, Yanmin Wu, Jiarui Meng, Qiankun Gao, Zhiyao Zhang, Ronggang Wang, Jian Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14078-14088
cities: ["Beijing", "Shenzhen", "Shenyang"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_InstanceGaussian_Appearance-Semantic_Joint_Gaussian_Representation_for_3D_Instance-Level_Perception_CVPR_2025_paper.html

title:
InstanceGaussian: Appearance-Semantic Joint Gaussian Representation for 3D Instance-Level Perception

abstract:
3D scene understanding is vital for applications in autonomous driving, robotics, and augmented reality. However, scene understanding based on 3D Gaussian Splatting faces three key challenges: (i) an imbalance between appearance and semantics, (ii) inconsistencies in object boundaries, and (iii) difficulties with top-down instance segmentation. To address these challenges, we propose InstanceGaussian, a method that jointly learns appearance and semantic features while adaptively aggregating instances. Our contributions are as follows: (i) a new Semantic-Scaffold-GS representation to improve feature representation and boundary delineation, (ii) a progressive training strategy for enhanced stability and segmentation, and (iii) a category-agnostic, bottom-up instance aggregation approach for better segmentation. Experimental results demonstrate that our approach achieves state-of-the-art performance in category-agnostic, open-vocabulary 3D point-level segmentation, validating the effectiveness of our proposed method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_InstanceGaussian_Appearance-Semantic_Joint_Gaussian_Representation_for_3D_Instance-Level_Perception_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_InstanceGaussian_Appearance-Semantic_Joint_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Haijie and Wu, Yanmin and Meng, Jiarui and Gao, Qiankun and Zhang, Zhiyao and Wang, Ronggang and Zhang, Jian},
    title     = {InstanceGaussian: Appearance-Semantic Joint Gaussian Representation for 3D Instance-Level Perception},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14078-14088}
}

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

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


authors: Vaibhav Rathore, Shubhranil B, Saikat Dutta, Sarthak Mehrotra, Zsolt Kira, Biplab Banerjee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4905-4915
affiliations: ["IIT Bombay", "IITB-Monash Research Academy", "Georgia Institute of Technology"]
cities: ["Mumbai", "Mumbai", "Atlanta"]
countries: ["India", "India", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Rathore_When_Domain_Generalization_meets_Generalized_Category_Discovery_An_Adaptive_Task-Arithmetic_CVPR_2025_paper.html

title:
When Domain Generalization meets Generalized Category Discovery: An Adaptive Task-Arithmetic Driven Approach

abstract:
Generalized Class Discovery (GCD) clusters base and novel classes in a target domain, using supervision from a source domain with only base classes. Current methods often falter with distribution shifts and typically require access to target data during training, which can sometimes be impractical. To address this issue, we introduce the novel paradigm of Domain Generalization in GCD (DG-GCD), where only source data is available for training, while the target domain--with a distinct data distribution--remains unseen until inference. To this end, our solution, DG2CD-Net, aims to construct a domain-independent, discriminative embedding space for GCD. The core innovation is an episodic training strategy that enhances cross-domain generalization by adapting a base model on tasks derived from source and synthetic domains generated by a foundation model. Each episode focuses on a cross-domain GCD task, diversifying task setups over episodes and combining open-set domain adaptation with a novel margin loss and representation learning for optimizing the feature space progressively. To capture the effects of fine-tunings on the base model, we extend task arithmetic by adaptively weighting the local task vectors concerning the fine-tuned models based on their GCD performance on a validation distribution. This episodic update mechanism boosts the adaptability of the base model to unseen targets. Experiments across three datasets confirm that DG2CD-Net outperforms existing GCD methods customized for DG-GCD

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Rathore_When_Domain_Generalization_meets_Generalized_Category_Discovery_An_Adaptive_Task-Arithmetic_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Rathore_When_Domain_Generalization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Rathore_2025_CVPR,
    author    = {Rathore, Vaibhav and B, Shubhranil and Dutta, Saikat and Mehrotra, Sarthak and Kira, Zsolt and Banerjee, Biplab},
    title     = {When Domain Generalization meets Generalized Category Discovery: An Adaptive Task-Arithmetic Driven Approach},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4905-4915}
}

raw_affiliation:
[{"affiliation": "IIT Bombay", "city": "Mumbai", "country": "India"}, {"affiliation": "IITB-Monash Research Academy", "city": "Mumbai", "country": "India"}, {"affiliation": "Georgia Institute of Technology", "city": "Atlanta", "country": "USA"}]


authors: Zhenhui Ding, Guilian Chen, Qin Zhang, Huisi Wu, Jing Qin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15632-15641
cities: ["Shenzhen", "Hong Kong"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ding_CSC-PA_Cross-image_Semantic_Correlation_via_Prototype_Attentions_for_Single-network_Semi-supervised_CVPR_2025_paper.html

title:
CSC-PA: Cross-image Semantic Correlation via Prototype Attentions for Single-network Semi-supervised Breast Tumor Segmentation

abstract:
Accurate automatic breast ultrasound (BUS) image segmentation is essential for early breast cancer screening and diagnosis. However, it remains challenging owing to (1) breast lesions of various scale and shape, (2) ambiguous boundaries caused by speckle noise and artifacts, and (3) the scarcity of high-quality annotations. Most existing semi-supervised methods employ the mean-teacher architecture, which merely learns semantic information within a single image and heavily relies on the performance of the teacher model. Therefore, we present a novel cross-image semantic correlation semi-supervised framework, named CSC-PA, to improve the performance of BUS image segmentation. CSC-PA is trained based on a single network, which integrates a foreground prototype attention (FPA) and an edge prototype attention (EPA). Specifically, FPA transfers complementary foreground information for more stable and complete lesion segmentation. On the other hand, EPA enhances edge features of lesions by using edge prototype, where an adaptive edge container is proposed to store global edge features and generate the edge prototype. Additionally, we introduce a pixel affinity loss (PAL) to exploit previously ignored contextual correlation in supervision, which further improves performance on edges. Extensive experiments on two benchmark BUS datasets demonstrate that our model outperforms other state-of-the-art methods under different partition protocols. Codes are available at https://github.com/shdkdh/CSC-PA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ding_CSC-PA_Cross-image_Semantic_Correlation_via_Prototype_Attentions_for_Single-network_Semi-supervised_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ding_CSC-PA_Cross-image_Semantic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ding_2025_CVPR,
    author    = {Ding, Zhenhui and Chen, Guilian and Zhang, Qin and Wu, Huisi and Qin, Jing},
    title     = {CSC-PA: Cross-image Semantic Correlation via Prototype Attentions for Single-network Semi-supervised Breast Tumor Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15632-15641}
}

raw_affiliation:
[{"affiliation": "College of Computer Science and Software Engineering, Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University", "city": "Hong Kong", "country": "China"}]

affiliations:
["College of Computer Science and Software Engineering, Shenzhen University", "Centre for Smart Health, School of Nursing, The Hong Kong Polytechnic University"]


title: BIP3D: Bridging 2D Images and 3D Perception for Embodied Intelligence
authors: Xuewu Lin, Tianwei Lin, Lichao Huang, Hongyu Xie, Zhizhong Su
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9007-9016
raw_affiliation: [{"affiliation": "Horizon Robotics", "city": "Beijing", "country": "China"}]
affiliations: ["Horizon Robotics"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_BIP3D_Bridging_2D_Images_and_3D_Perception_for_Embodied_Intelligence_CVPR_2025_paper.html

abstract:
In embodied intelligence systems, a key component is 3D perception algorithm, which enables agents to understand their surrounding environments. Previous algorithms primarily rely on point cloud, which, despite offering precise geometric information, still constrain perception performance due to inherent sparsity, noise, and data scarcity. In this work, we introduce a novel image-centric 3D perception model, BIP3D, which leverages expressive image features with explicit 3D position encoding to overcome the limitations of point-centric methods. Specifically, we leverage pre-trained 2D vision foundation models to enhance semantic understanding, and introduce a spatial enhancer module to improve spatial understanding. Together, these modules enable BIP3D to achieve multi-view, multi-modal feature fusion and end-to-end 3D perception. In our experiments, BIP3D outperforms current state-of-the-art results on the EmbodiedScan benchmark, achieving improvements of 5.69% in the 3D detection task and 15.25% in the 3D visual grounding task. Code has been released at https://github.com/HorizonRobotics/BIP3D.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_BIP3D_Bridging_2D_Images_and_3D_Perception_for_Embodied_Intelligence_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_BIP3D_Bridging_2D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Xuewu and Lin, Tianwei and Huang, Lichao and Xie, Hongyu and Su, Zhizhong},
    title     = {BIP3D: Bridging 2D Images and 3D Perception for Embodied Intelligence},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9007-9016}
}


title: Query Efficient Black-Box Visual Prompting with Subspace Learning
authors: Zhaogeng Liu, Haozhen Zhang, Hualin Zhang, Xingchen Li, Wanli Shi, Bin Gu, Yi Chang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4322-4331
cities: ["Changchun", "Abu Dhabi", "Beijing"]
countries: ["China", "UAE", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Query_Efficient_Black-Box_Visual_Prompting_with_Subspace_Learning_CVPR_2025_paper.html

abstract:
Visual Prompt Learning (VPL) has emerged as a powerful strategy for harnessing the capabilities of large-scale pre-trained models (PTMs) to tackle specific downstream tasks. However, the opaque nature of PTMs in many real-world applications has led to a growing interest in gradient-free approaches within VPL. A significant challenge with existing black-box VPL methods lies in the high dimensionality of visual prompts, which necessitates considerable API queries for tuning, thereby impacting efficiency. To address this issue, we propose a novel query-efficient framework for black-box visual prompting, designed to generate input-dependent visual prompts efficiently for large-scale black-box PTMs. Our framework is built upon the insight of reparameterizing prompts using neural networks, improving the typical pre-training-fine-tuning paradigm through the subspace learning strategy to maximize efficiency and adaptability from both the perspective of initial weights and parameter dimensionality. This tuning intrinsically optimizes low-dimensional representations within the well-learned subspace, enabling the efficient adaptation of the network to downstream tasks. Our approach significantly reduces the necessity for substantial API queries to PTMs, presenting an efficient method for leveraging large-scale black-box PTMs in visual prompting tasks. Most experimental results across various benchmarks demonstrate the effectiveness of our method, showcasing substantial reductions in the number of required API queries to PTMs while maintaining or even enhancing performance on downstream tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Query_Efficient_Black-Box_Visual_Prompting_with_Subspace_Learning_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Zhaogeng and Zhang, Haozhen and Zhang, Hualin and Li, Xingchen and Shi, Wanli and Gu, Bin and Chang, Yi},
    title     = {Query Efficient Black-Box Visual Prompting with Subspace Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4322-4331}
}

raw_affiliation:
[{"affiliation": "School of Artificial Intelligence, Jilin University, 2699 Qianjin Street, Changchun, China", "city": "Changchun", "country": "China"}, {"affiliation": "Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE", "city": "Abu Dhabi", "country": "UAE"}, {"affiliation": "Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE, China", "city": "Beijing", "country": "China"}]

affiliations:
["School of Artificial Intelligence, Jilin University, 2699 Qianjin Street, Changchun, China", "Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE", "Engineering Research Center of Knowledge-Driven Human-Machine Intelligence, MOE, China"]


title: VisionPAD: A Vision-Centric Pre-training Paradigm for Autonomous Driving
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17165-17175
affiliations: ["FNii", "SSE, CUHK-Shenzhen", "HKUST", "Huawei Noah\u2019s Ark Lab"]
cities: ["Shenzhen", "Shenzhen", "Hong Kong", "Unknown"]
countries: ["China", "China", "China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_VisionPAD_A_Vision-Centric_Pre-training_Paradigm_for_Autonomous_Driving_CVPR_2025_paper.html

authors:
Haiming Zhang, Wending Zhou, Yiyao Zhu, Xu Yan, Jiantao Gao, Dongfeng Bai, Yingjie Cai, Bingbing Liu, Shuguang Cui, Zhen Li

abstract:
This paper introduces VisionPAD, a novel self-supervised pre-training paradigm designed for vision-centric algorithms in autonomous driving. In contrast to previous approaches that employ neural rendering with explicit depth supervision, VisionPAD utilizes more efficient 3D Gaussian Splatting to reconstruct multi-view representations using only images as supervision. Specifically, we introduce a self-supervised method for voxel velocity estimation. By warping voxels to adjacent frames and supervising the rendered outputs, the model effectively learns motion cues in the sequential data. Furthermore, we adopt a multi-frame photometric consistency approach to enhance geometric perception. It projects adjacent frames to the current frame based on rendered depths and relative poses, boosting the 3D geometric representation through pure image supervision. Extensive experiments on autonomous driving datasets demonstrate that VisionPAD significantly improves performance in 3D object detection, occupancy prediction and map segmentation, surpassing state-of-the-art pre-training strategies by a considerable margin.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_VisionPAD_A_Vision-Centric_Pre-training_Paradigm_for_Autonomous_Driving_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_VisionPAD_A_Vision-Centric_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Haiming and Zhou, Wending and Zhu, Yiyao and Yan, Xu and Gao, Jiantao and Bai, Dongfeng and Cai, Yingjie and Liu, Bingbing and Cui, Shuguang and Li, Zhen},
    title     = {VisionPAD: A Vision-Centric Pre-training Paradigm for Autonomous Driving},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17165-17175}
}

raw_affiliation:
[{"affiliation": "FNii", "city": "Shenzhen", "country": "China"}, {"affiliation": "SSE, CUHK-Shenzhen", "city": "Shenzhen", "country": "China"}, {"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}, {"affiliation": "Huawei Noah\u2019s Ark Lab", "city": "Unknown", "country": "Unknown"}]


title: Detecting Adversarial Data Using Perturbation Forgery
authors: Qian Wang, Chen Li, Yuchen Luo, Hefei Ling, Shijuan Huang, Ruoxi Jia, Ning Yu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13917-13926
cities: ["Wuhan", "Wuhan", "Blacksburg", "Los Gatos"]
countries: ["China", "China", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Detecting_Adversarial_Data_Using_Perturbation_Forgery_CVPR_2025_paper.html

abstract:
As a defense strategy against adversarial attacks, adversarial detection aims to identify and filter out adversarial data from the data flow based on discrepancies in distribution and noise patterns between natural and adversarial data. Although previous detection methods achieve high performance in detecting gradient-based adversarial attacks, new attacks based on generative models with imbalanced and anisotropic noise patterns evade detection. Even worse, the significant inference time overhead and limited performance against unseen attacks make existing techniques impractical for real-world use. In this paper, we explore the proximity relationship among adversarial noise distributions and demonstrate the existence of an open covering for these distributions. By training on the open covering of adversarial noise distributions, a detector with strong generalization performance against various types of unseen attacks can be developed. Based on this insight, we heuristically propose Perturbation Forgery, which includes noise distribution perturbation, sparse mask generation, and pseudo-adversarial data production, to train an adversarial detector capable of detecting any unseen gradient-based, generative-based, and physical adversarial attacks. Comprehensive experiments conducted on multiple general and facial datasets, with a wide spectrum of attacks, validate the strong generalization of our method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Detecting_Adversarial_Data_Using_Perturbation_Forgery_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Detecting_Adversarial_Data_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Qian and Li, Chen and Luo, Yuchen and Ling, Hefei and Huang, Shijuan and Jia, Ruoxi and Yu, Ning},
    title     = {Detecting Adversarial Data Using Perturbation Forgery},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13917-13926}
}

raw_affiliation:
[{"affiliation": "Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Virginia Tech", "city": "Blacksburg", "country": "USA"}, {"affiliation": "Netflix Eyeline", "city": "Los Gatos", "country": "USA"}]

affiliations:
["Huazhong University of Science and Technology", "Wuhan University", "Virginia Tech", "Netflix Eyeline"]


title: CoA: Towards Real Image Dehazing via Compression-and-Adaptation
authors: Long Ma, Yuxin Feng, Yan Zhang, Jinyuan Liu, Weimin Wang, Guang-Yong Chen, Chengpei Xu, Zhuo Su
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11197-11206
cities: ["Dalian", "Guangzhou", "Fuzhou", "Sydney"]
countries: ["China", "China", "China", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ma_CoA_Towards_Real_Image_Dehazing_via_Compression-and-Adaptation_CVPR_2025_paper.html

abstract:
Learning-based image dehazing algorithms have shown remarkable success in synthetic domains. However, real image dehazing is still in suspense due to computational resource constraints and the diversity of real-world scenes. Therefore, there is an urgent need for an algorithm that excels in both efficiency and adaptability to address real image dehazing effectively. This work proposes a Compression-and-Adaptation (CoA) computational flow to tackle these challenges from a divide-and-conquer perspective. First, model compression is performed in the synthetic domain to develop a compact dehazing parameter space, satisfying efficiency demands. Then, a bilevel adaptation in the real domain is introduced to be fearless in unknown real environments by aggregating the synthetic dehazing capabilities during the learning process. Leveraging a succinct design free from additional constraints, our CoA exhibits domain-irrelevant stability and model-agnostic flexibility,  effectively bridging the model chasm between synthetic and real domains to further improve its practical utility. Extensive evaluations and analyses underscore the approach's superiority and effectiveness. The code is publicly available at https://github.com/fyxnl/COA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_CoA_Towards_Real_Image_Dehazing_via_Compression-and-Adaptation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Ma_2025_CVPR,
    author    = {Ma, Long and Feng, Yuxin and Zhang, Yan and Liu, Jinyuan and Wang, Weimin and Chen, Guang-Yong and Xu, Chengpei and Su, Zhuo},
    title     = {CoA: Towards Real Image Dehazing via Compression-and-Adaptation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11197-11206}
}

raw_affiliation:
[{"affiliation": "Dalian University of Technology", "city": "Dalian", "country": "China"}, {"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Fuzhou University", "city": "Fuzhou", "country": "China"}, {"affiliation": "University of New South Wales", "city": "Sydney", "country": "Australia"}]

affiliations:
["Dalian University of Technology", "Sun Yat-sen University", "Fuzhou University", "University of New South Wales"]


title: NightAdapter: Learning a Frequency Adapter for Generalizable Night-time Scene Segmentation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23838-23849
cities: ["Hangzhou", "Shenzhen", "Amsterdam", "Melbourne", "Macau"]
countries: ["China", "China", "Netherlands", "Australia", "Macau"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bi_NightAdapter_Learning_a_Frequency_Adapter_for_Generalizable_Night-time_Scene_Segmentation_CVPR_2025_paper.html

authors:
Qi Bi, Jingjun Yi, Huimin Huang, Hao Zheng, Haolan Zhan, Yawen Huang, Yuexiang Li, Xian Wu, Yefeng Zheng

abstract:
Night-time scene segmentation is a critical yet challenging task in the real-world applications, primarily due to the complicated lighting conditions. However, existing methods lack sufficient generalization ability to unseen nigh-time scenes with varying illumination.In light of this issue, we focus on investigating generalizable paradigms for night-time scene segmentation and propose an efficient fine-tuning scheme, dubbed as \texttt NightAdapter , alleviating the domain gap across various scenes.Interestingly, different properties embedded in the day-time and night-time features can be characterized by the bands after discrete sine transformation, which can be categorized into illumination-sensitive/-insensitive bands.Hence, our \texttt NightAdapter  is powered by two appealing designs: (1) Illumination-Insensitive Band Adaptation that provides a foundation for understanding the prior, enhancing the robustness to illumination shifts; (2) Illumination-Sensitive Band Adaptation that fine-tunes the randomized frequency bands, mitigating the domain gap between the day-time and various night-time scenes. As a consequence, illumination-insensitive enhancement improves the domain invariance, while illumination-sensitive diminution strengthens the domain shift between different scenes.\texttt NightAdapter  yields significant improvements over the state-of-the-art methods under various day-to-night, night-to-night, and in-domain night segmentation experiments.We will release our code.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bi_NightAdapter_Learning_a_Frequency_Adapter_for_Generalizable_Night-time_Scene_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bi_NightAdapter_Learning_a_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bi_2025_CVPR,
    author    = {Bi, Qi and Yi, Jingjun and Huang, Huimin and Zheng, Hao and Zhan, Haolan and Huang, Yawen and Li, Yuexiang and Wu, Xian and Zheng, Yefeng},
    title     = {NightAdapter: Learning a Frequency Adapter for Generalizable Night-time Scene Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23838-23849}
}

raw_affiliation:
[{"affiliation": "Westlake University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Jarvis Research Center, Tencent Youtu Lab", "city": "Shenzhen", "country": "China"}, {"affiliation": "University of Amsterdam", "city": "Amsterdam", "country": "Netherlands"}, {"affiliation": "Monash University", "city": "Melbourne", "country": "Australia"}, {"affiliation": "Macau University", "city": "Macau", "country": "Macau"}]

affiliations:
["Westlake University", "Jarvis Research Center, Tencent Youtu Lab", "University of Amsterdam", "Monash University", "Macau University"]


authors: Meng Pang, Wenjun Zhang, Nanrun Zhou, Shengbo Chen, Hong Rao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29299-29308
affiliations: ["Nanchang University", "Shanghai University of Engineering Science"]
cities: ["Nanchang", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pang_UMFN_Unified_Multi-Domain_Face_Normalization_for_Joint_Cross-domain_Prototype_Learning_CVPR_2025_paper.html

title:
UMFN: Unified Multi-Domain Face Normalization for Joint Cross-domain Prototype Learning and Heterogeneous Face Recognition

abstract:
Face normalization aims to enhance the robustness and effectiveness of face recognition systems by mitigating intra-personal variations in expressions, poses, occlusions, illuminations, and domains. Existing methods face limitations in handling multiple variations and adapting to cross-domain scenarios. To address these challenges, we propose a novel Unified Multi-Domain Face Normalization Network (UMFN) model, which can process face images with various types of facial variations from different domains, and reconstruct frontal, neutral-expression facial prototypes in the target domain. As an unsupervised domain adaptation model, UMFN facilitates concurrent training on multiple datasets across domains and demonstrates strong prototype reconstruction capabilities. Notably, UMFN serves as a joint prototype and feature learning framework, enabling the simultaneous extraction of domain-agnostic identity features through a decoupling mapping network and a feature domain classifier for adversarial training. Moreover, we design an efficient Heterogeneous Face Recognition (HFR) network that fuses domain-agnostic and identity-discriminative features for HFR, and introduce contrastive learning to enhance identity recognition accuracy. Empirical studies on diverse cross-domain face datasets validate the effectiveness of our proposed method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pang_UMFN_Unified_Multi-Domain_Face_Normalization_for_Joint_Cross-domain_Prototype_Learning_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Pang_2025_CVPR,
    author    = {Pang, Meng and Zhang, Wenjun and Zhou, Nanrun and Chen, Shengbo and Rao, Hong},
    title     = {UMFN: Unified Multi-Domain Face Normalization for Joint Cross-domain Prototype Learning and Heterogeneous Face Recognition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29299-29308}
}

raw_affiliation:
[{"affiliation": "Nanchang University", "city": "Nanchang", "country": "China"}, {"affiliation": "Shanghai University of Engineering Science", "city": "Shanghai", "country": "China"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19803-19813
cities: ["New Brunswick", "Houston", "Salt Lake City", "Fullerton"]
countries: ["USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_TopV_Compatible_Token_Pruning_with_Inference_Time_Optimization_for_Fast_CVPR_2025_paper.html

title:
TopV: Compatible Token Pruning with Inference Time Optimization for Fast and Low-Memory Multimodal Vision Language Model

authors:
Cheng Yang, Yang Sui, Jinqi Xiao, Lingyi Huang, Yu Gong, Chendi Li, Jinghua Yan, Yu Bai, Ponnuswamy Sadayappan, Xia Hu, Bo Yuan

abstract:
Vision-Language Models (VLMs) demand substantial computational resources during inference, largely due to the extensive visual input tokens for representing visual information. Previous studies have noted that visual tokens tend to receive less attention than text tokens, suggesting their lower importance during inference and potential for pruning. However, their methods encounter several chal- lenges: reliance on greedy heuristic criteria for token im- portance and incompatibility with FlashAttention and KV cache. To address these issues, we introduce TopV, a com- patible TOken Pruning with inference Time Optimization for fast and low-memory VLM, achieving efficient pruning without additional training or fine-tuning. Instead of re- lying on attention scores, we formulate token pruning as an optimization problem, accurately identifying important visual tokens while remaining compatible with FlashAtten- tion. Additionally, since we only perform this pruning once during the prefilling stage, it effectively reduces KV cache size. Our optimization framework incorporates a visual- aware cost function considering factors such as Feature Similarity, Relative Spatial Distance, and Absolute Central Distance, to measure the importance of each source visual token, enabling effective pruning of low-importance tokens. Extensive experiments demonstrate that our method outperforms previous token pruning methods, validating the effectiveness and efficiency of our approach.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_TopV_Compatible_Token_Pruning_with_Inference_Time_Optimization_for_Fast_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_TopV_Compatible_Token_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Cheng and Sui, Yang and Xiao, Jinqi and Huang, Lingyi and Gong, Yu and Li, Chendi and Yan, Jinghua and Bai, Yu and Sadayappan, Ponnuswamy and Hu, Xia and Yuan, Bo},
    title     = {TopV: Compatible Token Pruning with Inference Time Optimization for Fast and Low-Memory Multimodal Vision Language Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19803-19813}
}

raw_affiliation:
[{"affiliation": "Rutgers University", "city": "New Brunswick", "country": "USA"}, {"affiliation": "Rice University", "city": "Houston", "country": "USA"}, {"affiliation": "The University of Utah", "city": "Salt Lake City", "country": "USA"}, {"affiliation": "California State University, Fullerton", "city": "Fullerton", "country": "USA"}]

affiliations:
["Rutgers University", "Rice University", "The University of Utah", "California State University, Fullerton"]


title: Improving Autoregressive Visual Generation with Cluster-Oriented Token Prediction
authors: Teng Hu, Jiangning Zhang, Ran Yi, Jieyu Weng, Yabiao Wang, Xianfang Zeng, Zhucun Xue, Lizhuang Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9351-9360
affiliations: ["Shanghai Jiao Tong University", "Youtu Lab, Tencent", "Zhejiang University"]
cities: ["Shanghai", "Unknown", "Hangzhou"]
countries: ["China", "Unknown", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hu_Improving_Autoregressive_Visual_Generation_with_Cluster-Oriented_Token_Prediction_CVPR_2025_paper.html

abstract:
Employing LLMs for visual generation has recently become a research focus. However, the existing methods primarily transfer the LLM architecture to visual generation but rarely investigate the fundamental differences between language and vision. This oversight may lead to suboptimal utilization of visual generation capabilities within the LLM framework. In this paper, we explore the characteristics of visual embedding space under the LLM framework and discover that the correlation between visual embeddings can help achieve more stable and robust generation results. We present **IAR**, an **I**mproved **A**uto**R**egressive Visual Generation Method that enhances the training efficiency and generation quality of LLM-based visual generation models. Firstly, we propose a Codebook Rearrangement strategy that uses balanced k-means clustering algorithm to rearrange the visual codebook into clusters, ensuring high similarity among visual features within each cluster. Leveraging the rearranged codebook, we propose a Cluster-oriented Cross-entropy Loss that guides the model to correctly predict the cluster where the token is located. This approach ensures that even if the model predicts the wrong token index, there is a high probability the predicted token is located in the correct cluster, which significantly enhances the generation quality and robustness. Extensive experiments demonstrate that our method consistently enhances the model training efficiency and performance from 100M to 1.4B, reducing the training time by half while achieving the same FID. Additionally, our approach can be applied to various LLM-based visual generation models and adheres to the scaling law, providing a promising direction for future research in LLM-based visual generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hu_Improving_Autoregressive_Visual_Generation_with_Cluster-Oriented_Token_Prediction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hu_Improving_Autoregressive_Visual_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hu_2025_CVPR,
    author    = {Hu, Teng and Zhang, Jiangning and Yi, Ran and Weng, Jieyu and Wang, Yabiao and Zeng, Xianfang and Xue, Zhucun and Ma, Lizhuang},
    title     = {Improving Autoregressive Visual Generation with Cluster-Oriented Token Prediction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9351-9360}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Youtu Lab, Tencent", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]


title: Learned Binocular-Encoding Optics for RGBD Imaging Using Joint Stereo and Focus Cues
authors: Yuhui Liu, Liangxun Ou, Qiang Fu, Hadi Amata, Wolfgang Heidrich, Yifan Peng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15833-15842
affiliations: ["The University of Hong Kong", "King Abdullah University of Science and Technology"]
cities: ["Hong Kong", "Thuwal"]
countries: ["China", "Saudi Arabia"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Learned_Binocular-Encoding_Optics_for_RGBD_Imaging_Using_Joint_Stereo_and_CVPR_2025_paper.html

abstract:
Extracting high-fidelity RGBD information from two-dimensional (2D) images is essential for various visual computing applications. Stereo imaging, as a reliable passive imaging technique for obtaining three-dimensional (3D) scene information, has benefited greatly from deep learning advancements. However, existing stereo depth estimation algorithms struggle to perceive high-frequency information and resolve high-resolution depth maps in realistic camera settings with large depth variations. These algorithms commonly neglect the hardware parameter configuration, limiting the potential for achieving optimal solutions solely through software-based design strategies.This work presents a hardware-software co-designed RGBD imaging framework that leverages both stereo and focus cues to reconstruct texture-rich color images along with detailed depth maps over a wide depth range. A pair of rank-2 parameterized diffractive optical elements (DOEs) is employed to encode perpendicular complementary information optically during stereo acquisitions. Additionally, we employ an IGEV-UNet-fused neural network tailored to the proposed rank-2 encoding for stereo matching and image reconstruction. Through prototyping a stereo camera with customized DOEs, our deep stereo imaging paradigm has demonstrated superior performance over existing monocular and stereo imaging systems in both image PSNR by 2.96 dB gain and depth accuracy in high-frequency details across distances from 0.67 to 8 meters.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Learned_Binocular-Encoding_Optics_for_RGBD_Imaging_Using_Joint_Stereo_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Learned_Binocular-Encoding_Optics_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yuhui and Ou, Liangxun and Fu, Qiang and Amata, Hadi and Heidrich, Wolfgang and Peng, Yifan},
    title     = {Learned Binocular-Encoding Optics for RGBD Imaging Using Joint Stereo and Focus Cues},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15833-15842}
}

raw_affiliation:
[{"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "King Abdullah University of Science and Technology", "city": "Thuwal", "country": "Saudi Arabia"}]


title: Dual-view X-ray Detection: Can AI Detect Prohibited Items from Dual-view X-ray Images like Humans?
authors: Renshuai Tao, Haoyu Wang, Yuzhe Guo, Hairong Chen, Li Zhang, Xianglong Liu, Yunchao Wei, Yao Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10338-10347
cities: ["Beijing", "Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tao_Dual-view_X-ray_Detection_Can_AI_Detect_Prohibited_Items_from_Dual-view_CVPR_2025_paper.html

abstract:
To detect prohibited items in challenging categories, human inspectors typically rely on images from two distinct views (vertical and side). Can AI detect prohibited items from dual-view X-ray images in the same way humans do? Existing X-ray datasets often suffer from limitations, such as single-view imaging or insufficient sample diversity. To address these gaps, we introduce the Large-scale Dual-view X-ray (LDXray), which consists of 353,646 instances across 12 categories, providing a diverse and comprehensive resource for training and evaluating models. To emulate human intelligence in dual-view detection, we propose the Auxiliary-view Enhanced Network (AENet), a novel detection framework that leverages both the main and auxiliary views of the same object. The main-view pipeline focuses on detecting common categories, while the auxiliary-view pipeline handles more challenging categories using "expert models" learned from the main view. Extensive experiments on the LDXray dataset demonstrate that the dual-view mechanism significantly enhances detection performance, e.g., achieving improvements of up to +24.7%for the challenging category of umbrellas. Furthermore, our results show that AENet exhibits strong generalization across seven different detection models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tao_Dual-view_X-ray_Detection_Can_AI_Detect_Prohibited_Items_from_Dual-view_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Tao_2025_CVPR,
    author    = {Tao, Renshuai and Wang, Haoyu and Guo, Yuzhe and Chen, Hairong and Zhang, Li and Liu, Xianglong and Wei, Yunchao and Zhao, Yao},
    title     = {Dual-view X-ray Detection: Can AI Detect Prohibited Items from Dual-view X-ray Images like Humans?},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10338-10347}
}

raw_affiliation:
[{"affiliation": "Visual Intellgence +X International Cooperation Joint Laboratory of MOE, Beijing Jiaotong University", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Laboratory of Complex & Critical Software Environment, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "Department of Engineering Physics, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "National Engineering Laboratory for Dangerous Articles and Explosives Detection T echnologies", "city": "Beijing", "country": "China"}]

affiliations:
["Visual Intellgence +X International Cooperation Joint Laboratory of MOE, Beijing Jiaotong University", "State Key Laboratory of Complex & Critical Software Environment, Beihang University", "Department of Engineering Physics, Tsinghua University", "National Engineering Laboratory for Dangerous Articles and Explosives Detection T echnologies"]


title: LUCAS: Layered Universal Codec Avatars
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21127-21137
affiliations: ["Codec Avatars Lab, Meta", "Rutgers University"]
cities: ["Menlo Park", "New Brunswick"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_LUCAS_Layered_Universal_Codec_Avatars_CVPR_2025_paper.html

authors:
Di Liu, Teng Deng, Giljoo Nam, Yu Rong, Stanislav Pidhorskyi, Junxuan Li, Jason Saragih, Dimitris N. Metaxas, Chen Cao

abstract:
Photorealistic 3D head avatar reconstruction faces critical challenges in modeling dynamic face-hair interactions and achieving cross-identity generalization, particularly during expressions and head movements. We present LUCAS, a novel Universal Prior Model (UPM) for codec avatar modeling that disentangles face and hair through a layered representation. Unlike previous UPMs that treat hair as an integral part of the head, our approach separates the modeling of the hairless head and hair into distinct branches. LUCAS is the first to introduce a mesh-based UPM, facilitating real-time rendering on devices. LUCAS can be integrated with Gaussian Splatting to enhance visual fidelity, a feature particularly beneficial for rendering complex hairstyles. Experimental results indicate that LUCAS outperforms existing single-mesh and Gaussian-based avatar models in both quantitative and qualitative assessments, including evaluations on held-out subjects in zero-shot driving scenarios. LUCAS demonstrates superior dynamic performance in managing head pose changes, expression transfer, and hairstyle variations, thereby advancing the state-of-the-art in 3D head avatar reconstruction.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_LUCAS_Layered_Universal_Codec_Avatars_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_LUCAS_Layered_Universal_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Di and Deng, Teng and Nam, Giljoo and Rong, Yu and Pidhorskyi, Stanislav and Li, Junxuan and Saragih, Jason and Metaxas, Dimitris N. and Cao, Chen},
    title     = {LUCAS: Layered Universal Codec Avatars},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21127-21137}
}

raw_affiliation:
[{"affiliation": "Codec Avatars Lab, Meta", "city": "Menlo Park", "country": "USA"}, {"affiliation": "Rutgers University", "city": "New Brunswick", "country": "USA"}]


title: MobilePortrait: Real-Time One-Shot Neural Head Avatars on Mobile Devices
authors: Jianwen Jiang, Gaojie Lin, Zhengkun Rong, Chao Liang, Yongming Zhu, Jiaqi Yang, Tianyun Zhong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15920-15929
affiliations: ["Bytedance Intelligent Creation", "Zhejiang University"]
cities: ["Beijing", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_MobilePortrait_Real-Time_One-Shot_Neural_Head_Avatars_on_Mobile_Devices_CVPR_2025_paper.html

abstract:
Existing neural head avatars methods have achieved significant progress in the image quality and motion range of portrait animation. However, these methods prioritize effectiveness over computational overhead. This paper presents MobilePortrait, a lightweight one-shot neural head avatars method that reduces learning complexity by integrating external knowledge into both the motion modeling and image synthesis, enabling real-time inference on mobile devices. Specifically, We introduce a mixed keypoint representation of explicit and implicit keypoints for precise motion modeling and equip it with precomputed visual features to enhance both facial and background synthesis. With these designs, our model can achieve state-of-the-art performance using UNet with low FLOPs as the backbone, requiring less than 1/10 the computational demand. It has been validated to reach speeds of over 50 FPS on mobile devices and support both video and audio-driven inputs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jiang_MobilePortrait_Real-Time_One-Shot_Neural_Head_Avatars_on_Mobile_Devices_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jiang_MobilePortrait_Real-Time_One-Shot_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Jianwen and Lin, Gaojie and Rong, Zhengkun and Liang, Chao and Zhu, Yongming and Yang, Jiaqi and Zhong, Tianyun},
    title     = {MobilePortrait: Real-Time One-Shot Neural Head Avatars on Mobile Devices},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15920-15929}
}

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


title: D^3: Scaling Up Deepfake Detection by Learning from Discrepancy
authors: Yongqi Yang, Zhihao Qian, Ye Zhu, Olga Russakovsky, Yu Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23850-23859
cities: ["Wuhan", "Princeton"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_D3_Scaling_Up_Deepfake_Detection_by_Learning_from_Discrepancy_CVPR_2025_paper.html

abstract:
The boom of Generative AI brings opportunities entangled with risks and concerns. Existing literature emphasizes the generalization capability of deepfake detection on unseen generators, significantly promoting the detector's ability to identify more universal artifacts. This work seeks a step toward a universal deepfake detection system with better generalization and robustness. We do so by first scaling up the existing detection task setup from the one-generator to multiple-generators in training, during which we disclose two challenges presented in prior methodological designs and demonstrate the divergence of detectors' performance. Specifically, we reveal that the current methods tailored for training on one specific generator either struggle to learn comprehensive artifacts from multiple generators or sacrifice their fitting ability for seen generators (i.e., In-Domain (ID) performance) to exchange the generalization for unseen generators (i.e., Out-Of-Domain (OOD) performance). To tackle the above challenges, we propose our Discrepancy Deepfake Detector (D3) framework, whose core idea is to deconstruct the universal artifacts from multiple generators by introducing a parallel network branch that takes a distorted image feature as an extra discrepancy signal and supplements its original counterpart. Extensive scaled-up experiments demonstrate the effectiveness of D3, achieving 5.3% accuracy improvement in the OOD testing compared to the current SOTA methods while maintaining the ID performance. The source code will be updated in our GitHub repository: https://github.com/BigAandSmallq/D3.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_D3_Scaling_Up_Deepfake_Detection_by_Learning_from_Discrepancy_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_D3_Scaling_Up_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Yongqi and Qian, Zhihao and Zhu, Ye and Russakovsky, Olga and Wu, Yu},
    title     = {D{\textasciicircum}3: Scaling Up Deepfake Detection by Learning from Discrepancy},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23850-23859}
}

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

affiliations:
["School of Computer Science, Wuhan University", "Department of Computer Science, Princeton University"]


title: Jailbreaking the Non-Transferable Barrier via Test-Time Data Disguising
authors: Yongli Xiang, Ziming Hong, Lina Yao, Dadong Wang, Tongliang Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30671-30681
affiliations: ["The University of Sydney", "CSIRO", "The University of New South Wales"]
cities: ["Sydney", "Sydney", "Sydney"]
countries: ["Australia", "Australia", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xiang_Jailbreaking_the_Non-Transferable_Barrier_via_Test-Time_Data_Disguising_CVPR_2025_paper.html

abstract:
Non-transferable learning (NTL) has been proposed to protect model intellectual property (IP) by creating a "non-transferable barrier" to restrict generalization from authorized to unauthorized domains. Recently, well-designed attack, which restores the unauthorized-domain performance by fine-tuning NTL models on few authorized samples, highlights the security risks of NTL-based applications. However, such attack requires modifying model weights, thus being invalid in the black-box scenario. This raises a critical question: can we trust the security of NTL models deployed as black-box systems? In this work, we reveal the first loophole of black-box NTL models by proposing a novel attack method (dubbed as JailNTL) to jailbreak the non-transferable barrier through test-time data disguising, The main idea of JailNTL is to disguise unauthorized data so it can be identified as authorized by the NTL model, thereby bypassing the non-transferable barrier without modifying the NTL model weights. Specifically, JailNTL encourages unauthorized-domain disguising in two levels, including: (i) data-intrinsic disguising (DID) for eliminating domain discrepancy and preserving class-related content at the input-level, and (ii) model-guided disguising (MGD) for mitigating output-level statistics difference of the NTL model. Empirically, when attacking state-of-the-art (SOTA) NTL models in the black-box scenario, JailNTL achieves an accuracy increase of up to 55.7% in the unauthorized domain by using only 1% authorized samples, largely exceeding existing SOTA white-box attacks. Code is released at https://github.com/tmllab/2025_CVPR_JailNTL.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xiang_Jailbreaking_the_Non-Transferable_Barrier_via_Test-Time_Data_Disguising_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xiang_Jailbreaking_the_Non-Transferable_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xiang_2025_CVPR,
    author    = {Xiang, Yongli and Hong, Ziming and Yao, Lina and Wang, Dadong and Liu, Tongliang},
    title     = {Jailbreaking the Non-Transferable Barrier via Test-Time Data Disguising},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30671-30681}
}

raw_affiliation:
[{"affiliation": "The University of Sydney", "city": "Sydney", "country": "Australia"}, {"affiliation": "CSIRO", "city": "Sydney", "country": "Australia"}, {"affiliation": "The University of New South Wales", "city": "Sydney", "country": "Australia"}]


title: Light3R-SfM: Towards Feed-forward Structure-from-Motion
authors: Sven Elflein, Qunjie Zhou, Laura Leal-Taixé
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16774-16784
affiliations: ["NVIDIA", "Vector Institute", "University of Toronto"]
cities: ["Santa Clara", "Toronto", "Toronto"]
countries: ["USA", "Canada", "Canada"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Elflein_Light3R-SfM_Towards_Feed-forward_Structure-from-Motion_CVPR_2025_paper.html

abstract:
We present Light3R-SfM, a feed-forward, end-to-end learnable framework for efficient large-scale Structure-from-Motion (SfM) from unconstrained image collections. Unlike existing SfM solutions that rely on costly matching and global optimization to achieve accurate 3D reconstructions, Light3R-SfM addresses this limitation through a novel latent global alignment module. This module replaces traditional global optimization with a learnable attention mechanism, effectively capturing multi-view constraints across images for robust and precise camera pose estimation. Light3R-SfM constructs a sparse scene graph via retrieval-score-guided shortest path tree to dramatically reduce memory usage and computational overhead compared to the naive approach. Extensive experiments demonstrate that Light3R-SfM achieves competitive accuracy while significantly reducing runtime, making it ideal for 3D reconstruction tasks in real-world applications with a runtime constraint.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Elflein_Light3R-SfM_Towards_Feed-forward_Structure-from-Motion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Elflein_Light3R-SfM_Towards_Feed-forward_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Elflein_2025_CVPR,
    author    = {Elflein, Sven and Zhou, Qunjie and Leal-Taix\'e, Laura},
    title     = {Light3R-SfM: Towards Feed-forward Structure-from-Motion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16774-16784}
}

raw_affiliation:
[{"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "Vector Institute", "city": "Toronto", "country": "Canada"}, {"affiliation": "University of Toronto", "city": "Toronto", "country": "Canada"}]


title: Robotic Visual Instruction
authors: Yanbang Li, Ziyang Gong, Haoyang Li, Xiaoqi Huang, Haolan Kang, Guangping Bai, Xianzheng Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12155-12165
cities: ["N/A", "Shanghai", "San Diego", "N/A", "Guangzhou"]
countries: ["N/A", "China", "USA", "N/A", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Robotic_Visual_Instruction_CVPR_2025_paper.html

abstract:
Recently, natural language has been the primary medium for human-robot interaction. However, its inherent lack of spatial precision for robotic control introduces challenges such as ambiguity and verbosity. To address these limitations, we introduce the ***Robotic Visual Instruction (RoVI)***, a novel paradigm to guide robotic tasks through an object-centric, hand-drawn symbolic representation. RoVI effectively encodes spatial-temporal information into human-interpretable visual instructions through 2D sketches, utilizing arrows, circles, colors, and numbers to direct 3D robotic manipulation. To enable robots to understand RoVI better and generate precise actions based on RoVI, we present ***Visual Instruction Embodied Workflow (VIEW)***, a pipeline formulated for RoVI-conditioned policies. This approach leverages Vision-Language Models (VLMs) to interpret RoVI inputs, decode spatial and temporal constraints from 2D pixel space via keypoint extraction, and then transform them into executable 3D action sequences. We additionally curate a specialized dataset of 15K instances to fine-tune small VLMs for edge deployment, enabling them to effectively learn RoVI capabilities. Our approach is rigorously validated across 11 novel tasks in both real and simulated environments, demonstrating significant generalization capability. Notably, VIEW achieves an 87.5% success rate in real-world scenarios involving unseen tasks that feature multi-step actions, with disturbances, and trajectory-following requirements. Code and Datasets in this paper will be released soon.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Robotic_Visual_Instruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Robotic_Visual_Instruction_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Yanbang and Gong, Ziyang and Li, Haoyang and Huang, Xiaoqi and Kang, Haolan and Bai, Guangping and Ma, Xianzheng},
    title     = {Robotic Visual Instruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12155-12165}
}

raw_affiliation:
[{"affiliation": "Independent Researcher", "city": "N/A", "country": "N/A"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "UCSD", "city": "San Diego", "country": "USA"}, {"affiliation": "VIVO", "city": "N/A", "country": "N/A"}, {"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}]

affiliations:
["Independent Researcher", "Shanghai AI Laboratory", "UCSD", "VIVO", "South China University of Technology"]


title: Solving Instance Detection from an Open-World Perspective
authors: Qianqian Shen, Yunhan Zhao, Nahyun Kwon, Jeeeun Kim, Yanan Li, Shu Kong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9901-9910
cities: ["Hangzhou", "Irvine", "College Station", "Hangzhou", "Macau", "Macau"]
countries: ["China", "USA", "USA", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shen_Solving_Instance_Detection_from_an_Open-World_Perspective_CVPR_2025_paper.html

abstract:
Instance detection (InsDet) aims to localize specific object instances within a novel scene imagery based on given visual references. Technically, it requires proposal detection to identify all possible object instances, followed by instance-level matching to pinpoint the ones of interest. Its open-world nature supports its broad applications from robotics to AR/VR but also presents significant challenges: methods must generalize to unknown testing data distributions because (1) the testing scene imagery is unseen during training, and (2) there are domain gaps between visual references and detected proposals. Existing methods tackle these challenges by synthesizing diverse training examples or utilizing off-the-shelf foundation models (FMs). However, they only partially capitalize the available open-world information. In contrast, we approach InsDet from an Open-World perspective, introducing our method IDOW. We find that, while pretrained FMs yield high recall in instance detection, they are not specifically optimized for instance-level feature matching. Therefore, we adapt pretrained FMs for improved instance-level matching using open-world data. Our approach incorporates metric learning along with novel data augmentations, which sample distractors as negative examples and synthesize novel-view instances to enrich the visual references. Extensive experiments demonstrate that our method significantly outperforms prior works, achieving >10 AP over previous results on two recently released challenging benchmark datasets in both conventional and novel instance detection settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_Solving_Instance_Detection_from_an_Open-World_Perspective_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shen_Solving_Instance_Detection_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shen_2025_CVPR,
    author    = {Shen, Qianqian and Zhao, Yunhan and Kwon, Nahyun and Kim, Jeeeun and Li, Yanan and Kong, Shu},
    title     = {Solving Instance Detection from an Open-World Perspective},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9901-9910}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "UC Irvine", "city": "Irvine", "country": "USA"}, {"affiliation": "Texas A&M University", "city": "College Station", "country": "USA"}, {"affiliation": "Zhejiang Lab", "city": "Hangzhou", "country": "China"}, {"affiliation": "University of Macau", "city": "Macau", "country": "China"}, {"affiliation": "Institute of Collaborative Innovation", "city": "Macau", "country": "China"}]

affiliations:
["Zhejiang University", "UC Irvine", "Texas A&M University", "Zhejiang Lab", "University of Macau", "Institute of Collaborative Innovation"]


title: Percept, Memory, and Imagine: World Feature Simulating for Open-Domain Unknown Object Detection
authors: Aming Wu, Cheng Deng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4682-4691
affiliations: ["School of Electronic Engineering, Xidian University"]
cities: ["Xi'an"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Percept_Memory_and_Imagine_World_Feature_Simulating_for_Open-Domain_Unknown_CVPR_2025_paper.html

abstract:
To accelerate the safe deployment of object detectors, we focus on reducing the impact of both covariate and semantic shifts. And we consider a realistic yet challenging scenario, namely Open-Domain Unknown Object Detection (ODU-OD), which aims to detect unknown objects in unseen target domains without accessing any auxiliary data. Towards ODU-OD, it is feasible to learn a robust discriminative boundary by synthesizing virtual features. Generally, perception, memory, and imagination are three essential capacities for human beings. Through multi-level perception and rich memory about known objects, the characteristics of unknown objects can be imagined sufficiently, enhancing the ability of discriminating known from unknown objects. Inspired by this idea, an approach of World Feature Simulation (WFS) is proposed, mainly consisting of a multi-level perception, memory recorder, and unknown-feature generator. Specifically, after extracting the features of the input, we separately employ a Mamba and Graph Network to obtain the global-level and connective-level representations. Next, a codebook containing multiple learnable codewords is defined to preserve fragmented memory of known objects. Meanwhile, we perform a modulated operation on the memory to form the imagination bank involving unknown characteristics. Finally, to alleviate the impact of lacking supervision data, based on the multi-level representation and imagination bank, a dedicated unknown-feature generator is designed to recurrently synthesize outlier features deviating from in-distribution (ID) objects. The significant performance gains on four different detection tasks demonstrate the superiorities of our method. The code will be released at https://github.com/AmingWu/WFS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Percept_Memory_and_Imagine_World_Feature_Simulating_for_Open-Domain_Unknown_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Percept_Memory_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Aming and Deng, Cheng},
    title     = {Percept, Memory, and Imagine: World Feature Simulating for Open-Domain Unknown Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4682-4691}
}

raw_affiliation:
[{"affiliation": "School of Electronic Engineering, Xidian University", "city": "Xi'an", "country": "China"}]


title: Efficient Depth Estimation for Unstable Stereo Camera Systems on AR Glasses
authors: Yongfan Liu, Hyoukjun Kwon
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6252-6261
raw_affiliation: [{"affiliation": "University of California, Irvine", "city": "Irvine", "country": "USA"}]
affiliations: ["University of California, Irvine"]
cities: ["Irvine"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Efficient_Depth_Estimation_for_Unstable_Stereo_Camera_Systems_on_AR_CVPR_2025_paper.html

abstract:
Stereo depth estimation is a fundamental component in augmented reality (AR), which requires low latency for real-time processing. However, preprocessing such as rectification and non-ML computations such as cost volume require significant amount of latency exceeding that of an ML model itself, which hinders the real-time processing required by AR. Therefore, we develop alternative approaches to the rectification and cost volume that consider ML acceleration (GPU and NPUs) in recent hardware. For pre-processing, we eliminate it by introducing homography matrix prediction network with a rectification positional encoding (RPE), which delivers both low latency and robustness to unrectified images. For cost volume, we replace it with a group-pointwise convolution-based operator and approximation of cosine similarity based on layernorm and dot product. Based on our approaches, we develop MultiHeadDepth (replacing cost volume) and HomoDepth (MultiHeadDepth + removing pre-processing) models. MultiHeadDepth provides 11.8-30.3% improvements in accuracy and 22.9-25.2% reduction in latency compared to a state-of-the-art depth estimation model for AR glasses from industry. HomoDepth, which can directly process unrectified images, reduces the end-to-end latency by 44.5%. We also introduce a multi-task learning method to handle misaligned stereo inputs on HomoDepth, which reduces the AbsRel error by 10.0-24.3%. The overall results demonstrate the efficacy of our approaches, which not only reduce the inference latency but also improve the model performance. Our code is available at https://github.com/UCI-ISA-Lab/MultiHeadDepth-HomoDepth

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Efficient_Depth_Estimation_for_Unstable_Stereo_Camera_Systems_on_AR_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Efficient_Depth_Estimation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yongfan and Kwon, Hyoukjun},
    title     = {Efficient Depth Estimation for Unstable Stereo Camera Systems on AR Glasses},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6252-6261}
}


title: 3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29501-29512
affiliations: ["University of Michigan", "New York University"]
cities: ["Ann Arbor", "New York"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_3D-GRAND_A_Million-Scale_Dataset_for_3D-LLMs_with_Better_Grounding_and_CVPR_2025_paper.html

authors:
Jianing Yang, Xuweiyi Chen, Nikhil Madaan, Madhavan Iyengar, Shengyi Qian, David F. Fouhey, Joyce Chai

abstract:
The integration of language and 3D perception is crucial for embodied agents and robots that comprehend and interact with the physical world. While large language models (LLMs) have demonstrated impressive language understanding and generation capabilities, their adaptation to 3D environments (3D-LLMs) remains in its early stages. A primary challenge is a lack of large-scale datasets with dense grounding between language and 3D scenes. We introduce 3D-GRAND, a pioneering large-scale dataset comprising 40,087 household scenes paired with 6.2 million densely-grounded scene-language instructions. Our results show that instruction tuning with 3D-GRAND significantly enhances grounding capabilities and reduces hallucinations in 3D-LLMs. As part of our contributions, we propose a comprehensive benchmark 3D-POPE to systematically evaluate hallucination in 3D-LLMs, enabling fair comparisons of models. Our experiments highlight a scaling effect between dataset size and 3D-LLM performance, emphasizing the importance of large-scale 3D-text datasets for embodied AI research. Our results demonstrate early signals for effective sim-to-real transfer, indicating that models trained on large synthetic data can perform well on real-world 3D scans. Through 3D-GRAND and 3D-POPE, we aim to equip the embodied AI community with resources and insights to lead to more reliable and better-grounded 3D-LLMs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_3D-GRAND_A_Million-Scale_Dataset_for_3D-LLMs_with_Better_Grounding_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_3D-GRAND_A_Million-Scale_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Jianing and Chen, Xuweiyi and Madaan, Nikhil and Iyengar, Madhavan and Qian, Shengyi and Fouhey, David F. and Chai, Joyce},
    title     = {3D-GRAND: A Million-Scale Dataset for 3D-LLMs with Better Grounding and Less Hallucination},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29501-29512}
}

raw_affiliation:
[{"affiliation": "University of Michigan", "city": "Ann Arbor", "country": "USA"}, {"affiliation": "New York University", "city": "New York", "country": "USA"}]


title: LiDAR-RT: Gaussian-based Ray Tracing for Dynamic LiDAR Re-simulation
authors: Chenxu Zhou, Lvchang Fu, Sida Peng, Yunzhi Yan, Zhanhua Zhang, Yong Chen, Jiazhi Xia, Xiaowei Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1538-1548
affiliations: ["Zhejiang University", "Central South University", "Geely Automobile Research Institute"]
cities: ["Hangzhou", "Changsha", "Hangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_LiDAR-RT_Gaussian-based_Ray_Tracing_for_Dynamic_LiDAR_Re-simulation_CVPR_2025_paper.html

abstract:
This paper targets the challenge of real-time LiDAR re-simulation in dynamic driving scenarios. Recent approaches utilize neural radiance fields combined with the physical modeling of LiDAR sensors to achieve high-fidelity re-simulation results. Unfortunately, these methods face limitations due to high computational demands in large-scale scenes and cannot perform real-time LiDAR rendering. To overcome these constraints, we propose LiDAR-RT, a novel framework that supports real-time, physically accurate LiDAR re-simulation for driving scenes. Our primary contribution is the development of an efficient and effective rendering pipeline, which integrates Gaussian primitives and hardware-accelerated ray tracing technology. Specifically, we model the physical properties of LiDAR sensors using Gaussian primitives with learnable parameters and incorporate scene graphs to handle scene dynamics. Building upon this scene representation, our framework first constructs a bounding volume hierarchy (BVH), then casts rays for each pixel and generates novel LiDAR views through a differentiable rendering algorithm. Importantly, our framework supports realistic rendering with flexible scene editing operations and various sensor configurations. Extensive experiments across multiple public benchmarks demonstrate that our method outperforms state-of-the-art methods in terms of rendering quality and efficiency. Codes will be released to ensure reproducibility.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_LiDAR-RT_Gaussian-based_Ray_Tracing_for_Dynamic_LiDAR_Re-simulation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_LiDAR-RT_Gaussian-based_Ray_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Chenxu and Fu, Lvchang and Peng, Sida and Yan, Yunzhi and Zhang, Zhanhua and Chen, Yong and Xia, Jiazhi and Zhou, Xiaowei},
    title     = {LiDAR-RT: Gaussian-based Ray Tracing for Dynamic LiDAR Re-simulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1538-1548}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Central South University", "city": "Changsha", "country": "China"}, {"affiliation": "Geely Automobile Research Institute", "city": "Hangzhou", "country": "China"}]


title: Generative Zero-Shot Composed Image Retrieval
authors: Lan Wang, Wei Ao, Vishnu Naresh Boddeti, Ser-Nam Lim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29690-29700
affiliations: ["Michigan State University", "University of Central Florida"]
cities: ["East Lansing", "Orlando"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Generative_Zero-Shot_Composed_Image_Retrieval_CVPR_2025_paper.html

abstract:
Composed Image Retrieval (CIR) is a vision-language task utilizing queries comprising images and textual descriptions to achieve precise image retrieval. This task seeks to find images that are visually similar to a reference image while incorporating specific changes or features described textually (visual delta). CIR enables a more flexible and user-specific retrieval by bridging visual data with verbal instructions. This paper introduces a novel generative method that augments Composed Image Retrieval by Composed Image Generation (CIG) to provide pseudo-target images. CIG utilizes a textual inversion network to map reference images into semantic word space, which generates pseudo-target images in combination with textual descriptions. These images serve as additional visual information, significantly improving the accuracy and relevance of retrieved images when integrated into existing retrieval frameworks. Experiments conducted across multiple CIR datasets and several baseline methods demonstrate improvements in retrieval performance, which shows the potential of our approach as an effective add-on for existing composed image retrieval.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Generative_Zero-Shot_Composed_Image_Retrieval_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Generative_Zero-Shot_Composed_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Lan and Ao, Wei and Boddeti, Vishnu Naresh and Lim, Ser-Nam},
    title     = {Generative Zero-Shot Composed Image Retrieval},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29690-29700}
}

raw_affiliation:
[{"affiliation": "Michigan State University", "city": "East Lansing", "country": "USA"}, {"affiliation": "University of Central Florida", "city": "Orlando", "country": "USA"}]


title: Large-Scale Text-to-Image Model with Inpainting is a Zero-Shot Subject-Driven Image Generator
authors: Chaehun Shin, Jooyoung Choi, Heeseung Kim, Sungroh Yoon
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7986-7996
cities: ["Seoul", "Seoul"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shin_Large-Scale_Text-to-Image_Model_with_Inpainting_is_a_Zero-Shot_Subject-Driven_Image_CVPR_2025_paper.html

abstract:
Subject-driven text-to-image generation aims to produce images of a new subject within a desired context by accurately capturing both the visual characteristics of the subject and the semantic content of a text prompt. Traditional methods rely on time- and resource-intensive fine-tuning for subject alignment, while recent zero-shot approaches leverage on-the-fly image prompting, often sacrificing subject alignment. In this paper, we introduce Diptych Prompting, a novel zero-shot approach that reinterprets as an inpainting task with precise subject alignment by leveraging the emergent property of diptych generation in large-scale text-to-image models. Diptych Prompting arranges an incomplete diptych with the reference image in the left panel, and performs text-conditioned inpainting on the right panel. We further prevent unwanted content leakage by removing the background in the reference image and improve fine-grained details in the generated subject by enhancing attention weights between the panels during inpainting. Experimental results confirm that our approach significantly outperforms zero-shot image prompting methods, resulting in images that are visually preferred by users. Additionally, our method supports not only subject-driven generation but also stylized image generation and subject-driven image editing, demonstrating versatility across diverse image generation applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shin_Large-Scale_Text-to-Image_Model_with_Inpainting_is_a_Zero-Shot_Subject-Driven_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shin_Large-Scale_Text-to-Image_Model_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shin_2025_CVPR,
    author    = {Shin, Chaehun and Choi, Jooyoung and Kim, Heeseung and Yoon, Sungroh},
    title     = {Large-Scale Text-to-Image Model with Inpainting is a Zero-Shot Subject-Driven Image Generator},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7986-7996}
}

raw_affiliation:
[{"affiliation": "Data Science and AI Laboratory, ECE, Seoul National University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "AIIS, ASRI, INMC, ISRC, and Interdisciplinary Program in AI, Seoul National University", "city": "Seoul", "country": "South Korea"}]

affiliations:
["Data Science and AI Laboratory, ECE, Seoul National University", "AIIS, ASRI, INMC, ISRC, and Interdisciplinary Program in AI, Seoul National University"]


title: MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors
authors: Riku Murai, Eric Dexheimer, Andrew J. Davison
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16695-16705
raw_affiliation: [{"affiliation": "Imperial College London", "city": "London", "country": "UK"}]
affiliations: ["Imperial College London"]
cities: ["London"]
countries: ["UK"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Murai_MASt3R-SLAM_Real-Time_Dense_SLAM_with_3D_Reconstruction_Priors_CVPR_2025_paper.html

abstract:
We present a real-time monocular dense SLAM system designed bottom-up from MASt3R, a two-view 3D reconstruction and matching prior.  Equipped with this strong prior, our system is robust on in-the-wild video sequences despite making no assumption on a fixed or parametric camera model beyond a unique camera centre.  We introduce efficient methods for pointmap matching, camera tracking and local fusion, graph construction and loop closure, and second-order global optimisation. With known calibration, a simple modification to the system achieves state-of-the-art performance across various benchmarks.  Altogether, we propose a plug-and-play monocular SLAM system capable of producing globally consistent poses and dense geometry while operating at 15 FPS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Murai_MASt3R-SLAM_Real-Time_Dense_SLAM_with_3D_Reconstruction_Priors_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Murai_MASt3R-SLAM_Real-Time_Dense_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Murai_2025_CVPR,
    author    = {Murai, Riku and Dexheimer, Eric and Davison, Andrew J.},
    title     = {MASt3R-SLAM: Real-Time Dense SLAM with 3D Reconstruction Priors},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16695-16705}
}


title: Flow-NeRF: Joint Learning of Geometry, Poses, and Dense Flow within Unified Neural Representations
authors: Xunzhi Zheng, Dan Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 993-1002
affiliations: ["Department of Computer Science and Engineering, HKUST"]
cities: ["Hong Kong"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zheng_Flow-NeRF_Joint_Learning_of_Geometry_Poses_and_Dense_Flow_within_CVPR_2025_paper.html

abstract:
Learning accurate scene reconstruction without pose priors in neural radiance fields is challenging due to inherent geometric ambiguity. Recent development either relies on correspondence priors for regularization or uses off-the-shelf flow estimators to derive analytical poses. However, the potential for jointly learning scene geometry, camera poses, and dense flow within a unified neural representation remains largely unexplored. In this paper, we present Flow-NeRF, a unified framework that simultaneously optimizes scene geometry, camera poses, and dense optical flow all on-the-fly. To enable the learning of dense flow within the neural radiance field, we design and build a bijective mapping for flow estimation, conditioned on pose. To make the scene reconstruction benefit from the flow estimation, we develop an effective feature enhancement mechanism to pass canonical space features to world space representations, significantly enhancing scene geometry. We validate our model across four important tasks, i.e., novel view synthesis, depth estimation, camera pose prediction, and dense optical flow estimation, using several datasets. Our approach surpasses previous methods in almost all metrics for novel-view view synthesis and depth estimation and yields both qualitatively sound and quantitatively accurate novel-view flow. Our project page is https://zhengxunzhi.github.io/flownerf/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zheng_Flow-NeRF_Joint_Learning_of_Geometry_Poses_and_Dense_Flow_within_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zheng_Flow-NeRF_Joint_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zheng_2025_CVPR,
    author    = {Zheng, Xunzhi and Xu, Dan},
    title     = {Flow-NeRF: Joint Learning of Geometry, Poses, and Dense Flow within Unified Neural Representations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {993-1002}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science and Engineering, HKUST", "city": "Hong Kong", "country": "China"}]


authors: Mi Luo, Zihui Xue, Alex Dimakis, Kristen Grauman
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15802-15812
affiliations: ["The University of Texas at Austin", "UC Berkeley", "Bespoke Labs"]
cities: ["Austin", "Berkeley", "Unknown"]
countries: ["USA", "USA", "Unknown"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Luo_Viewpoint_Rosetta_Stone_Unlocking_Unpaired_Ego-Exo_Videos_for_View-invariant_Representation_CVPR_2025_paper.html

title:
Viewpoint Rosetta Stone: Unlocking Unpaired Ego-Exo Videos for View-invariant Representation Learning

abstract:
Egocentric and exocentric perspectives of human action differ significantly, yet overcoming this extreme viewpoint gap is critical for applications in augmented reality and robotics. We propose ViewpointRosetta, an approach that unlocks large-scale unpaired ego and exo video data to learn clip-level viewpoint-invariant video representations. Our framework introduces (1) a diffusion-based Rosetta Stone Translator (RST), which, leveraging a moderate amount of synchronized multi-view videos, serves as a translator in feature space to decipher the alignments between unpaired ego and exo data, and (2) a dual encoder that aligns unpaired data representations through contrastive learning with RST-based synthetic feature augmentation and soft alignment. To evaluate the learned features in a standardized setting, we construct a new cross-view benchmark using Ego-Exo4D, covering cross-view retrieval, action recognition, and skill assessment. Our framework demonstrates superior cross-view understanding compared to previous view-invariant learning and egocentric video representation learning approaches, and opens the door to bringing vast amounts of traditional third-person video to bear on the more nascent first-person setting.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Luo_Viewpoint_Rosetta_Stone_Unlocking_Unpaired_Ego-Exo_Videos_for_View-invariant_Representation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Luo_Viewpoint_Rosetta_Stone_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Luo_2025_CVPR,
    author    = {Luo, Mi and Xue, Zihui and Dimakis, Alex and Grauman, Kristen},
    title     = {Viewpoint Rosetta Stone: Unlocking Unpaired Ego-Exo Videos for View-invariant Representation Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15802-15812}
}

raw_affiliation:
[{"affiliation": "The University of Texas at Austin", "city": "Austin", "country": "USA"}, {"affiliation": "UC Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "Bespoke Labs", "city": "Unknown", "country": "Unknown"}]


title: Cross-modal Information Flow in Multimodal Large Language Models
authors: Zhi Zhang, Srishti Yadav, Fengze Han, Ekaterina Shutova
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19781-19791
cities: ["Amsterdam", "Copenhagen", "Munich"]
countries: ["Netherlands", "Denmark", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Cross-modal_Information_Flow_in_Multimodal_Large_Language_Models_CVPR_2025_paper.html

abstract:
The recent advancements in auto-regressive multimodal large language models (MLLMs) have demonstrated promising progress for vision-language tasks. While there exists a variety of studies investigating the processing of linguistic information within large language models, little is currently known about the inner working mechanism of MLLMs and how linguistic and visual information interact within these models. In this study, we aim to fill this gap by examining the information flow between different modalities---language and vision---in MLLMs, focusing on visual question answering. Specifically, given an image-question pair as input, we investigate where in the model and how the visual and linguistic information are combined to generate the final prediction. Conducting experiments with a series of models from the LLaVA series, we find that there are two distinct stages in the process of integration of the two modalities. In the lower layers, the model first transfers the more general visual features of the whole image into the representations of (linguistic) question tokens. In the middle layers, it once again transfers visual information about specific objects relevant to the question to the respective token positions of the question. Finally, in the higher layers, the resulting multimodal representation is propagated to the last position of the input sequence for the final prediction. Overall, our findings provide a new and comprehensive perspective on the spatial and functional aspects of image and language processing in the MLLMs, thereby facilitating future research into multimodal information localization and editing. Our code and collected dataset are released here: https://github.com/FightingFighting/cross-modal-information-flow-in-MLLM.git.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Cross-modal_Information_Flow_in_Multimodal_Large_Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Cross-modal_Information_Flow_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Zhi and Yadav, Srishti and Han, Fengze and Shutova, Ekaterina},
    title     = {Cross-modal Information Flow in Multimodal Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19781-19791}
}

raw_affiliation:
[{"affiliation": "ILLC, University of Amsterdam, Netherlands", "city": "Amsterdam", "country": "Netherlands"}, {"affiliation": "Dept. of Computer Science, University of Copenhagen, Denmark", "city": "Copenhagen", "country": "Denmark"}, {"affiliation": "Dept. of Computer Engineering, Technical University of Munich, Germany", "city": "Munich", "country": "Germany"}]

affiliations:
["ILLC, University of Amsterdam, Netherlands", "Dept. of Computer Science, University of Copenhagen, Denmark", "Dept. of Computer Engineering, Technical University of Munich, Germany"]


title: Consistent and Controllable Image Animation with Motion Diffusion Models
authors: Xin Ma, Yaohui Wang, Gengyun Jia, Xinyuan Chen, Tien-Tsin Wong, Yuan-Fang Li, Cunjian Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7288-7298
cities: ["Melbourne", "Shanghai", "Nanjing"]
countries: ["Australia", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ma_Consistent_and_Controllable_Image_Animation_with_Motion_Diffusion_Models_CVPR_2025_paper.html

abstract:
Diffusion models have achieved significant progress in the task of image animation due to their powerful generative capabilities. However, preserving appearance consistency to the static input image, and avoiding abrupt motion change in the generated animation, remains challenging. In this paper, we introduce Cinemo, a novel image animation approach that aims to achieve better appearance consistency and motion smoothness. The core of Cinemo is to focus on learning the distribution of motion residuals, rather than directly predicting frames as in existing diffusion models. During the inference, we further mitigate the sudden motion changes in the generated video by introducing a novel DCT-based noise refinement strategy. To counteract the over-smoothing of motion, we introduce a dynamics degree control design for better control of the magnitude of motion. Altogether, these strategies enable Cinemo to produce highly consistent, smooth, and motion-controllable results. Extensive experiments compared with several state-of-the-art methods demonstrate the effectiveness and superiority of our proposed approach. In the end, we also demonstrate how our model can be applied for motion transfer or video editing of any given video.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_Consistent_and_Controllable_Image_Animation_with_Motion_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ma_Consistent_and_Controllable_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ma_2025_CVPR,
    author    = {Ma, Xin and Wang, Yaohui and Jia, Gengyun and Chen, Xinyuan and Wong, Tien-Tsin and Li, Yuan-Fang and Chen, Cunjian},
    title     = {Consistent and Controllable Image Animation with Motion Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7288-7298}
}

raw_affiliation:
[{"affiliation": "Department of Data Science & AI, Faculty of Information Technology, Monash University", "city": "Melbourne", "country": "Australia"}, {"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Nanjing University of Posts and Telecommunications", "city": "Nanjing", "country": "China"}]

affiliations:
["Department of Data Science & AI, Faculty of Information Technology, Monash University", "Shanghai Artificial Intelligence Laboratory", "Nanjing University of Posts and Telecommunications"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23604-23614
cities: ["Hangzhou", "Singapore", "Hong Kong", "Beijing"]
countries: ["China", "Singapore", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hu_Towards_Better_Alignment_Training_Diffusion_Models_with_Reinforcement_Learning_Against_CVPR_2025_paper.html

title:
Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse Rewards

authors:
Zijing Hu, Fengda Zhang, Long Chen, Kun Kuang, Jiahui Li, Kaifeng Gao, Jun Xiao, Xin Wang, Wenwu Zhu

abstract:
Diffusion models have achieved remarkable success in text-to-image generation. However, their practical applications are hindered by the misalignment between generated images and corresponding text prompts. To tackle this issue, reinforcement learning (RL) has been considered for diffusion model fine-tuning. Yet, RL's effectiveness is limited by the challenge of sparse reward, where feedback is only available at the end of the generation process. This makes it difficult to identify which actions during the denoising process contribute positively to the final generated image, potentially leading to ineffective or unnecessary denoising policies. To this end, this paper presents a novel RL-based framework that addresses the sparse reward problem when training diffusion models. Our framework, named \text B ^2\text -DiffuRL , employs two strategies: **B**ackward progressive training and **B**ranch-based sampling. For one thing, backward progressive training focuses initially on the final timesteps of the denoising process and gradually extends the training interval to earlier timesteps, easing the learning difficulty associated with sparse rewards. For another, we perform branch-based sampling for each training interval. By comparing the samples within the same branch, we can identify how much the policies of the current training interval contribute to the final image, which helps to learn effective policies instead of unnecessary ones. \text B ^2\text -DiffuRL  is compatible with existing optimization algorithms. Extensive experiments demonstrate the effectiveness of \text B ^2\text -DiffuRL  in improving prompt-image alignment and maintaining diversity in generated images. The code for this work is available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hu_Towards_Better_Alignment_Training_Diffusion_Models_with_Reinforcement_Learning_Against_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hu_Towards_Better_Alignment_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hu_2025_CVPR,
    author    = {Hu, Zijing and Zhang, Fengda and Chen, Long and Kuang, Kun and Li, Jiahui and Gao, Kaifeng and Xiao, Jun and Wang, Xin and Zhu, Wenwu},
    title     = {Towards Better Alignment: Training Diffusion Models with Reinforcement Learning Against Sparse Rewards},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23604-23614}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["Zhejiang University", "Nanyang Technological University", "The Hong Kong University of Science and Technology", "Tsinghua University"]


title: Spatial457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large Mutimodal Models
authors: Xingrui Wang, Wufei Ma, Tiezheng Zhang, Celso M de Melo, Jieneng Chen, Alan Yuille
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24669-24679
affiliations: ["Johns Hopkins University", "DEVCOM Army Research Laboratory"]
cities: ["Baltimore", "Adelphi"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Spatial457_A_Diagnostic_Benchmark_for_6D_Spatial_Reasoning_of_Large_CVPR_2025_paper.html

abstract:
Although large multimodal models (LMMs) have demonstrated remarkable capabilities in visual scene interpretation and reasoning, their capacity for complex and precise 3-dimensional spatial reasoning remains uncertain. Existing benchmarks focus predominantly on 2D spatial understanding and lack a framework to comprehensively evaluate 6D spatial reasoning across varying complexities.To address this limitation, we present Spatial457, a scalable and unbiased synthetic dataset designed with 4 key spatial components: multi-object recognition, 2D and 3D spatial relationships, and 3D orientation. Spatial457 supports a cascading evaluation structure, offering 7 question types across 5 difficulty levels that progress from basic single-object recognition to our newly proposed complex 6D spatial reasoning tasks.We evaluated various large multimodal models (LMMs) on Spatial457, observing a general decline in performance as task complexity increases, particularly in 3D reasoning and 6D spatial tasks. To quantify these challenges, we introduce the Relative Performance Dropping Rate (RPDR), highlighting key weaknesses in 3D reasoning capabilities. Leveraging the unbiased attribute design of our dataset, we also uncover prediction biases across different attributes, with similar patterns observed in real-world image settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Spatial457_A_Diagnostic_Benchmark_for_6D_Spatial_Reasoning_of_Large_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Spatial457_A_Diagnostic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Xingrui and Ma, Wufei and Zhang, Tiezheng and de Melo, Celso M and Chen, Jieneng and Yuille, Alan},
    title     = {Spatial457: A Diagnostic Benchmark for 6D Spatial Reasoning of Large Mutimodal Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24669-24679}
}

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


title: Omnidirectional Multi-Object Tracking
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21959-21969
affiliations: ["Hunan University", "Zhejiang University"]
cities: ["Changsha", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Luo_Omnidirectional_Multi-Object_Tracking_CVPR_2025_paper.html

authors:
Kai Luo, Hao Shi, Sheng Wu, Fei Teng, Mengfei Duan, Chang Huang, Yuhang Wang, Kaiwei Wang, Kailun Yang

abstract:
Panoramic imagery, with its 360deg field of view, offers comprehensive information to support Multi-Object Tracking (MOT) in capturing spatial and temporal relationships of surrounding objects. However, most MOT algorithms are tailored for pinhole images with limited views, impairing their effectiveness in panoramic settings. Additionally, panoramic image distortions, such as resolution loss, geometric deformation, and uneven lighting, hinder direct adaptation of existing MOT methods, leading to significant performance degradation. To address these challenges, we propose OmniTrack, an omnidirectional MOT framework that incorporates Tracklet Management to introduce temporal cues, FlexiTrack Instances for object localization and association, and the CircularStatE Module to alleviate image and geometric distortions. This integration enables tracking in panoramic field-of-view scenarios, even under rapid sensor motion. To mitigate the lack of panoramic MOT datasets, we introduce the QuadTrack dataset--a comprehensive panoramic dataset collected by a quadruped robot, featuring diverse challenges such as panoramic fields of view, intense motion, and complex environments. Extensive experiments on the public JRDB dataset and the newly introduced QuadTrack benchmark demonstrate the state-of-the-art performance of the proposed framework. OmniTrack achieves a HOTA score of 26.92% on JRDB, representing an improvement of 3.43%, and further achieves 23.45% on QuadTrack, surpassing the baseline by 6.81%. The established dataset and source code are available at https://github.com/xifen523/OmniTrack.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Luo_Omnidirectional_Multi-Object_Tracking_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Luo_Omnidirectional_Multi-Object_Tracking_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Luo_2025_CVPR,
    author    = {Luo, Kai and Shi, Hao and Wu, Sheng and Teng, Fei and Duan, Mengfei and Huang, Chang and Wang, Yuhang and Wang, Kaiwei and Yang, Kailun},
    title     = {Omnidirectional Multi-Object Tracking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21959-21969}
}

raw_affiliation:
[{"affiliation": "Hunan University", "city": "Changsha", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]


title: Potential Field Based Deep Metric Learning
authors: Shubhang Bhatnagar, Narendra Ahuja
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25549-25559
raw_affiliation: [{"affiliation": "University of Illinois Urbana-Champaign", "city": "Urbana", "country": "USA"}]
affiliations: ["University of Illinois Urbana-Champaign"]
cities: ["Urbana"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bhatnagar_Potential_Field_Based_Deep_Metric_Learning_CVPR_2025_paper.html

abstract:
Deep metric learning (DML) involves training a network to learn a semantically meaningful representation space. Many current approaches mine n-tuples of examples and model interactions within each tuplets. We present a novel, compositional DML model that instead of in tuples, represents the influence of each example (embedding) by a continuous potential field, and superposes the fields to obtain their combined global potential field. We use attractive/repulsive potential fields to represent interactions among embeddings from images of the same/different classes. Contrary to typical learning methods, where mutual influence of samples is proportional to their distance, we enforce reduction in such influence with distance, leading to a decaying field. We show that such decay helps improve performance on real world datasets with large intra-class variations and label noise. Like other proxy-based methods, we also use proxies to succinctly represent sub-populations of examples. We evaluate our method on three standard DML benchmarks- Cars-196, CUB-200-2011, and SOP datasets where it outperforms state-of-the-art baselines.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bhatnagar_Potential_Field_Based_Deep_Metric_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bhatnagar_Potential_Field_Based_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Bhatnagar_2025_CVPR,
    author    = {Bhatnagar, Shubhang and Ahuja, Narendra},
    title     = {Potential Field Based Deep Metric Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25549-25559}
}


title: Enhancing Vision-Language Compositional Understanding with Multimodal Synthetic Data
authors: Haoxin Li, Boyang Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24849-24861
raw_affiliation: [{"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]
affiliations: ["Nanyang Technological University"]
cities: ["Singapore"]
countries: ["Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Enhancing_Vision-Language_Compositional_Understanding_with_Multimodal_Synthetic_Data_CVPR_2025_paper.html

abstract:
Paired image-text data with subtle variations in-between (e.g., people holding surfboards vs. people holding shovels) hold the promise of producing Vision-Language Models with proper compositional understanding. Synthesizing such training data from generative models is a highly coveted prize due to the reduced cost of data collection. However, synthesizing training images for compositional learning presents three challenges: (1) efficiency in generating large quantities of images, (2) text alignment between the generated image and the caption in the exact place of the subtle change, and (3) image fidelity in ensuring sufficient similarity with the original real images in all other places. We propose SPARCL (Synthetic Perturbations for Advancing Robust Compositional Learning), which integrates image feature injection into a fast text-to-image generative model, followed by an image style transfer step, to meet the three challenges. Further, to cope with any residual issues of text alignment, we propose an adaptive margin loss to filter out potentially incorrect synthetic samples and focus the learning on informative hard samples. Evaluation on four compositional understanding benchmarks demonstrates that SPARCL significantly improves the compositionality of CLIP, boosting the average accuracy of the CLIP base model by over 8% across all benchmarks and outperforming state-of-the-art methods by 2% on three benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Enhancing_Vision-Language_Compositional_Understanding_with_Multimodal_Synthetic_Data_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Enhancing_Vision-Language_Compositional_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Haoxin and Li, Boyang},
    title     = {Enhancing Vision-Language Compositional Understanding with Multimodal Synthetic Data},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24849-24861}
}


title: Directional Label Diffusion Model for Learning from Noisy Labels
authors: Senyu Hou, Gaoxia Jiang, Jia Zhang, Shangrong Yang, Husheng Guo, Yaqing Guo, Wenjian Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25738-25748
cities: ["Taiyuan", "Taiyuan"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hou_Directional_Label_Diffusion_Model_for_Learning_from_Noisy_Labels_CVPR_2025_paper.html

abstract:
In image classification, the label quality of training data critically influences model generalization, especially for deep neural networks (DNNs). Traditionally, learning from noisy labels (LNL) can improve the generalization of DNNs through complex architectures or a series of robust techniques, but its performance improvement is limited by the discriminative paradigm. Unlike traditional ways, we resolve the LNL problems from the perspective of robust label generation, based on diffusion models within the generative paradigm. To expand the diffusion model into a robust classifier that explicitly accommodates more noise knowledge, we propose a Directional Label Diffusion (DLD) model. It disentangles the diffusion process into two paths, i.e., directional diffusion and random diffusion. Specifically, directional diffusion simulates the corruption of true labels into a directed noise distribution, prioritizing the removal of likely noise, whereas random diffusion introduces inherent randomness to support label recovery. This architecture enable DLD to gradually infer labels from an initial random state, interpretably diverging from the specified noise distribution. To adapt the model to diverse noisy environments, we design a low-cost label pre-correction method that automatically supplies more accurate label information to the diffusion model, without requiring manual intervention or additional iterations. Our approach outperforms state-of-the-art methods on both simulated and real-world noisy datasets. Code is available at https://github.com/SenyuHou/DLD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hou_Directional_Label_Diffusion_Model_for_Learning_from_Noisy_Labels_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hou_Directional_Label_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hou_2025_CVPR,
    author    = {Hou, Senyu and Jiang, Gaoxia and Zhang, Jia and Yang, Shangrong and Guo, Husheng and Guo, Yaqing and Wang, Wenjian},
    title     = {Directional Label Diffusion Model for Learning from Noisy Labels},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25738-25748}
}

raw_affiliation:
[{"affiliation": "School of Computer and Information Technology, Shanxi University, China", "city": "Taiyuan", "country": "China"}, {"affiliation": "Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education, Shanxi University, China", "city": "Taiyuan", "country": "China"}]

affiliations:
["School of Computer and Information Technology, Shanxi University, China", "Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education, Shanxi University, China"]


title: AA-CLIP: Enhancing Zero-Shot Anomaly Detection via Anomaly-Aware CLIP
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4744-4754
cities: ["Hefei", "Suzhou", "Beijing", "Hefei", "Stanford"]
countries: ["China", "China", "China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ma_AA-CLIP_Enhancing_Zero-Shot_Anomaly_Detection_via_Anomaly-Aware_CLIP_CVPR_2025_paper.html

authors:
Wenxin Ma, Xu Zhang, Qingsong Yao, Fenghe Tang, Chenxu Wu, Yingtai Li, Rui Yan, Zihang Jiang, S.Kevin Zhou

abstract:
Anomaly detection (AD) identifies outliers for applications like defect and lesion detection. While CLIP shows promise for zero-shot AD tasks due to its strong generalization capabilities, its inherent Anomaly-Unawareness leads to limited discrimination between normal and abnormal features. To address this problem, we propose Anomaly-Aware CLIP (AA-CLIP), which enhances CLIP's anomaly discrimination ability in both text and visual spaces while preserving its generalization capability. AA-CLIP is achieved through a straightforward yet effective two-stage approach: it first creates anomaly-aware text anchors to differentiate normal and abnormal semantics clearly, then aligns patch-level visual features with these anchors for precise anomaly localization. This two-stage strategy, with the help of residual adapters, gradually adapts CLIP in a controlled manner, achieving effective AD while maintaining CLIP's class knowledge. Extensive experiments validate AA-CLIP as a resource-efficient solution for zero-shot AD tasks, achieving state-of-the-art results in industrial and medical applications. The code is available at https://github.com/Mwxinnn/AA-CLIP.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_AA-CLIP_Enhancing_Zero-Shot_Anomaly_Detection_via_Anomaly-Aware_CLIP_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ma_AA-CLIP_Enhancing_Zero-Shot_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ma_2025_CVPR,
    author    = {Ma, Wenxin and Zhang, Xu and Yao, Qingsong and Tang, Fenghe and Wu, Chenxu and Li, Yingtai and Yan, Rui and Jiang, Zihang and Zhou, S.Kevin},
    title     = {AA-CLIP: Enhancing Zero-Shot Anomaly Detection via Anomaly-Aware CLIP},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4744-4754}
}

raw_affiliation:
[{"affiliation": "School of Biomedical Engineering, Division of Life Sciences and Medicine, USTC", "city": "Hefei", "country": "China"}, {"affiliation": "MIRACLE Center, Suzhou Institute for Advance Research, USTC", "city": "Suzhou", "country": "China"}, {"affiliation": "Key Laboratory of Intelligent Information Processing of CAS, ICT, CAS", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Laboratory of Precision and Intelligent Chemistry, USTC", "city": "Hefei", "country": "China"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}]

affiliations:
["School of Biomedical Engineering, Division of Life Sciences and Medicine, USTC", "MIRACLE Center, Suzhou Institute for Advance Research, USTC", "Key Laboratory of Intelligent Information Processing of CAS, ICT, CAS", "State Key Laboratory of Precision and Intelligent Chemistry, USTC", "Stanford University"]


title: HybridGS: Decoupling Transients and Statics with 2D and 3D Gaussian Splatting
authors: Jingyu Lin, Jiaqi Gu, Lubin Fan, Bojian Wu, Yujing Lou, Renjie Chen, Ligang Liu, Jieping Ye
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 788-797
cities: ["Hefei", "Unknown", "Shanghai"]
countries: ["China", "Unknown", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_HybridGS_Decoupling_Transients_and_Statics_with_2D_and_3D_Gaussian_CVPR_2025_paper.html

abstract:
Generating high-quality novel view renderings of 3D Gaussian Splatting (3DGS) in scenes featuring transient objects is challenging. We propose a novel hybrid representation, termed as HybridGS, using 2D Gaussians for transient objects per image and maintaining traditional 3D Gaussians for the whole static scenes. 3DGS is suited for modeling static scenes that assume multi-view consistency, but the transient objects appear occasionally and do not adhere to the assumption, thus we model them as planar objects from a single view by 2D Gaussians. Our novel representation decomposes the scene from the perspective of fundamental viewpoint consistency. Additionally, we present a multi-view supervision method for 3DGS that leverages information from co-visible regions, further enhancing the distinctions between the transients and statics. Then, we propose a straightforward yet effective multi-stage training strategy to ensure robust training and view synthesis. Experiments on benchmarks show our state-of-the-art performance of novel view synthesis in indoor and outdoor scenes, even in the presence of distracting elements. Project page: https://gujiaqivadin.github.io/hybridgs/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_HybridGS_Decoupling_Transients_and_Statics_with_2D_and_3D_Gaussian_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_HybridGS_Decoupling_Transients_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Jingyu and Gu, Jiaqi and Fan, Lubin and Wu, Bojian and Lou, Yujing and Chen, Renjie and Liu, Ligang and Ye, Jieping},
    title     = {HybridGS: Decoupling Transients and Statics with 2D and 3D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {788-797}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Independent Researcher", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]

affiliations:
["University of Science and Technology of China", "Independent Researcher", "Shanghai Jiao Tong University"]


title: Keyframe-Guided Creative Video Inpainting
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13009-13020
countries: ["China", "China", "USA", "China", "China", "USA", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_Keyframe-Guided_Creative_Video_Inpainting_CVPR_2025_paper.html

authors:
Yuwei Guo, Ceyuan Yang, Anyi Rao, Chenlin Meng, Omer Bar-Tal, Shuangrui Ding, Maneesh Agrawala, Dahua Lin, Bo Dai

abstract:
Video inpainting, which aims to fill missing regions with visually coherent content, has emerged as a crucial technique for creative applications such as editing. While existing approaches achieve visual consistency or text-guided generation, they often struggle to balance coherence and creative diversity. In this work, we introduce VideoRepainter, a two-stage framework that allows users to inpaint a keyframe using established image-level techniques, then propagate the changes to other frames. Our approach can leverage state-of-the-art image models for keyframe manipulation, thereby easing the burden of the video-inpainting process. To this end, we integrate an image-to-video model with a symmetric condition mechanism to address ambiguity caused by direct mask downsampling. We further explore efficient strategies for mask synthesis and parameter tuning to reduce costs in data processing and model training. Evaluations demonstrate our method achieves superior results in both visual fidelity and content diversity compared to existing approaches, providing a practical solution for creative video manipulation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_Keyframe-Guided_Creative_Video_Inpainting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_Keyframe-Guided_Creative_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Yuwei and Yang, Ceyuan and Rao, Anyi and Meng, Chenlin and Bar-Tal, Omer and Ding, Shuangrui and Agrawala, Maneesh and Lin, Dahua and Dai, Bo},
    title     = {Keyframe-Guided Creative Video Inpainting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13009-13020}
}

raw_affiliation:
[{"affiliation": "CUHK", "city": "Hong Kong", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Pika Labs", "city": "Mountain View", "country": "USA"}, {"affiliation": "ByteDance", "city": "Beijing", "country": "China"}, {"affiliation": "CPII under InnoHK", "city": "Hong Kong", "country": "China"}, {"affiliation": "Stanford", "city": "Stanford", "country": "USA"}, {"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}, {"affiliation": "HKU", "city": "Hong Kong", "country": "China"}, {"affiliation": "Feeling AI", "city": "Hong Kong", "country": "China"}]

affiliations:
["CUHK", "Shanghai AI Laboratory", "Pika Labs", "ByteDance", "CPII under InnoHK", "Stanford", "HKUST", "HKU", "Feeling AI"]

cities:
["Hong Kong", "Shanghai", "Mountain View", "Beijing", "Hong Kong", "Stanford", "Hong Kong", "Hong Kong", "Hong Kong"]


title: Channel Consistency Prior and Self-Reconstruction Strategy Based Unsupervised Image Deraining
authors: Guanglu Dong, Tianheng Zheng, Yuanzhouhan Cao, Linbo Qing, Chao Ren
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7469-7479
affiliations: ["Sichuan University", "Beijing Jiaotong University"]
cities: ["Chengdu", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dong_Channel_Consistency_Prior_and_Self-Reconstruction_Strategy_Based_Unsupervised_Image_Deraining_CVPR_2025_paper.html

abstract:
Recently, deep image deraining models based on paired datasets have made a series of remarkable progress. However, they cannot be well applied in real-world applications due to the difficulty of obtaining real paired datasets and the poor generalization performance. In this paper, we propose a novel Channel Consistency Prior and Self-Reconstruction Strategy Based Unsupervised Image Deraining framework, CSUD, to tackle the aforementioned challenges. During training with unpaired data, CSUD is capable of generating high-quality pseudo clean and rainy image pairs which are used to enhance the performance of deraining network. Specifically, to preserve more image background details while transferring rain streaks from rainy images to the unpaired clean images, we propose a novel Channel Consistency Loss (CCLoss) by introducing the Channel Consistency Prior (CCP) of rain streaks into training process, thereby ensuring that the generated pseudo rainy images closely resemble the real ones. Furthermore, we propose a novel Self-Reconstruction (SR) strategy to alleviate the redundant information transfer problem of the generator, further improving the deraining performance and the generalization capability of our method. Extensive experiments on multiple synthetic and real-world datasets demonstrate that the deraining performance of CSUD surpasses other state-of-the-art unsupervised methods and CSUD exhibits superior generalization capability. Code is available at https://github.com/GuangluDong0728/CSUD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dong_Channel_Consistency_Prior_and_Self-Reconstruction_Strategy_Based_Unsupervised_Image_Deraining_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dong_Channel_Consistency_Prior_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dong_2025_CVPR,
    author    = {Dong, Guanglu and Zheng, Tianheng and Cao, Yuanzhouhan and Qing, Linbo and Ren, Chao},
    title     = {Channel Consistency Prior and Self-Reconstruction Strategy Based Unsupervised Image Deraining},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7469-7479}
}

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


title: MobileMamba: Lightweight Multi-Receptive Visual Mamba Network
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4497-4507
affiliations: ["Zhejiang University", "Youtu Lab, Tencent", "Huazhong University of Science and Technology"]
cities: ["Hangzhou", "Shenzhen", "Wuhan"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/He_MobileMamba_Lightweight_Multi-Receptive_Visual_Mamba_Network_CVPR_2025_paper.html

authors:
Haoyang He, Jiangning Zhang, Yuxuan Cai, Hongxu Chen, Xiaobin Hu, Zhenye Gan, Yabiao Wang, Chengjie Wang, Yunsheng Wu, Lei Xie

abstract:
Previous research on lightweight models has primarily focused on CNNs and Transformer-based designs. CNNs, with their local receptive fields, struggle to capture long-range dependencies, while Transformers, despite their global modeling capabilities, are limited by quadratic computational complexity in high-resolution scenarios. Recently, state-space models have gained popularity in the visual domain due to their linear computational complexity. Despite their low FLOPs, current lightweight Mamba-based models exhibit suboptimal throughput. In this work, we propose the MobileMamba framework, which balances efficiency and performance. We design a three-stage network to enhance inference speed significantly. At a fine-grained level, we introduce the Multi-Receptive Field Feature Interaction (MRFFI) module, comprising the Long-Range Wavelet Transform-Enhanced Mamba (WTE-Mamba), Efficient Multi-Kernel Depthwise Deconvolution (MK-DeConv), and Eliminate Redundant Identity components. This module integrates multi-receptive field information and enhances high-frequency detail extraction. Additionally, we employ training and testing strategies to further improve performance and efficiency. MobileMamba achieves up to 83.6% on Top-1, surpassing existing state-of-the-art methods which is maximum x21| faster than LocalVim on GPU. Extensive experiments on high-resolution downstream tasks demonstrate that MobileMamba surpasses current efficient models, achieving an optimal balance between speed and accuracy.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/He_MobileMamba_Lightweight_Multi-Receptive_Visual_Mamba_Network_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/He_MobileMamba_Lightweight_Multi-Receptive_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{He_2025_CVPR,
    author    = {He, Haoyang and Zhang, Jiangning and Cai, Yuxuan and Chen, Hongxu and Hu, Xiaobin and Gan, Zhenye and Wang, Yabiao and Wang, Chengjie and Wu, Yunsheng and Xie, Lei},
    title     = {MobileMamba: Lightweight Multi-Receptive Visual Mamba Network},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4497-4507}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Youtu Lab, Tencent", "city": "Shenzhen", "country": "China"}, {"affiliation": "Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}]


title: EdgeTAM: On-Device Track Anything Model
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13832-13842
affiliations: ["Meta AI", "Nanyang Technological University", "The University of Hong Kong", "Feeling AI"]
cities: ["Menlo Park", "Singapore", "Hong Kong", "Hong Kong"]
countries: ["USA", "Singapore", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_EdgeTAM_On-Device_Track_Anything_Model_CVPR_2025_paper.html

authors:
Chong Zhou, Chenchen Zhu, Yunyang Xiong, Saksham Suri, Fanyi Xiao, Lemeng Wu, Raghuraman Krishnamoorthi, Bo Dai, Chen Change Loy, Vikas Chandra, Bilge Soran

abstract:
On top of Segment Anything Model (SAM), SAM 2 further extends its capability from image to video inputs through a memory bank mechanism and obtains a remarkable performance compared with previous methods, making it a foundation model for video segmentation task. In this paper, we aim at making SAM 2 much more efficient so that it even runs on mobile devices while maintaining a comparable performance. Despite several works optimizing SAM for better efficiency, we find they are not sufficient for SAM 2 because they all focus on compressing the image encoder, while our benchmark shows that the newly introduced memory attention blocks are also the latency bottleneck. Given this observation, we propose EdgeTAM, which leverages a novel 2D Spatial Perceiver to reduce the computational cost. In particular, the proposed 2D Spatial Perceiver encodes the densely stored frame-level memories with a lightweight Transformer that contains a fixed set of learnable queries. Given that video segmentation is a dense prediction task, we find preserving the spatial structure of the memories is essential so that the queries are split into global-level and patch-level groups. We also propose a distillation pipeline that further improves the performance without inference overhead. As a result, EdgeTAM achieves 87.7, 70.0, 72.3, and 71.7 J&F on DAVIS 2017, MOSE, SA-V val, and SA-V test, while running at 16 FPS on iPhone 15 Pro Max. The code and models are available at https://github.com/facebookresearch/EdgeTAM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_EdgeTAM_On-Device_Track_Anything_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_EdgeTAM_On-Device_Track_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Chong and Zhu, Chenchen and Xiong, Yunyang and Suri, Saksham and Xiao, Fanyi and Wu, Lemeng and Krishnamoorthi, Raghuraman and Dai, Bo and Loy, Chen Change and Chandra, Vikas and Soran, Bilge},
    title     = {EdgeTAM: On-Device Track Anything Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13832-13842}
}

raw_affiliation:
[{"affiliation": "Meta AI", "city": "Menlo Park", "country": "USA"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Feeling AI", "city": "Hong Kong", "country": "China"}]


title: SimLTD: Simple Supervised and Semi-Supervised Long-Tailed Object Detection
authors: Phi Vu Tran
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4672-4681
raw_affiliation: [{"affiliation": "LexisNexis Risk Solutions", "city": "New York", "country": "USA"}]
affiliations: ["LexisNexis Risk Solutions"]
cities: ["New York"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tran_SimLTD_Simple_Supervised_and_Semi-Supervised_Long-Tailed_Object_Detection_CVPR_2025_paper.html

abstract:
While modern visual recognition systems have made significant advancements, many continue to struggle with the open problem of learning from few exemplars. This paper focuses on the task of object detection in the setting where object classes follow a natural long-tailed distribution. Existing methods for long-tailed detection resort to external ImageNet labels to augment the low-shot training instances. However, such dependency on a large labeled database has limited utility in practical scenarios. We propose a versatile and scalable approach to leverage optional unlabeled images, which are easy to collect without the burden of human annotations. Our SimLTD framework is straightforward and intuitive, and consists of three simple steps: (1) pre-training on abundant head classes; (2) transfer learning on scarce tail classes; and (3) fine-tuning on a sampled set of both head and tail classes. Our approach can be viewed as an improved head-to-tail model transfer paradigm without the added complexities of meta-learning or knowledge distillation, as was required in past research. By harnessing supplementary unlabeled images, without extra image labels, SimLTD establishes new record results on the challenging LVIS v1 benchmark across both supervised and semi-supervised settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tran_SimLTD_Simple_Supervised_and_Semi-Supervised_Long-Tailed_Object_Detection_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Tran_2025_CVPR,
    author    = {Tran, Phi Vu},
    title     = {SimLTD: Simple Supervised and Semi-Supervised Long-Tailed Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4672-4681}
}


title: EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14303-14313
cities: ["Unknown", "Abu Dhabi", "Canberra", "Linkoping"]
countries: ["Unknown", "UAE", "Australia", "Sweden"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Soni_EarthDial_Turning_Multi-sensory_Earth_Observations_to_Interactive_Dialogues_CVPR_2025_paper.html

authors:
Sagar Soni, Akshay Dudhane, Hiyam Debary, Mustansar Fiaz, Muhammad Akhtar Munir, Muhammad Sohail Danish, Paolo Fraccaro, Campbell D Watson, Levente J Klein, Fahad Shahbaz Khan, Salman Khan

abstract:
Automated analysis of vast Earth observation data via interactive Vision-Language Models (VLMs) can unlock new opportunities for environmental monitoring, disaster response, and resource management. Existing generic VLMs do not perform well on Remote Sensing data, while the recent Geo-spatial VLMs remain restricted to a fixed resolution and few sensor modalities. In this paper, we introduce EarthDial, a conversational assistant specifically designed for Earth Observation (EO) data, transforming complex, multi-sensory Earth observations into interactive, natural language dialogues. EarthDial supports multispectral, multi-temporal, and multi-resolution imagery, enabling a wide range of remote sensing tasks, including classification, detection, captioning, question answering, visual reasoning, and visual grounding. To achieve this, we introduce an extensive instruction tuning dataset comprising over 11.11M instruction pairs covering RGB, Synthetic Aperture Radar (SAR), and multispectral modalities such as Near-Infrared (NIR) and infrared. Furthermore, EarthDial handles bi-temporal and multi-temporal sequence analysis for applications like change detection. Our extensive experimental results on 44 downstream datasets demonstrate that EarthDial outperforms existing generic and domain specific models, achieving better generalization across various EO tasks. Our source codes and pre-trained models are at https://github.com/hiyamdebary/EarthDial.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Soni_EarthDial_Turning_Multi-sensory_Earth_Observations_to_Interactive_Dialogues_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Soni_EarthDial_Turning_Multi-sensory_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Soni_2025_CVPR,
    author    = {Soni, Sagar and Dudhane, Akshay and Debary, Hiyam and Fiaz, Mustansar and Munir, Muhammad Akhtar and Danish, Muhammad Sohail and Fraccaro, Paolo and Watson, Campbell D and Klein, Levente J and Khan, Fahad Shahbaz and Khan, Salman},
    title     = {EarthDial: Turning Multi-sensory Earth Observations to Interactive Dialogues},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14303-14313}
}

raw_affiliation:
[{"affiliation": "IBM Research", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Mohamed bin Zayed University of AI", "city": "Abu Dhabi", "country": "UAE"}, {"affiliation": "Australian National University", "city": "Canberra", "country": "Australia"}, {"affiliation": "Link\u00a8oping University", "city": "Linkoping", "country": "Sweden"}]

affiliations:
["IBM Research", "Mohamed bin Zayed University of AI", "Australian National University", "Link\u00a8oping University"]


title: Learning Endogenous Attention for Incremental Object Detection
authors: Xiang Song, Yuhang He, Jingyuan Li, Qiang Wang, Yihong Gong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30354-30364
cities: ["Xi'an", "Xi'an", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Song_Learning_Endogenous_Attention_for_Incremental_Object_Detection_CVPR_2025_paper.html

abstract:
In this paper, we focus on a challenging Incremental Object Detection (IOD) problem. Existing IOD methods follow an image-to-annotation alignment paradigm, which attempts to complete the annotations for old categories and subsequently learns both new and old categories in new tasks. This paradigm inherently introduces missing/redundant/inaccurate annotations of old categories, resulting in a suboptimal performance. Instead, we propose a novel annotation-to-instance alignment IOD paradigm and develop a corresponding method named Learning Endogenous Attention (LEA). Inspired by the human brain, LEA enables the model to focus on annotated task-specific objects, while ignoring irrelevant ones, thus solving the annotation incomplete problem in IOD. Concretely, our LEA consists of Endogenous Attention Modules (EAMs) and an Energy-based Task Modulator (ETM). During training, we add the dedicated EAMs for each new task and train them to focus on the new categories. During testing, ETM predicts task IDs using energy functions, directing the model to detect task-specific objects. The detection results corresponding to all task IDs are combined as the final output, thereby alleviating the catastrophic forgetting of old knowledge. Extensive experiments on COCO 2017 and Pascal VOC 2007 demonstrate the effectiveness of our method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Song_Learning_Endogenous_Attention_for_Incremental_Object_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Song_Learning_Endogenous_Attention_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Song_2025_CVPR,
    author    = {Song, Xiang and He, Yuhang and Li, Jingyuan and Wang, Qiang and Gong, Yihong},
    title     = {Learning Endogenous Attention for Incremental Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30354-30364}
}

raw_affiliation:
[{"affiliation": "School of Software Engineering, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "College of Artificial Intelligence, Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "Shenzhen University of Advanced Technology", "city": "Shenzhen", "country": "China"}]

affiliations:
["School of Software Engineering, Xi\u2019an Jiaotong University", "College of Artificial Intelligence, Xi\u2019an Jiaotong University", "Shenzhen University of Advanced Technology"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26822-26833
affiliations: ["SenseTime Research", "State Key Lab of CAD&CG, Zhejiang University", "Tetras.AI"]
cities: ["Hong Kong", "Hangzhou", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhai_StarGen_A_Spatiotemporal_Autoregression_Framework_with_Video_Diffusion_Model_for_CVPR_2025_paper.html

title:
StarGen: A Spatiotemporal Autoregression Framework with Video Diffusion Model for Scalable and Controllable Scene Generation

authors:
Shangjin Zhai, Zhichao Ye, Jialin Liu, Weijian Xie, Jiaqi Hu, Zhen Peng, Hua Xue, Danpeng Chen, Xiaomeng Wang, Lei Yang, Nan Wang, Haomin Liu, Guofeng Zhang

abstract:
Recent advances in large reconstruction and generative models have significantly improved scene reconstruction and novel view generation. However, due to compute limitations, each inference with these large models is confined to a small area, making long-range consistent scene generation challenging. To address this, we propose StarGen, a novel framework that employs a pre-trained video diffusion model in an autoregressive manner for long-range scene generation. The generation of each video clip is conditioned on the 3D warping of spatially adjacent images and the temporally overlapping image from previously generated clips, improving spatiotemporal consistency in long-range scene generation with precise pose control. The spatiotemporal condition is compatible with various input conditions, facilitating diverse tasks, including sparse view interpolation, perpetual view generation, and layout-conditioned city generation. Quantitative and qualitative evaluations demonstrate StarGen's superior scalability, fidelity, and pose accuracy compared to state-of-the-art methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhai_StarGen_A_Spatiotemporal_Autoregression_Framework_with_Video_Diffusion_Model_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhai_StarGen_A_Spatiotemporal_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhai_2025_CVPR,
    author    = {Zhai, Shangjin and Ye, Zhichao and Liu, Jialin and Xie, Weijian and Hu, Jiaqi and Peng, Zhen and Xue, Hua and Chen, Danpeng and Wang, Xiaomeng and Yang, Lei and Wang, Nan and Liu, Haomin and Zhang, Guofeng},
    title     = {StarGen: A Spatiotemporal Autoregression Framework with Video Diffusion Model for Scalable and Controllable Scene Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26822-26833}
}

raw_affiliation:
[{"affiliation": "SenseTime Research", "city": "Hong Kong", "country": "China"}, {"affiliation": "State Key Lab of CAD&CG, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Tetras.AI", "city": "Beijing", "country": "China"}]


title: HyperSeg: Hybrid Segmentation Assistant with Fine-grained Visual Perceiver
authors: Cong Wei, Yujie Zhong, Haoxian Tan, Yong Liu, Jie Hu, Dengjie Li, Zheng Zhao, Yujiu Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8931-8941
affiliations: ["Tsinghua Shenzhen International Graduate School, Tsinghua University", "Meituan Inc."]
cities: ["Shenzhen", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wei_HyperSeg_Hybrid_Segmentation_Assistant_with_Fine-grained_Visual_Perceiver_CVPR_2025_paper.html

abstract:
This paper aims to address universal segmentation for image and video perception with the strong reasoning ability empowered by Visual Large Language Models (VLLMs). Despite significant progress in current unified segmentation methods, limitations in adaptation to both image and video scenarios, as well as the complex reasoning segmentation, make it difficult for them to handle various challenging instructions and achieve an accurate understanding of fine-grained vision-language correlations. We propose HyperSeg, the first VLLM-based universal segmentation model for pixel-level image and video perception, encompassing generic segmentation tasks and more complex reasoning perception tasks requiring powerful reasoning abilities and world knowledge. Besides, to fully leverage the recognition capabilities of VLLMs and the fine-grained visual information, HyperSeg incorporates hybrid entity recognition and fine-grained visual perceiver modules for various segmentation tasks. Combined with the temporal adapter, HyperSeg achieves a comprehensive understanding of temporal information. Experimental results validate the effectiveness of our insights in resolving universal image and video segmentation tasks, including the more complex reasoning perception tasks. Our code is available at https://github.com/congvvc/HyperSeg.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wei_HyperSeg_Hybrid_Segmentation_Assistant_with_Fine-grained_Visual_Perceiver_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wei_HyperSeg_Hybrid_Segmentation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wei_2025_CVPR,
    author    = {Wei, Cong and Zhong, Yujie and Tan, Haoxian and Liu, Yong and Hu, Jie and Li, Dengjie and Zhao, Zheng and Yang, Yujiu},
    title     = {HyperSeg: Hybrid Segmentation Assistant with Fine-grained Visual Perceiver},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8931-8941}
}

raw_affiliation:
[{"affiliation": "Tsinghua Shenzhen International Graduate School, Tsinghua University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Meituan Inc.", "city": "Beijing", "country": "China"}]


title: Diffusion-based Event Generation for High-Quality Image Deblurring
authors: Xinan Xie, Qing Zhang, Wei-Shi Zheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2194-2203
cities: ["Guangzhou", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_Diffusion-based_Event_Generation_for_High-Quality_Image_Deblurring_CVPR_2025_paper.html

abstract:
While event-based deblurring have demonstrated impressive results, they are impractical for consumer photos captured by cell phones and digital cameras that are not equipped with the event sensor. To address this problem, we in this paper propose a novel deblurring framework called Event Generation Deblurring (EGDeblurring), which allows to effectively deblur an image by generating event guidance describing the motion information using a diffusion model. Specifically, we design a motion prior generation diffusion model and a feature extractor to produce prior information beneficial for deblurring, rather than generating the raw event representation. In order to achieve effective fusion of motion prior information with blurry images and produce high-quality results, we develop a regression deblurring network embedded with a dual-attention channel fusion block. Experiments on multiple datasets demonstrate that our method outperforms state-of-the-art image deblurring methods. Our code is available at https://github.com/XinanXie/EGDeblurring  https://github.com/XinanXie/EGDeblurring.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_Diffusion-based_Event_Generation_for_High-Quality_Image_Deblurring_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_Diffusion-based_Event_Generation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Xinan and Zhang, Qing and Zheng, Wei-Shi},
    title     = {Diffusion-based Event Generation for High-Quality Image Deblurring},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2194-2203}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Sun Yat-sen University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education", "city": "Beijing", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, Sun Yat-sen University", "Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education"]


title: Video Summarization with Large Language Models
authors: Min Jung Lee, Dayoung Gong, Minsu Cho
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18981-18991
affiliations: ["Pohang University of Science and Technology (POSTECH)", "GenGenAI"]
cities: ["Pohang", "Unknown"]
countries: ["South Korea", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Video_Summarization_with_Large_Language_Models_CVPR_2025_paper.html

abstract:
The exponential increase in video content poses significant challenges in terms of efficient navigation, search, and retrieval, thus requiring advanced video summarization techniques. Existing video summarization methods, which heavily rely on visual features and temporal dynamics, often fail to capture the semantics of video content, resulting in incomplete or incoherent summaries. To tackle the challenge, we propose a new video summarization framework that leverages the capabilities of recent Large Language Models (LLMs), expecting that the knowledge learned from massive data enables LLMs to evaluate video frames in a manner that better aligns with diverse semantics and human judgments, effectively addressing the inherent subjectivity in defining keyframes. Our method, dubbed  LLM-based Video Summarization (LLMVS), translates video frames into a sequence of captions using a Muti-modal Large Language Model (M-LLM) and then assesses the importance of each frame using an LLM, based on the captions in its local context. These local importance scores are refined through a global attention mechanism in the entire context of video captions, ensuring that our summaries effectively reflect both the details and the overarching narrative. Our experimental results demonstrate the superiority of the proposed method over existing ones in standard benchmarks, highlighting the potential of LLMs in the processing of multimedia content.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_Video_Summarization_with_Large_Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_Video_Summarization_with_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Min Jung and Gong, Dayoung and Cho, Minsu},
    title     = {Video Summarization with Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18981-18991}
}

raw_affiliation:
[{"affiliation": "Pohang University of Science and Technology (POSTECH)", "city": "Pohang", "country": "South Korea"}, {"affiliation": "GenGenAI", "city": "Unknown", "country": "Unknown"}]


authors: Mohd Hozaifa Khan, Ravi Kiran Sarvadevabhatla
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18176-18186
affiliations: ["Centre for Visual Information Technology (CVIT), IIIT Hyderabad, India"]
cities: ["Hyderabad"]
countries: ["India"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Khan_Sketchtopia_A_Dataset_and_Foundational_Agents_for_Benchmarking_Asynchronous_Multimodal_CVPR_2025_paper.html

title:
Sketchtopia: A Dataset and Foundational Agents for Benchmarking Asynchronous Multimodal Communication with Iconic Feedback

abstract:
We introduce Sketchtopia, a large-scale dataset and AI framework designed to explore goal-driven, multimodal communication through asynchronous interactions in a Pictionary-inspired setup. Sketchtopia captures natural human interactions, including freehand sketches, open-ended guesses, and iconic feedback gestures, showcasing the complex dynamics of cooperative communication under constraints. It features over 20K gameplay sessions from 916 players, capturing 263K sketches, 10K erases, 56K guesses and 19.4K iconic feedbacks. We introduce multimodal foundational agents with capabilities for generative sketching, guess generation and asynchronous communication. Our dataset also includes 800 human-agent sessions for benchmarking the agents. We introduce novel metrics to characterize collaborative success, responsiveness to feedback and inter-agent asynchronous communication. Sketchtopia pushes the boundaries of multimodal AI, establishing a new benchmark for studying asynchronous, goal-oriented interactions between humans and AI agents. The dataset can be found at https://sketchtopia25.github.io/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Khan_Sketchtopia_A_Dataset_and_Foundational_Agents_for_Benchmarking_Asynchronous_Multimodal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Khan_Sketchtopia_A_Dataset_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Khan_2025_CVPR,
    author    = {Khan, Mohd Hozaifa and Sarvadevabhatla, Ravi Kiran},
    title     = {Sketchtopia: A Dataset and Foundational Agents for Benchmarking Asynchronous Multimodal Communication with Iconic Feedback},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18176-18186}
}

raw_affiliation:
[{"affiliation": "Centre for Visual Information Technology (CVIT), IIIT Hyderabad, India", "city": "Hyderabad", "country": "India"}]


title: Consistency-aware Self-Training for Iterative-based Stereo Matching
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16641-16650
affiliations: ["Fudan University", "Xiaomi Inc", "Shanghai AI Laboratory", "The Chinese University of Hong Kong"]
cities: ["Shanghai", "Beijing", "Shanghai", "Hong Kong"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Consistency-aware_Self-Training_for_Iterative-based_Stereo_Matching_CVPR_2025_paper.html

authors:
Jingyi Zhou, Peng Ye, Haoyu Zhang, Jiakang Yuan, Rao Qiang, Liu YangChenXu, Wu Cailin, Feng Xu, Tao Chen

abstract:
Iterative-based methods have become mainstream in stereo matching due to their high performance. However, these methods heavily rely on labeled data and face challenges with unlabeled real-world data. To this end, we propose a consistency-aware self-training framework for iterative-based stereo matching for the first time, leveraging real-world unlabeled data in a teacher-student manner. We first observe that regions with larger errors tend to exhibit more pronounced oscillation characteristics during model prediction.Based on this, we introduce a novel consistency-aware soft filtering module to evaluate the reliability of teacher-predicted pseudo-labels, which consists of a multi-resolution prediction consistency filter and an iterative prediction consistency filter to assess the prediction fluctuations of multiple resolutions and iterative optimization respectively. Further, we introduce a consistency-aware soft-weighted loss to adjust the weight of pseudo-labels accordingly, relieving the error accumulation and performance degradation problem due to incorrect pseudo-labels. Extensive experiments demonstrate that our method can improve the performance of various iterative-based stereo matching approaches in various scenarios. In particular, our method can achieve further enhancements over the current SOTA methods on several benchmark datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Consistency-aware_Self-Training_for_Iterative-based_Stereo_Matching_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Jingyi and Ye, Peng and Zhang, Haoyu and Yuan, Jiakang and Qiang, Rao and YangChenXu, Liu and Cailin, Wu and Xu, Feng and Chen, Tao},
    title     = {Consistency-aware Self-Training for Iterative-based Stereo Matching},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16641-16650}
}

raw_affiliation:
[{"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Xiaomi Inc", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}]


title: MV-MATH: Evaluating Multimodal Math Reasoning in Multi-Visual Contexts
authors: Peijie Wang, Zhong-Zhi Li, Fei Yin, Dekang Ran, Cheng-Lin Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19541-19551
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_MV-MATH_Evaluating_Multimodal_Math_Reasoning_in_Multi-Visual_Contexts_CVPR_2025_paper.html

abstract:
Multimodal Large Language Models (MLLMs) have shown promising capabilities in mathematical reasoning within visual contexts across various datasets. However, most existing multimodal math benchmarks are limited to single-visual contexts, which diverges from the multi-visual scenarios commonly encountered in real-world mathematical applications. To address this gap, we introduce MV-MATH: a meticulously curated dataset of 2,009 high-quality mathematical problems. Each problem integrates multiple images interleaved with text, derived from authentic K-12 scenarios and enriched with detailed annotations. MV-MATH includes multiple-choice, free-form, and multi-step questions, covering 11 subject areas across 3 difficulty levels, and serves as a comprehensive and rigorous benchmark for assessing MLLMs' mathematical reasoning in multi-visual contexts. Through extensive experimentation, we observe that MLLMs encounter substantial challenges in multi-visual math tasks, with a considerable performance gap relative to human capabilities on MV-MATH. Furthermore, we analyze the performance and error patterns of various models, providing insights into MLLMs' mathematical reasoning capabilities within multi-visual settings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_MV-MATH_Evaluating_Multimodal_Math_Reasoning_in_Multi-Visual_Contexts_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_MV-MATH_Evaluating_Multimodal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Peijie and Li, Zhong-Zhi and Yin, Fei and Ran, Dekang and Liu, Cheng-Lin},
    title     = {MV-MATH: Evaluating Multimodal Math Reasoning in Multi-Visual Contexts},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19541-19551}
}

raw_affiliation:
[{"affiliation": "MAIS, Institute of Automation of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Arti\ufb01cial Intelligence, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["MAIS, Institute of Automation of Chinese Academy of Sciences", "School of Arti\ufb01cial Intelligence, University of Chinese Academy of Sciences"]


title: Balanced Rate-Distortion Optimization in Learned Image Compression
authors: Yichi Zhang, Zhihao Duan, Yuning Huang, Fengqing Zhu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2428-2438
affiliations: ["Elmore Family School of Electrical and Computer Engineering, Purdue University"]
cities: ["West Lafayette"]
countries: ["U.S.A."]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Balanced_Rate-Distortion_Optimization_in_Learned_Image_Compression_CVPR_2025_paper.html

abstract:
Learned image compression (LIC) using deep learning architectures has seen significant advancements, yet standard rate-distortion (R-D) optimization often encounters imbalanced updates due to diverse gradients of the rate and distortion objectives. This imbalance can lead to suboptimal optimization, where one objective dominates, thereby reducing overall compression efficiency. To address this challenge, we reformulate R-D optimization as a multi-objective optimization (MOO) problem and introduce two balanced R-D optimization strategies that adaptively adjust gradient updates to achieve more equitable improvements in both rate and distortion. The first proposed strategy utilizes a coarse-to-fine gradient descent approach along standard R-D optimization trajectories, making it particularly suitable for training LIC models from scratch. The second proposed strategy analytically addresses the reformulated optimization as a quadratic programming problem with an equality constraint, which is ideal for fine-tuning existing models. Experimental results demonstrate that both proposed methods enhance the R-D performance of LIC models, achieving around a 2% BD-Rate reduction with acceptable additional training cost, leading to a more balanced and efficient optimization process. Code will be available at https://gitlab.com/viper-purdue/Balanced-RD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Balanced_Rate-Distortion_Optimization_in_Learned_Image_Compression_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Yichi and Duan, Zhihao and Huang, Yuning and Zhu, Fengqing},
    title     = {Balanced Rate-Distortion Optimization in Learned Image Compression},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2428-2438}
}

raw_affiliation:
[{"affiliation": "Elmore Family School of Electrical and Computer Engineering, Purdue University", "city": "West Lafayette", "country": "U.S.A."}]


title: Bridge the Gap: From Weak to Full Supervision for Temporal Action Localization with PseudoFormer
authors: Ziyi Liu, Yangcen Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8711-8720
affiliations: ["University of Science and Technology Beijing", "Georgia Institute of Technology"]
cities: ["Beijing", "Atlanta"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Bridge_the_Gap_From_Weak_to_Full_Supervision_for_Temporal_CVPR_2025_paper.html

abstract:
Weakly-supervised Temporal Action Localization (WTAL) has achieved notable success but still suffers from a lack of temporal annotations, leading to a performance and framework gap compared with fully-supervised methods. While recent approaches employ pseudo labels for training, three key challenges: generating high-quality pseudo labels, making full use of different priors, and optimizing training methods with noisy labels remain unresolved. Due to these perspectives, we propose PseudoFormer, a novel two-branch framework that bridges the gap between weakly and fully-supervised Temporal Action Localization (TAL). We first introduce RickerFusion, which maps all predicted action proposals to a global shared space to generate pseudo labels with better quality. Subsequently, we leverage both snippet-level and proposal-level labels with different priors from the weak branch to train the regression-based model in the full branch. Finally, the uncertainty mask and iterative refinement mechanism are applied for training with noisy pseudo labels. PseudoFormer achieves state-of-the-art WTAL results on the two commonly used benchmarks, THUMOS14 and ActivityNet1.3. Besides, extensive ablation studies demonstrate the contribution of each component of our method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Bridge_the_Gap_From_Weak_to_Full_Supervision_for_Temporal_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Bridge_the_Gap_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Ziyi and Liu, Yangcen},
    title     = {Bridge the Gap: From Weak to Full Supervision for Temporal Action Localization with PseudoFormer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8711-8720}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology Beijing", "city": "Beijing", "country": "China"}, {"affiliation": "Georgia Institute of Technology", "city": "Atlanta", "country": "USA"}]


title: HomoGen: Enhanced Video Inpainting via Homography Propagation and Diffusion
authors: Ding Ding, Yueming Pan, Ruoyu Feng, Qi Dai, Kai Qiu, Jianmin Bao, Chong Luo, Zhenzhong Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22953-22962
cities: ["Wuhan", "Xi'an", "Hefei", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ding_HomoGen_Enhanced_Video_Inpainting_via_Homography_Propagation_and_Diffusion_CVPR_2025_paper.html

abstract:
In this paper, we present HomoGen, an enhanced video inpainting method based on homography propagation and diffusion models. HomoGen leverages homography registration to propagate contextual pixels as priors for generating missing content in corrupted videos. Unlike previous flow-based propagation methods, which introduce local distortions due to point-to-point optical flows, homography-induced artifacts are typically global structural distortions that preserve semantic integrity. To effectively utilize these priors for generation, we employ a video diffusion model that inherently prioritizes semantic information within the priors over pixel-level details. A content-adaptive control mechanism is proposed to scale and inject the priors into intermediate video latents during iterative denoising. In contrast to existing transformer-based networks that often suffer from artifacts within priors, leading to error accumulation and unrealistic results, our denoising diffusion network can smooth out artifacts and ensure natural outputs. Extensive experiments demonstrate the effectiveness of the proposed method qualitatively and quantitatively.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ding_HomoGen_Enhanced_Video_Inpainting_via_Homography_Propagation_and_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ding_HomoGen_Enhanced_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ding_2025_CVPR,
    author    = {Ding, Ding and Pan, Yueming and Feng, Ruoyu and Dai, Qi and Qiu, Kai and Bao, Jianmin and Luo, Chong and Chen, Zhenzhong},
    title     = {HomoGen: Enhanced Video Inpainting via Homography Propagation and Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22953-22962}
}

raw_affiliation:
[{"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Microsoft Research Asia", "city": "Beijing", "country": "China"}]

affiliations:
["Wuhan University", "Xi\u2019an Jiaotong University", "University of Science and Technology of China", "Microsoft Research Asia"]


title: Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model
authors: Zhaochong An, Guolei Sun, Yun Liu, Runjia Li, Junlin Han, Ender Konukoglu, Serge Belongie
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16997-17007
cities: ["Copenhagen", "Zurich", "Tianjin", "Oxford"]
countries: ["Denmark", "Switzerland", "China", "United Kingdom"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/An_Generalized_Few-shot_3D_Point_Cloud_Segmentation_with_Vision-Language_Model_CVPR_2025_paper.html

abstract:
Generalized few-shot 3D point cloud segmentation (GFS-PCS) adapts models to new classes with few support samples while retaining base class segmentation. Existing GFS-PCS methods enhance prototypes via interacting with support or query features but remain limited by sparse knowledge from few-shot samples. Meanwhile, 3D vision-language models (3D VLMs), generalizing across open-world novel classes, contain rich but noisy novel class knowledge. In this work, we introduce a GFS-PCS framework that synergizes dense but noisy pseudo-labels from 3D VLMs with precise yet sparse few-shot samples to maximize the strengths of both, named GFS-VL. Specifically, we present a prototype-guided pseudo-label selection to filter low-quality regions, followed by an adaptive infilling strategy that combines knowledge from pseudo-label contexts and few-shot samples to adaptively label the filtered, unlabeled areas. Additionally, we design a novel-base mix strategy to embed few-shot samples into training scenes, preserving essential context for improved novel class learning. Moreover, recognizing the limited diversity in current GFS-PCS benchmarks, we introduce two challenging benchmarks with diverse novel classes for comprehensive generalization evaluation. Experiments validate the effectiveness of our framework across models and datasets. Our approach and benchmarks provide a solid foundation for advancing GFS-PCS in the real world. The code is at here.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/An_Generalized_Few-shot_3D_Point_Cloud_Segmentation_with_Vision-Language_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/An_Generalized_Few-shot_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{An_2025_CVPR,
    author    = {An, Zhaochong and Sun, Guolei and Liu, Yun and Li, Runjia and Han, Junlin and Konukoglu, Ender and Belongie, Serge},
    title     = {Generalized Few-shot 3D Point Cloud Segmentation with Vision-Language Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16997-17007}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science, University of Copenhagen", "city": "Copenhagen", "country": "Denmark"}, {"affiliation": "Computer Vision Laboratory, ETH Zurich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "College of Computer Science, Nankai University", "city": "Tianjin", "country": "China"}, {"affiliation": "Department of Engineering Science, University of Oxford", "city": "Oxford", "country": "United Kingdom"}]

affiliations:
["Department of Computer Science, University of Copenhagen", "Computer Vision Laboratory, ETH Zurich", "College of Computer Science, Nankai University", "Department of Engineering Science, University of Oxford"]


title: Do ImageNet-trained Models Learn Shortcuts? The Impact of Frequency Shortcuts on Generalization
authors: Shunxin Wang, Raymond Veldhuis, Nicola Strisciuglio
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25198-25207
raw_affiliation: [{"affiliation": "University of Twente", "city": "Enschede", "country": "Netherlands"}]
affiliations: ["University of Twente"]
cities: ["Enschede"]
countries: ["Netherlands"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Do_ImageNet-trained_Models_Learn_Shortcuts_The_Impact_of_Frequency_Shortcuts_CVPR_2025_paper.html

abstract:
Frequency shortcuts refer to specific frequency patterns that models heavily rely on for correct classification. Previous studies have shown that models trained on small image datasets often exploit such shortcuts, potentially impairing their generalization performance. However,  existing methods for identifying frequency shortcuts require  expensive computations and become impractical for analyzing models trained on large datasets.  In this work, we propose the first approach to more efficiently analyze frequency shortcuts at a large scale.  We show that both CNN and transformer models learn frequency shortcuts on ImageNet. We also expose that frequency shortcut solutions can yield good performance on out-of-distribution (OOD) test sets which largely retain texture information. However, these shortcuts, mostly aligned with texture patterns, hinder model generalization on rendition-based OOD test sets. These observations suggest that current OOD evaluations often overlook the impact of frequency shortcuts on model generalization. Future benchmarks could thus benefit from explicitly assessing and accounting for these shortcuts to build models that generalize across a broader range of OOD scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Do_ImageNet-trained_Models_Learn_Shortcuts_The_Impact_of_Frequency_Shortcuts_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Do_ImageNet-trained_Models_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Shunxin and Veldhuis, Raymond and Strisciuglio, Nicola},
    title     = {Do ImageNet-trained Models Learn Shortcuts? The Impact of Frequency Shortcuts on Generalization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25198-25207}
}


title: HORP: Human-Object Relation Priors Guided HOI Detection
authors: Pei Geng, Jian Yang, Shanshan Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25325-25335
cities: ["Nanjing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Geng_HORP_Human-Object_Relation_Priors_Guided_HOI_Detection_CVPR_2025_paper.html

abstract:
Human-Object Interaction (HOI) detection aims to predict the <Human, Interaction, Object> triplets, where the core challenge lies in recognizing the interaction of each human-object pair. Despite recent progress thanks to more advanced model architectures, HOI performance remains unsatisfactory. In this work, we first perform some failure analysis and find that the accuracy of the no-interaction category is extremely low, largely hindering the improvement of overall performance. We further look into the error types and find the mis-classification between no-interaction and with-interaction ones can be handled by human-object relation priors. Specifically, to better distinguish no-interaction from direct interactions, we propose 3D location prior, which indicates the distance between human and object; as of no-interaction vs. indirect interactions, we propose gaze area prior, which denotes whether human can see the object or not. The above two types of human-object relation priors are represented by text and are combined with the original visual features, generating multi-modal cues for interaction recognition.Experimental results on the HICO-DET and V-COCO datasets demonstrate that our proposed human-object relation priors are effective and our method HORP surpasses previous methods under various settings and scenarios. In particular, the usage of our priors significantly enhances the model's recognition ability for the no-interaction category.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Geng_HORP_Human-Object_Relation_Priors_Guided_HOI_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Geng_HORP_Human-Object_Relation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Geng_2025_CVPR,
    author    = {Geng, Pei and Yang, Jian and Zhang, Shanshan},
    title     = {HORP: Human-Object Relation Priors Guided HOI Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25325-25335}
}

raw_affiliation:
[{"affiliation": "PCA Lab, Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, and Jiangsu Key Lab of Image and Video Understanding for Social Security, School of Computer Science and Engineering, Nanjing University of Science and Technology", "city": "Nanjing", "country": "China"}]

affiliations:
["PCA Lab, Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education, and Jiangsu Key Lab of Image and Video Understanding for Social Security, School of Computer Science and Engineering, Nanjing University of Science and Technology"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24169-24179
affiliations: ["Meta, GenAI", "University of Wisconsin-Madison", "University of Illinois Urbaba-Champaign"]
cities: ["Menlo Park", "Madison", "Champaign"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Building_a_Mind_Palace_Structuring_Environment-Grounded_Semantic_Graphs_for_Effective_CVPR_2025_paper.html

title:
Building a Mind Palace: Structuring Environment-Grounded Semantic Graphs for Effective Long Video Analysis with LLMs

authors:
Zeyi Huang, Yuyang Ji, Xiaofang Wang, Nikhil Mehta, Tong Xiao, Donghyun Lee, Sigmund Vanvalkenburgh, Shengxin Zha, Bolin Lai, Licheng Yu, Ning Zhang, Yong Jae Lee, Miao Liu

abstract:
Long-form video understanding with Large Vision Language Models is challenged by the need to analyze temporally dispersed yet spatially concentrated key moments within limited context windows. In this work, we introduce VideoMindPalace, a new framework inspired by the "Mind Palace", which organizes critical video moments into a topologically structured semantic graph. VideoMindPalace organizes key information through (i) hand-object tracking and interaction, (ii) clustered activity zones representing specific areas of recurring activities, and (iii) environment layout mapping, allowing natural language parsing by LLMs to provide grounded insights on spatio-temporal and 3D context. In addition, we propose the Video MindPalace  Benchmark (VMB), to assess human-like reasoning, including spatial localization, temporal reasoning, and layout-aware sequential understanding. Evaluated on VMB and established video QA datasets, including EgoSchema, NExT-QA, IntentQA, and the Active Memories Benchmark, VideoMindPalace demonstrates notable gains in spatio-temporal coherence and human-aligned reasoning, advancing long-form video analysis capabilities in VLMs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Building_a_Mind_Palace_Structuring_Environment-Grounded_Semantic_Graphs_for_Effective_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_Building_a_Mind_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Zeyi and Ji, Yuyang and Wang, Xiaofang and Mehta, Nikhil and Xiao, Tong and Lee, Donghyun and Vanvalkenburgh, Sigmund and Zha, Shengxin and Lai, Bolin and Yu, Licheng and Zhang, Ning and Lee, Yong Jae and Liu, Miao},
    title     = {Building a Mind Palace: Structuring Environment-Grounded Semantic Graphs for Effective Long Video Analysis with LLMs},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24169-24179}
}

raw_affiliation:
[{"affiliation": "Meta, GenAI", "city": "Menlo Park", "country": "USA"}, {"affiliation": "University of Wisconsin-Madison", "city": "Madison", "country": "USA"}, {"affiliation": "University of Illinois Urbaba-Champaign", "city": "Champaign", "country": "USA"}]


