# papers

title: Leveraging Temporal Cues for Semi-Supervised Multi-View 3D Object Detection
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27401-27412
affiliations: ["Carnegie Mellon University", "DENSO Corporation", "DENSO International America, Inc."]
cities: ["Pittsburgh", "Kariya", "Southfield"]
countries: ["USA", "Japan", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Park_Leveraging_Temporal_Cues_for_Semi-Supervised_Multi-View_3D_Object_Detection_CVPR_2025_paper.html

authors:
Jinhyung Park, Navyata Sanghvi, Hiroki Adachi, Yoshihisa Shibata, Shawn Hunt, Shinya Tanaka, Hironobu Fujiyoshi, Kris Kitani

abstract:
While recent advancements in camera-based 3D object detection demonstrate remarkable performance, they require thousands or even millions of human-annotated frames. This requirement significantly inhibits their deployment in various locations and sensor configurations. To address this gap, we propose a performant semi-supervised framework that leverages unlabeled RGB-only driving sequences - data easily collected with cost-effective RGB cameras - to significantly improve temporal, camera-only 3D detectors. We observe that the standard semi-supervised pseudo-labeling paradigm underperforms in this temporal, camera-only setting due to poor 3D localization of pseudo-labels. To address this, we train a single 3D detector to handle RGB sequences both forward and backward in time, then ensemble both its forwards and backwards pseudo-labels for semi-supervised learning. We further improve the pseudo-label quality by leveraging 3D object tracking to infill missing detections and by eschewing simple confidence thresholding in favor of using the auxiliary 2D detection head to filter 3D predictions. Finally, to enable the backbone to learn directly from the unlabeled data itself, we introduce an object-query conditioned masked reconstruction objective. Our framework demonstrates remarkable performance improvement on large-scale autonomous driving datasets nuScenes and nuPlan.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Park_Leveraging_Temporal_Cues_for_Semi-Supervised_Multi-View_3D_Object_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Park_Leveraging_Temporal_Cues_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Park_2025_CVPR,
    author    = {Park, Jinhyung and Sanghvi, Navyata and Adachi, Hiroki and Shibata, Yoshihisa and Hunt, Shawn and Tanaka, Shinya and Fujiyoshi, Hironobu and Kitani, Kris},
    title     = {Leveraging Temporal Cues for Semi-Supervised Multi-View 3D Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27401-27412}
}

raw_affiliation:
[{"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "DENSO Corporation", "city": "Kariya", "country": "Japan"}, {"affiliation": "DENSO International America, Inc.", "city": "Southfield", "country": "USA"}]


title: Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution
authors: Shijun Shi, Jing Xu, Lijing Lu, Zhihang Li, Kai Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7385-7395
cities: ["Wuxi", "Hefei", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shi_Self-supervised_ControlNet_with_Spatio-Temporal_Mamba_for_Real-world_Video_Super-resolution_CVPR_2025_paper.html

abstract:
Existing diffusion-based video super-resolution (VSR) methods are susceptible to introducing complex degradations and noticeable artifacts into high-resolution videos due to their inherent randomness. In this paper, we propose a noise-robust real-world VSR framework by incorporating self-supervised learning and Mamba into pre-trained latent diffusion models. To ensure content consistency across adjacent frames, we enhance the diffusion model with a global spatio-temporal attention mechanism using the Video State-Space block with a 3D Selective Scan module, which reinforces coherence at an affordable computational cost. To further reduce artifacts in generated details, we introduce a self-supervised ControlNet that leverages HR features as guidance and employs contrastive learning to extract degradation-insensitive features from LR videos. Finally, a three-stage training strategy based on a mixture of HR-LR videos is proposed to stabilize VSR training. The proposed Self-supervised ControlNet with Spatio-Temporal Continuous Mamba based VSR algorithm achieves superior perceptual quality than state-of-the-arts on real-world VSR benchmark datasets, validating the effectiveness of the proposed model design and training strategies.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shi_Self-supervised_ControlNet_with_Spatio-Temporal_Mamba_for_Real-world_Video_Super-resolution_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Shi_2025_CVPR,
    author    = {Shi, Shijun and Xu, Jing and Lu, Lijing and Li, Zhihang and Hu, Kai},
    title     = {Self-supervised ControlNet with Spatio-Temporal Mamba for Real-world Video Super-resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7385-7395}
}

raw_affiliation:
[{"affiliation": "Jiangnan University", "city": "Wuxi", "country": "China"}, {"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Chinese Academy of Sciences", "city": "Beijing", "country": "China"}]

affiliations:
["Jiangnan University", "University of Science and Technology of China", "Peking University", "Chinese Academy of Sciences"]


title: LATTE-MV: Learning to Anticipate Table Tennis Hits from Monocular Videos
authors: Daniel Etaat, Dvij Kalaria, Nima Rahmanian, S. Shankar Sastry
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7115-7124
raw_affiliation: [{"affiliation": "University of California, Berkeley", "city": "Berkeley", "country": "USA"}]
affiliations: ["University of California, Berkeley"]
cities: ["Berkeley"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Etaat_LATTE-MV_Learning_to_Anticipate_Table_Tennis_Hits_from_Monocular_Videos_CVPR_2025_paper.html

abstract:
Physical agility is a necessary skill in competitive table tennis, but by no means sufficient. Champions excel in this fast-paced and highly dynamic environment by anticipating their opponent's intent - buying themselves the necessary time to react. In this work, we take one step towards designing such an anticipatory agent. Previous works have developed systems capable of real-time table tennis gameplay, though they often do not leverage anticipation. Among the works that forecast opponent actions, their approaches are limited by dataset size and variety. Our paper contributes (1) a scalable system for reconstructing monocular video of table tennis matches in 3D and (2) an uncertainty-aware controller that anticipates opponent actions. We demonstrate in simulation that our policy improves the ball return rate against high-speed hits from 49.9% to 59.0% as compared to a baseline non-anticipatory policy.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Etaat_LATTE-MV_Learning_to_Anticipate_Table_Tennis_Hits_from_Monocular_Videos_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Etaat_LATTE-MV_Learning_to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Etaat_2025_CVPR,
    author    = {Etaat, Daniel and Kalaria, Dvij and Rahmanian, Nima and Sastry, S. Shankar},
    title     = {LATTE-MV: Learning to Anticipate Table Tennis Hits from Monocular Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7115-7124}
}


title: Logits DeConfusion with CLIP for Few-Shot Learning
authors: Shuo Li, Fang Liu, Zehua Hao, Xinyi Wang, Lingling Li, Xu Liu, Puhua Chen, Wenping Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25411-25421
cities: ["Xi'an"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Logits_DeConfusion_with_CLIP_for_Few-Shot_Learning_CVPR_2025_paper.html

abstract:
With its powerful visual-language alignment capability, CLIP performs well in zero-shot and few-shot learning tasks. However, we found in experiments that CLIP's logits suffer from serious inter-class confusion problems in downstream tasks, and the ambiguity between categories seriously affects the accuracy. To address this challenge, we propose a novel method called Logits DeConfusion, which effectively learns and eliminates inter-class confusion in logits by combining our Multi-level Adapter Fusion (MAF) module with our Inter-Class Deconfusion (ICD) module. Our MAF extracts features from different levels and fuses them uniformly to enhance feature representation. Our ICD learnably eliminates inter-class confusion in logits with a residual structure. Experimental results show that our method can significantly improve the classification performance and alleviate the inter-class confusion problem. The code is available at https://github.com/LiShuo1001/LDC.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Logits_DeConfusion_with_CLIP_for_Few-Shot_Learning_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Shuo and Liu, Fang and Hao, Zehua and Wang, Xinyi and Li, Lingling and Liu, Xu and Chen, Puhua and Ma, Wenping},
    title     = {Logits DeConfusion with CLIP for Few-Shot Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25411-25421}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University", "city": "Xi'an", "country": "China"}]

affiliations:
["Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University"]


title: Distilling Spatially-Heterogeneous Distortion Perception for Blind Image Quality Assessment
authors: Xudong Li, Wenjie Nie, Yan Zhang, Runze Hu, Ke Li, Xiawu Zheng, Liujuan Cao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2344-2354
cities: ["Xiamen", "Beijing", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Distilling_Spatially-Heterogeneous_Distortion_Perception_for_Blind_Image_Quality_Assessment_CVPR_2025_paper.html

abstract:
In the Blind Image Quality Assessment (BIQA) field, accurately assessing the quality of authentically distorted images presents a substantial challenge due to the diverse distortion types in natural settings. Existing state-of-the-art IQA methods mix a sequence of distortions into entire images to establish global distortion priors, but are inadequate for authentic images with spatially varied distortions. To address this, we introduce a novel IQA framework that employs knowledge distillation tailored to perceive spatially heterogeneous distortions, enhancing quality-distortion awareness. Specifically, we introduce a novel Block-wise Degradation Modelling approach that applies distinct distortions to different spatial blocks of an image, thereby expanding local distortion priors. Following this, we present a Block-wise Aggregation and Filtering module that enables fine-grained attention to the quality information within different distortion areas of the image. Furthermore, to effectively capture the complex relationships between distortions across different regions while preserving overall quality perception, we introduce Contrastive Knowledge Distillation to enhance the model's ability to discriminate between different types of distortions and Affinity Knowledge Distillation to model the correlation among distortions in different regions. Extensive experiments on standard BIQA datasets demonstrate the effectiveness and competitiveness of the proposed method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Distilling_Spatially-Heterogeneous_Distortion_Perception_for_Blind_Image_Quality_Assessment_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Distilling_Spatially-Heterogeneous_Distortion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Xudong and Nie, Wenjie and Zhang, Yan and Hu, Runze and Li, Ke and Zheng, Xiawu and Cao, Liujuan},
    title     = {Distilling Spatially-Heterogeneous Distortion Perception for Blind Image Quality Assessment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2344-2354}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 361005, P.R. China", "city": "Xiamen", "country": "China"}, {"affiliation": "School of Information and Electronics, Beijing Institute of Technology, Beijing, China", "city": "Beijing", "country": "China"}, {"affiliation": "Tencent Youtu Lab, Shanghai, China", "city": "Shanghai", "country": "China"}]

affiliations:
["Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, 361005, P.R. China", "School of Information and Electronics, Beijing Institute of Technology, Beijing, China", "Tencent Youtu Lab, Shanghai, China"]


title: Pay Attention to the Foreground in Object-Centric Learning
authors: Pinzhuo Tian, Shengjie Yang, Hang Yu, Alex Kot
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30281-30290
cities: ["Shanghai", "Singapore"]
countries: ["China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tian_Pay_Attention_to_the_Foreground_in_Object-Centric_Learning_CVPR_2025_paper.html

abstract:
The slot attention-based method is widely used in unsupervised object-centric learning, aiming to decompose scenes into interpretable objects and associate them with slots. However, complex backgrounds in the real images can disrupt the model's focus, leading it to excessively segment background stuff into different regions based on low-level information such as color or texture variations. As a result, the detailed segmentation of foreground objects, which requires shape or geometric information, is often neglected.  To address this issue, we introduce a contrastive learning-based indicator designed to differentiate between foreground and background. Integrating this indicator into the existing slot attention-based method enables the model to focus more on segmenting foreground objects while minimizing background distractions. During the testing phase, we utilize a spectral clustering mechanism to refine the results based on the similarity between the slots. Experimental results show that incorporating our method with various state-of-the-art models significantly improves their performance on both simulated data and real-world datasets. Furthermore, multiple sets of ablation experiments confirm the effectiveness of each proposed component.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tian_Pay_Attention_to_the_Foreground_in_Object-Centric_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tian_Pay_Attention_to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tian_2025_CVPR,
    author    = {Tian, Pinzhuo and Yang, Shengjie and Yu, Hang and Kot, Alex},
    title     = {Pay Attention to the Foreground in Object-Centric Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30281-30290}
}

raw_affiliation:
[{"affiliation": "School of Computer Engineering and Science, Shanghai University, China", "city": "Shanghai", "country": "China"}, {"affiliation": "School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore", "city": "Singapore", "country": "Singapore"}]

affiliations:
["School of Computer Engineering and Science, Shanghai University, China", "School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3583-3592
cities: ["Stony Brook", "Seoul", "Montreal", "Montreal", "Montreal"]
countries: ["USA", "South Korea", "Canada", "Canada", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_2DMamba_Efficient_State_Space_Model_for_Image_Representation_with_Applications_CVPR_2025_paper.html

title:
2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification

authors:
Jingwei Zhang, Anh Tien Nguyen, Xi Han, Vincent Quoc-Huy Trinh, Hong Qin, Dimitris Samaras, Mahdi S. Hosseini

abstract:
Efficiently modeling large 2D contexts is essential for various fields including Giga-Pixel Whole Slide Imaging (WSI) and remote sensing. Transformer-based models offer high parallelism but face challenges due to their quadratic complexity for handling long sequences. Recently, Mamba introduced a selective State Space Model (SSM) with linear complexity and high parallelism, enabling effective and efficient modeling of wide context in 1D sequences. However, extending Mamba to vision tasks, which inherently involve 2D structures, results in spatial discrepancies due to the limitations of 1D sequence processing. On the other hand, current 2D SSMs inherently model 2D structures but they suffer from prohibitively slow computation due to the lack of efficient parallel algorithms. In this work, we propose 2DMamba, a novel 2D selective SSM framework that incorporates the 2D spatial structure of images into Mamba, with a highly optimized hardware-aware operator, adopting both spatial continuity and computational efficiency. We validate the versatility of our approach on both WSIs and natural images. Extensive experiments on 10 public datasets for WSI classification and survival analysis show that 2DMamba improves up to 2.48% in AUC, 3.11% in F1 score, 2.47% in accuracy and 5.52% in C-index. Additionally, integrating our method with VMamba for natural imaging yields 0.5 to 0.7 improvements in mIoU on the ADE20k semantic segmentation dataset, and 0.2% accuracy improvement on ImageNet-1K classification dataset. Our code is available at https://github.com/AtlasAnalyticsLab/2DMamba.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_2DMamba_Efficient_State_Space_Model_for_Image_Representation_with_Applications_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_2DMamba_Efficient_State_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Jingwei and Nguyen, Anh Tien and Han, Xi and Trinh, Vincent Quoc-Huy and Qin, Hong and Samaras, Dimitris and Hosseini, Mahdi S.},
    title     = {2DMamba: Efficient State Space Model for Image Representation with Applications on Giga-Pixel Whole Slide Image Classification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3583-3592}
}

raw_affiliation:
[{"affiliation": "Stony Brook University", "city": "Stony Brook", "country": "USA"}, {"affiliation": "Korea University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Concordia University", "city": "Montreal", "country": "Canada"}, {"affiliation": "Mila\u2013Quebec AI Institute", "city": "Montreal", "country": "Canada"}, {"affiliation": "University of Montreal Hospital Center", "city": "Montreal", "country": "Canada"}]

affiliations:
["Stony Brook University", "Korea University", "Concordia University", "Mila\u2013Quebec AI Institute", "University of Montreal Hospital Center"]


title: Unboxed: Geometrically and Temporally Consistent Video Outpainting
authors: Zhongrui Yu, Martina Megaro-Boldini, Robert W. Sumner, Abdelaziz Djelouah
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7309-7319
affiliations: ["ETH Z \u00a8urich", "DisneyResearch|Studios"]
cities: ["Zurich", "Unknown"]
countries: ["Switzerland", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_Unboxed_Geometrically_and_Temporally_Consistent_Video_Outpainting_CVPR_2025_paper.html

abstract:
Extending the field of view of video content beyond its original version has many applications: immersive viewing experience with VR devices, reformatting 4:3 legacy content to today's viewing conditions with wide screens, or simply extending vertically captured phone videos. Many existing works focus on synthesizing the video using generative models only. Despite promising results, this strategy seems at the moment limited in terms of quality. In this work, we address this problem using two key ideas: 3D supported outpainting for the static regions of the images, and leveraging pre-trained video diffusion model to ensure realistic and temporally coherent results, particularly for the dynamic parts. In the first stage, we iterate between image outpainting and updating the 3D scene representation - we use 3D Gaussian Splatting. Then we consider dynamic objects independently per frame and inpaint missing pixels. Finally, we propose a denoising scheme that allows to maintain known reliable regions and update the dynamic parts to obtain temporally realistic results.We achieve state-of-the-art video outpainting. This is validated quantitatively and through a user study. We are also able to extend the field of view largely beyond the limits reached by existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_Unboxed_Geometrically_and_Temporally_Consistent_Video_Outpainting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_Unboxed_Geometrically_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Zhongrui and Megaro-Boldini, Martina and Sumner, Robert W. and Djelouah, Abdelaziz},
    title     = {Unboxed: Geometrically and Temporally Consistent Video Outpainting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7309-7319}
}

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


authors: Zhikai Li, Xuewen Liu, Dongrong Joe Fu, Jianquan Li, Qingyi Gu, Kurt Keutzer, Zhen Dong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9131-9141
affiliations: ["Institute of Automation, Chinese Academy of Sciences", "University of California, Berkeley"]
cities: ["Beijing", "Berkeley"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_K-Sort_Arena_Efficient_and_Reliable_Benchmarking_for_Generative_Models_via_CVPR_2025_paper.html

title:
K-Sort Arena: Efficient and Reliable Benchmarking for Generative Models via K-wise Human Preferences

abstract:
The rapid advancement of visual generative models necessitates efficient and reliable evaluation methods. Arena platform, which gathers user votes on model comparisons, can rank models with human preferences. However, traditional Arena methods, while established, require an excessive number of comparisons for ranking to converge and are vulnerable to preference noise in voting, suggesting the need for better approaches tailored to contemporary evaluation challenges. In this paper, we introduce K-Sort Arena, an efficient and reliable platform based on a key insight: images and videos possess higher perceptual intuitiveness than texts, enabling rapid evaluation of multiple samples simultaneously. Consequently, K-Sort Arena employs K-wise comparisons, allowing K models to engage in free-for-all competitions, which yield much richer information than pairwise comparisons. To enhance the robustness of the system, we leverage probabilistic modeling and Bayesian updating techniques. We propose an exploration-exploitation-based matchmaking strategy to facilitate more informative comparisons. In our experiments, K-Sort Arena exhibits 16.3xfaster convergence compared to the widely used ELO algorithm. To further validate the superiority and obtain a comprehensive leaderboard, we collect human feedback via crowdsourced evaluations of numerous cutting-edge text-to-image and text-to-video models. Thanks to its high efficiency, K-Sort Arena can continuously incorporate emerging models and update the leaderboard with minimal votes. Our project has undergone several months of internal testing and is now available online.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_K-Sort_Arena_Efficient_and_Reliable_Benchmarking_for_Generative_Models_via_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_K-Sort_Arena_Efficient_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Zhikai and Liu, Xuewen and Fu, Dongrong Joe and Li, Jianquan and Gu, Qingyi and Keutzer, Kurt and Dong, Zhen},
    title     = {K-Sort Arena: Efficient and Reliable Benchmarking for Generative Models via K-wise Human Preferences},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9131-9141}
}

raw_affiliation:
[{"affiliation": "Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "University of California, Berkeley", "city": "Berkeley", "country": "USA"}]


title: Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes
authors: Aodi Li, Liansheng Zhuang, Xiao Long, Minghong Yao, Shafei Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15349-15359
affiliations: ["University of Science and Technology of China", "Peng Cheng Laboratory"]
cities: ["Hefei", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Seeking_Consistent_Flat_Minima_for_Better_Domain_Generalization_via_Refining_CVPR_2025_paper.html

abstract:
Domain generalization aims to learn a model from multiple training domains and generalize it to unseen test domains. Recent theory has shown that seeking the deep models, whose parameters lie in the flat minima of the loss landscape, can significantly reduce the out-of-domain generalization error. However, existing methods often neglect the consistency of loss landscapes in different domains, resulting in models that are not simultaneously in the optimal flat minima in all domains, which limits their generalization ability. To address this issue, this paper proposes an iterative Self-Feedback Training (SFT) framework to seek consistent flat minima that are shared across different domains by progressively refining loss landscapes during training. It alternatively generates a feedback signal by measuring the inconsistency of loss landscapes in different domains and refines these loss landscapes for greater consistency using this feedback signal. Benefiting from the consistency of the flat minima within these refined loss landscapes, our SFT helps achieve better out-of-domain generalization. Extensive experiments on DomainBed demonstrate superior performances of SFT when compared to state-of-the-art sharpness-aware methods and other prevalent DG baselines. On average across five DG benchmarks, SFT surpasses the sharpness-aware minimization by 2.6% with ResNet-50 and 1.5% with ViT-B/16, respectively.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Seeking_Consistent_Flat_Minima_for_Better_Domain_Generalization_via_Refining_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Seeking_Consistent_Flat_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Aodi and Zhuang, Liansheng and Long, Xiao and Yao, Minghong and Wang, Shafei},
    title     = {Seeking Consistent Flat Minima for Better Domain Generalization via Refining Loss Landscapes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15349-15359}
}

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


authors: Federico Lincetto, Gianluca Agresti, Mattia Rossi, Pietro Zanuttigh
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10964-10973
affiliations: ["University of Padova", "Sony Europe Limited"]
cities: ["Padova", "London"]
countries: ["Italy", "United Kingdom"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lincetto_MultimodalStudio_A_Heterogeneous_Sensor_Dataset_and_Framework_for_Neural_Rendering_CVPR_2025_paper.html

title:
MultimodalStudio: A Heterogeneous Sensor Dataset and Framework for Neural Rendering across Multiple Imaging Modalities

abstract:
Neural Radiance Fields (NeRF) have shown impressive performances in the rendering of 3D scenes from arbitrary viewpoints. While RGB images are widely preferred for training volume rendering models, the interest in other radiance modalities is also growing. However, the capability of the underlying implicit neural models to learn and transfer information across heterogeneous imaging modalities has seldom been explored, mostly due to the limited training data availability. For this purpose, we present MultimodalStudio (MMS): it encompasses MMS-DATA and MMS-FW. MMS-DATA is a multimodal multi-view dataset containing 32 scenes acquired with 5 different imaging modalities: RGB, monochrome, near-infrared, polarization and multispectral. MMS-FW is a novel modular multimodal NeRF framework designed to handle multimodal raw data and able to support an arbitrary number of multi-channel devices. Through extensive experiments, we demonstrate that MMS-FW trained on MMS-DATA can transfer information between different imaging modalities and produce higher quality renderings than using single modalities alone. We publicly release the dataset and the framework, to promote the research on multimodal volume rendering and beyond.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lincetto_MultimodalStudio_A_Heterogeneous_Sensor_Dataset_and_Framework_for_Neural_Rendering_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lincetto_MultimodalStudio_A_Heterogeneous_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Lincetto_2025_CVPR,
    author    = {Lincetto, Federico and Agresti, Gianluca and Rossi, Mattia and Zanuttigh, Pietro},
    title     = {MultimodalStudio: A Heterogeneous Sensor Dataset and Framework for Neural Rendering across Multiple Imaging Modalities},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10964-10973}
}

raw_affiliation:
[{"affiliation": "University of Padova", "city": "Padova", "country": "Italy"}, {"affiliation": "Sony Europe Limited", "city": "London", "country": "United Kingdom"}]


title: Dense-SfM: Structure from Motion with Dense Consistent Matching
authors: JongMin Lee, Sungjoo Yoo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6404-6414
affiliations: ["Seoul National University", "Seoul National University"]
cities: ["Seoul", "Seoul"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Dense-SfM_Structure_from_Motion_with_Dense_Consistent_Matching_CVPR_2025_paper.html

abstract:
We present Dense-SfM, a novel Structure from Motion (SfM) framework designed for dense and accurate 3D reconstruction from multi-view images. Sparse keypoint matching, which traditional SfM methods often rely on, limits both accuracy and point density, especially in texture-less areas. Dense-SfM addresses this limitation by integrating dense matching with a Gaussian Splatting (GS) based track extension which gives more consistent, longer feature tracks. To further improve reconstruction accuracy, Dense-SfM is equipped with a multi-view kernelized matching module leveraging transformer and Gaussian Process architectures, for robust track refinement across multi-views. Evaluations on the ETH3D and Texture-Poor SfM datasets show that Dense-SfM offers significant improvements in accuracy and density over state-of-the-art methods. Project page: https://icetea-cv.github.io/densesfm/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_Dense-SfM_Structure_from_Motion_with_Dense_Consistent_Matching_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_Dense-SfM_Structure_from_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, JongMin and Yoo, Sungjoo},
    title     = {Dense-SfM: Structure from Motion with Dense Consistent Matching},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6404-6414}
}

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


title: FluidNexus: 3D Fluid Reconstruction and Prediction from a Single Video
authors: Yue Gao, Hong-Xing Yu, Bo Zhu, Jiajun Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26091-26101
affiliations: ["Stanford University", "Microsoft", "Georgia Institute of Technology"]
cities: ["Stanford", "Redmond", "Atlanta"]
countries: ["USA", "USA", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_FluidNexus_3D_Fluid_Reconstruction_and_Prediction_from_a_Single_Video_CVPR_2025_paper.html

abstract:
We study reconstructing and predicting 3D fluid appearance and velocity from a single video. Current methods require multi-view videos for fluid reconstruction. We present FluidNexus, a novel framework that bridges video generation and physics simulation to tackle this task. Our key insight is to synthesize multiple novel-view videos as references for reconstruction. FluidNexus consists of two key components: (1) a novel-view video synthesizer that combines frame-wise view synthesis with video diffusion refinement for generating realistic videos, and (2) a physics-integrated particle representation coupling differentiable simulation and rendering to simultaneously facilitate 3D fluid reconstruction and prediction. To evaluate our approach, we collect two new real-world fluid datasets featuring textured backgrounds and object interactions. Our method enables dynamic novel view synthesis, future prediction, and interaction simulation from a single fluid video. Project website: https://yuegao.me/FluidNexus.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_FluidNexus_3D_Fluid_Reconstruction_and_Prediction_from_a_Single_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_FluidNexus_3D_Fluid_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gao_2025_CVPR,
    author    = {Gao, Yue and Yu, Hong-Xing and Zhu, Bo and Wu, Jiajun},
    title     = {FluidNexus: 3D Fluid Reconstruction and Prediction from a Single Video},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26091-26101}
}

raw_affiliation:
[{"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}, {"affiliation": "Microsoft", "city": "Redmond", "country": "USA"}, {"affiliation": "Georgia Institute of Technology", "city": "Atlanta", "country": "USA"}]


title: MuTri: Multi-view Tri-alignment for OCT to OCTA 3D Image Translation
authors: Zhuangzhuang Chen, Hualiang Wang, Chubin Ou, Xiaomeng Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20885-20894
cities: ["Hong Kong", "Guangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_MuTri_Multi-view_Tri-alignment_for_OCT_to_OCTA_3D_Image_Translation_CVPR_2025_paper.html

abstract:
Optical coherence tomography angiography (OCTA) shows its great importance in imaging microvascular networks by providing accurate 3D imaging of blood vessels, but it relies upon specialized sensors and expensive devices. For this reason, previous works show the potential to translate the readily available 3D Optical Coherence Tomography (OCT) images into 3D OCTA images.  However, existing OCTA translation methods directly learn the mapping from the OCT domain to the OCTA domain in continuous and infinite space with guidance from only a single view, i.e., the OCTA project map, resulting in suboptimal results. To this end, we propose the multi-view Tri-alignment framework for OCT to OCTA 3D image translation in discrete and finite space, named MuTri. In the first stage, we pre-train two vector-quantized variational auto-encoder (VQVAE) by reconstructing 3D OCT and 3D OCTA data, providing semantic prior for subsequent multi-view guidances. In the second stage, our multi-view tri-alignment facilitates another VQVAE model to learn the mapping from the OCT domain to the OCTA domain in discrete and finite space. Specifically, a contrastive-inspired semantic alignment is proposed to maximize the mutual information with the pre-trained models from OCT and OCTA views, to facilitate codebook learning. Meanwhile, a vessel structure alignment is proposed to minimize the structure discrepancy with the pre-trained models from the OCTA project map view, benefiting from learning the detailed vessel structure information. We also collect the first large-scale dataset, namely, OCTA2024, which contains a pair of OCT and OCTA volumes from 846 subjects. Our codes and datasets are available at: https://github.com/xmed-lab/MuTri.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_MuTri_Multi-view_Tri-alignment_for_OCT_to_OCTA_3D_Image_Translation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_MuTri_Multi-view_Tri-alignment_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Zhuangzhuang and Wang, Hualiang and Ou, Chubin and Li, Xiaomeng},
    title     = {MuTri: Multi-view Tri-alignment for OCT to OCTA 3D Image Translation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20885-20894}
}

raw_affiliation:
[{"affiliation": "Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "Department of Radiology, Guangdong Provincial People\u2019s Hospital, Southern Medical University", "city": "Guangzhou", "country": "China"}]

affiliations:
["Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology", "Department of Radiology, Guangdong Provincial People\u2019s Hospital, Southern Medical University"]


title: Sketchy Bounding-box Supervision for 3D Instance Segmentation
authors: Qian Deng, Le Hui, Jin Xie, Jian Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8879-8888
cities: ["Tianjin", "Xi\u2019an", "Nanjing", "Suzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Deng_Sketchy_Bounding-box_Supervision_for_3D_Instance_Segmentation_CVPR_2025_paper.html

abstract:
Bounding box supervision has gained considerable attention in weakly supervised 3D instance segmentation. While this approach alleviates the need for extensive point-level annotations, obtaining accurate bounding boxes in practical applications remains challenging. To this end, we explore the inaccurate bounding box, named sketchy bounding box, which is imitated through perturbing ground truth bounding box by adding scaling, translation, and rotation. In this paper, we propose Sketchy-3DIS, a novel weakly 3D instance segmentation framework, which jointly learns pseudo labeler and segmentator to improve the performance under the sketchy bounding-box supervisions. Specifically, we first propose an adaptive box-to-point pseudo labeler that adaptively learns to assign points located in the overlapped parts between two sketchy bounding boxes to the correct instance, resulting in compact and pure pseudo instance labels. Then, we present a coarse-to-fine instance segmentator that first predicts coarse instances from the entire point cloud and then learns fine instances based on the region of coarse instances. Finally, by using the pseudo instance labels to supervise the instance segmentator, we can gradually generate high-quality instances through joint training. Extensive experiments show that our method achieves state-of-the-art performance on both the ScanNetV2 and S3DIS benchmarks, and even outperforms several fully supervised methods using sketchy bounding boxes. Code is available at https://github.com/dengq7/Sketchy-3DIS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Deng_Sketchy_Bounding-box_Supervision_for_3D_Instance_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Deng_Sketchy_Bounding-box_Supervision_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Deng_2025_CVPR,
    author    = {Deng, Qian and Hui, Le and Xie, Jin and Yang, Jian},
    title     = {Sketchy Bounding-box Supervision for 3D Instance Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8879-8888}
}

raw_affiliation:
[{"affiliation": "PCA Lab, College of Computer Science, Nankai University, Tianjin, China", "city": "Tianjin", "country": "China"}, {"affiliation": "School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an, China", "city": "Xi\u2019an", "country": "China"}, {"affiliation": "State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China", "city": "Nanjing", "country": "China"}, {"affiliation": "School of Intelligence Science and Technology, Nanjing University, Suzhou, China", "city": "Suzhou", "country": "China"}]

affiliations:
["PCA Lab, College of Computer Science, Nankai University, Tianjin, China", "School of Electronics and Information, Northwestern Polytechnical University, Xi\u2019an, China", "State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China", "School of Intelligence Science and Technology, Nanjing University, Suzhou, China"]


authors: Wenhao Shen, Mingliang Zhou, Yu Chen, Xuekai Wei, Yong Feng, Huayan Pu, Weijia Jia
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17990-17999
cities: ["Chongqing", "Chongqing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shen_Image_Quality_Assessment_Investigating_Causal_Perceptual_Effects_with_Abductive_Counterfactual_CVPR_2025_paper.html

title:
Image Quality Assessment: Investigating Causal Perceptual Effects with Abductive Counterfactual Inference

abstract:
Existing full-reference image quality assessment (FR-IQA) methods often fail to capture the complex causal mechanisms that underlie human perceptual responses to image distortions, limiting their ability to generalize across diverse scenarios. In this paper, we propose an FR-IQA method based on abductive counterfactual inference to investigate the causal relationships between deep network features and perceptual distortions. First, we explore the causal effects of deep features on perception and integrate causal reasoning with feature comparison, constructing a model that effectively handles complex distortion types across different IQA scenarios. Second, the analysis of the perceptual causal correlations of our proposed method is independent of the backbone architecture and thus can be applied to a variety of deep networks. Through abductive counterfactual experiments, we validate the proposed causal relationships, confirming the model's superior perceptual relevance and interpretability of quality scores. The experimental results demonstrate the robustness and effectiveness of the method, providing competitive quality predictions across multiple benchmarks. The source code is available at https://anonymous.4open.science/r/DeepCausalQuality-25BC.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_Image_Quality_Assessment_Investigating_Causal_Perceptual_Effects_with_Abductive_Counterfactual_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Shen_2025_CVPR,
    author    = {Shen, Wenhao and Zhou, Mingliang and Chen, Yu and Wei, Xuekai and Feng, Yong and Pu, Huayan and Jia, Weijia},
    title     = {Image Quality Assessment: Investigating Causal Perceptual Effects with Abductive Counterfactual Inference},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17990-17999}
}

raw_affiliation:
[{"affiliation": "College of Computer Science, Chongqing University", "city": "Chongqing", "country": "China"}, {"affiliation": "State Key Laboratory of Mechanical Transmissions, Chongqing University", "city": "Chongqing", "country": "China"}, {"affiliation": "BNU-UIC Institute of Artificial Intelligence and Future Networks, Beijing Normal University", "city": "Beijing", "country": "China"}]

affiliations:
["College of Computer Science, Chongqing University", "State Key Laboratory of Mechanical Transmissions, Chongqing University", "BNU-UIC Institute of Artificial Intelligence and Future Networks, Beijing Normal University"]


title: Pos3R: 6D Pose Estimation for Unseen Objects Made Easy
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16818-16828
affiliations: ["Australian National University", "RIOS Intelligent Machines"]
cities: ["Canberra", "Sydney"]
countries: ["Australia", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Deng_Pos3R_6D_Pose_Estimation_for_Unseen_Objects_Made_Easy_CVPR_2025_paper.html

authors:
Weijian Deng, Dylan Campbell, Chunyi Sun, Jiahao Zhang, Shubham Kanitkar, Matt E. Shaffer, Stephen Gould

abstract:
Foundation models have significantly reduced the need for task-specific training, while also enhancing generalizability. However, state-of-the-art 6D pose estimators either require further training with pose supervision or neglect advances obtainable from 3D foundation models. The latter is a missed opportunity, since these models are better equipped to predict 3D-consistent features, which are of significant utility for the pose estimation task. To address this gap, we propose Pos3R, a method for estimating the 6D pose of any object from a single RGB image, making extensive use of a 3D reconstruction foundation model and requiring no additional training. We identify template selection as a particular bottleneck for existing methods that is significantly alleviated by the use of a 3D model, which can more easily distinguish between template poses than a 2D model. Despite its simplicity, Pos3R achieves competitive performance on the Benchmark for 6D Object Pose Estimation (BOP), matching or surpassing existing refinement-free methods. Additionally, Pos3R integrates seamlessly with render-and-compare refinement techniques, demonstrating adaptability for high-precision applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Deng_Pos3R_6D_Pose_Estimation_for_Unseen_Objects_Made_Easy_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Deng_Pos3R_6D_Pose_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Deng_2025_CVPR,
    author    = {Deng, Weijian and Campbell, Dylan and Sun, Chunyi and Zhang, Jiahao and Kanitkar, Shubham and Shaffer, Matt E. and Gould, Stephen},
    title     = {Pos3R: 6D Pose Estimation for Unseen Objects Made Easy},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16818-16828}
}

raw_affiliation:
[{"affiliation": "Australian National University", "city": "Canberra", "country": "Australia"}, {"affiliation": "RIOS Intelligent Machines", "city": "Sydney", "country": "Australia"}]


title: DeformCL: Learning Deformable Centerline Representation for Vessel Extraction in 3D Medical Image
authors: Ziwei Zhao, Zhixing Zhang, Yuhang Liu, Zhao Zhang, Haojun Yu, Dong Wang, Liwei Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30896-30905
cities: ["Unknown", "Beijing", "Beijing", "Beijing", "Guangzhou"]
countries: ["Unknown", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_DeformCL_Learning_Deformable_Centerline_Representation_for_Vessel_Extraction_in_3D_CVPR_2025_paper.html

abstract:
In the field of 3D medical imaging, accurately extracting and representing the blood vessels with curvilinear structures holds paramount importance for clinical diagnosis. Previous methods have commonly relied on discrete representation like mask, often resulting in local fractures or scattered fragments due to the inherent limitations of the per-pixel classification paradigm. In this work, we introduce DeformCL, a new continuous representation based on Deformable Centerlines, where centerline points act as nodes connected by edges that capture spatial relationships. Compared with previous representations, DeformCL offers three key advantages: natural connectivity, noise robustness, and interaction facility. We present a comprehensive training pipeline structured in a cascaded manner to fully exploit these favorable properties of DeformCL. Extensive experiments on four 3D vessel segmentation datasets demonstrate the effectiveness and superiority of our method. Furthermore, the visualization of curved planar reformation images validates the clinical significance of the proposed framework.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_DeformCL_Learning_Deformable_Centerline_Representation_for_Vessel_Extraction_in_3D_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_DeformCL_Learning_Deformable_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Ziwei and Zhang, Zhixing and Liu, Yuhang and Zhang, Zhao and Yu, Haojun and Wang, Dong and Wang, Liwei},
    title     = {DeformCL: Learning Deformable Centerline Representation for Vessel Extraction in 3D Medical Image},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30896-30905}
}

raw_affiliation:
[{"affiliation": "Yizhun Medical AI Co., Ltd", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Center for Data Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Center for Machine Learning Research, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Pazhou Laboratory (Huangpu), Guangzhou, Guangdong, China", "city": "Guangzhou", "country": "China"}]

affiliations:
["Yizhun Medical AI Co., Ltd", "Center for Data Science, Peking University", "Center for Machine Learning Research, Peking University", "State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University", "Pazhou Laboratory (Huangpu), Guangzhou, Guangdong, China"]


title: StreetCrafter: Street View Synthesis with Controllable Video Diffusion Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 822-832
affiliations: ["Zhejiang University", "Li Auto Inc.", "Cornell University"]
cities: ["Hangzhou", "Beijing", "Ithaca"]
countries: ["China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yan_StreetCrafter_Street_View_Synthesis_with_Controllable_Video_Diffusion_Models_CVPR_2025_paper.html

authors:
Yunzhi Yan, Zhen Xu, Haotong Lin, Haian Jin, Haoyu Guo, Yida Wang, Kun Zhan, Xianpeng Lang, Hujun Bao, Xiaowei Zhou, Sida Peng

abstract:
This paper aims to tackle the problem of photorealistic view synthesis from vehicle sensors data. Recent advancements in neural scene representation have achieved notable success in rendering high-quality autonomous driving scenes,but the performance significantly degrades as the viewpoint deviates from the training trajectory. To mitigate this problem, we introduce StreetCrafter, a novel controllable video diffusion model that utilizes LiDAR point cloud renderings as pixel-level conditions, which fully exploits the generative prior for novel view synthesis, while preserving precise camera control.Moreover, the utilization of pixel-level LiDAR condition allows us to make accurate pixel-level edits to target scenes.In addition, the generative prior of StreetCrafter can be effectively incorporated into dynamic scene representations to achieve real-time rendering. Experiments on Waymo Open and Pandaset datasets demonstrate that our model enables flexible control over viewpoint changes, enlarging the view synthesis regions for satisfying rendering, which outperforms existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yan_StreetCrafter_Street_View_Synthesis_with_Controllable_Video_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yan_StreetCrafter_Street_View_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yan_2025_CVPR,
    author    = {Yan, Yunzhi and Xu, Zhen and Lin, Haotong and Jin, Haian and Guo, Haoyu and Wang, Yida and Zhan, Kun and Lang, Xianpeng and Bao, Hujun and Zhou, Xiaowei and Peng, Sida},
    title     = {StreetCrafter: Street View Synthesis with Controllable Video Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {822-832}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Li Auto Inc.", "city": "Beijing", "country": "China"}, {"affiliation": "Cornell University", "city": "Ithaca", "country": "USA"}]


title: OCRT: Boosting Foundation Models in the Open World with Object-Concept-Relation Triad
authors: Luyao Tang, Yuxuan Yuan, Chaoqi Chen, Zeyu Zhang, Yue Huang, Kun Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25422-25433
cities: ["Xiamen", "Xiamen", "Shenzhen", "Canberra", "Pittsburgh", "Abu Dhabi"]
countries: ["China", "China", "China", "Australia", "USA", "UAE"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_OCRT_Boosting_Foundation_Models_in_the_Open_World_with_Object-Concept-Relation_CVPR_2025_paper.html

abstract:
Although foundation models (FMs) claim to be powerful, their generalization ability significantly decreases when faced with distribution shifts, weak supervision, or malicious attacks in the open world. On the other hand, most domain generalization or adversarial fine-tuning methods are task-related or model-specific, ignoring the universality in practical applications and the transferability between FMs. This paper delves into the problem of generalizing FMs to the out-of-domain data. We propose a novel framework, Object-Concept-Relation Triad (OCRT), that enables FMs to extract sparse, high-level concepts and intricate relational structures from raw visual inputs. The key idea is to bind objects in visual scenes and a set of object-centric representations through unsupervised decoupling and iterative refinement. To be specific, we project the object-centric representations onto a semantic concept space that the model can readily interpret, and estimate their importance to filter out irrelevant elements. Then, a concept-based graph, which has a flexible degree, is constructed to incorporate the set of concepts and their corresponding importance, enabling the extraction of high-order factors from informative concepts and facilitating relational reasoning among these concepts. Extensive experiments demonstrate that OCRT can substantially boost the generalizability and robustness of SAM and CLIP across multiple downstream tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_OCRT_Boosting_Foundation_Models_in_the_Open_World_with_Object-Concept-Relation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tang_OCRT_Boosting_Foundation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Luyao and Yuan, Yuxuan and Chen, Chaoqi and Zhang, Zeyu and Huang, Yue and Zhang, Kun},
    title     = {OCRT: Boosting Foundation Models in the Open World with Object-Concept-Relation Triad},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25422-25433}
}

raw_affiliation:
[{"affiliation": "Key Laboratory of Multimedia Trusted Perception and Ef\ufb01cient Computing, Ministry of Education of China, Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "School of Informatics, Xiamen University", "city": "Xiamen", "country": "China"}, {"affiliation": "Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "The Australian National University", "city": "Canberra", "country": "Australia"}, {"affiliation": "Carnegie Mellon University", "city": "Pittsburgh", "country": "USA"}, {"affiliation": "Mohamed bin Zayed University of Arti\ufb01cial Intelligence", "city": "Abu Dhabi", "country": "UAE"}]

affiliations:
["Key Laboratory of Multimedia Trusted Perception and Ef\ufb01cient Computing, Ministry of Education of China, Xiamen University", "School of Informatics, Xiamen University", "Shenzhen University", "The Australian National University", "Carnegie Mellon University", "Mohamed bin Zayed University of Arti\ufb01cial Intelligence"]


title: SPARS3R: Semantic Prior Alignment and Regularization for Sparse 3D Reconstruction
authors: Yutao Tang, Yuxiang Guo, Deming Li, Cheng Peng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26810-26821
raw_affiliation: [{"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}]
affiliations: ["Johns Hopkins University"]
cities: ["Baltimore"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_SPARS3R_Semantic_Prior_Alignment_and_Regularization_for_Sparse_3D_Reconstruction_CVPR_2025_paper.html

abstract:
Recent efforts in Gaussian-Splat-based Novel View Synthesis can achieve photorealistic rendering; however, such capability is limited in sparse-view scenarios due to sparse initialization and over-fitting floaters. Recent progress in depth estimation and alignment can provide dense point cloud using few views; however, the resulting pose accuracy is suboptimal. In this work, we present SPARS3R, which combines the advantages of accurate pose estimation from Structure-from-Motion and dense point cloud from depth estimation. To this end, SPARS3R first performs a Global Fusion Alignment process that maps a prior dense point cloud to a sparse point cloud from Structure-from-Motion based on triangulated correspondences. RANSAC is applied during this process to distinguish inliers and outliers. SPARS3R then performs a second, Semantic Outlier Alignment step, which extracts semantically coherent regions around the outliers and performs local alignment in these regions. Along with several improvements in the evaluation process, we demonstrate that SPARS3R can achieve photorealistic rendering with sparse images and significantly outperforms existing approaches.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_SPARS3R_Semantic_Prior_Alignment_and_Regularization_for_Sparse_3D_Reconstruction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tang_SPARS3R_Semantic_Prior_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Yutao and Guo, Yuxiang and Li, Deming and Peng, Cheng},
    title     = {SPARS3R: Semantic Prior Alignment and Regularization for Sparse 3D Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26810-26821}
}


authors: Hanzhi Chen, Boyang Sun, Anran Zhang, Marc Pollefeys, Stefan Leutenegger
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27661-27672
affiliations: ["Technical University of Munich", "ETH Z\u00a8urich", "Microsoft"]
cities: ["Munich", "Zurich", "Redmond"]
countries: ["Germany", "Switzerland", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_VidBot_Learning_Generalizable_3D_Actions_from_In-the-Wild_2D_Human_Videos_CVPR_2025_paper.html

title:
VidBot: Learning Generalizable 3D Actions from In-the-Wild 2D Human Videos for Zero-Shot Robotic Manipulation

abstract:
Future robots are envisioned as versatile systems capable of performing a variety of household tasks. The big question remains, how can we bridge the embodiment gap while minimizing physical robot learning, which fundamentally does not scale well. We argue that learning from in-the-wild human videos offers a promising solution for robotic manipulation tasks, as vast amounts of relevant data already exist on the internet. In this work, we present VidBot, a framework enabling zero-shot robotic manipulation using learned 3D affordance from in-the-wild monocular RGB-only human videos. VidBot leverages a pipeline to extract explicit representations from them, namely 3D hand trajectories from videos, combining a depth foundation model with structure-from-motion techniques to reconstruct temporally consistent, metric-scale 3D affordance representations agnostic to embodiments. We introduce a coarse-to-fine affordance learning model that first identifies coarse actions from the pixel space and then generates fine-grained interaction trajectories with a diffusion model, conditioned on coarse actions and guided by test-time constraints for context-aware interaction planning, enabling substantial generalization to novel scenes and embodiments. Extensive experiments demonstrate the efficacy of VidBot, which significantly outperforms counterparts across 13 manipulation tasks in zero-shot settings and can be seamlessly deployed across robot systems in real-world environments. VidBot paves the way for leveraging everyday human videos to make robot learning more scalable.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_VidBot_Learning_Generalizable_3D_Actions_from_In-the-Wild_2D_Human_Videos_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_VidBot_Learning_Generalizable_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Hanzhi and Sun, Boyang and Zhang, Anran and Pollefeys, Marc and Leutenegger, Stefan},
    title     = {VidBot: Learning Generalizable 3D Actions from In-the-Wild 2D Human Videos for Zero-Shot Robotic Manipulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27661-27672}
}

raw_affiliation:
[{"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "ETH Z\u00a8urich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "Microsoft", "city": "Redmond", "country": "USA"}]


title: Learning Person-Specific Animatable Face Models from In-the-Wild Images via a Shared Base Model
authors: Yuxiang Mao, Zhenfeng Fan, ZhiJie Zhang, Zhiheng Zhang, Shihong Xia
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5602-5613
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mao_Learning_Person-Specific_Animatable_Face_Models_from_In-the-Wild_Images_via_a_CVPR_2025_paper.html

abstract:
Training a generic 3D face reconstruction model in a self-supervised manner using large-scale, in-the-wild 2D face image datasets enhances robustness to varying lighting conditions and occlusions while allowing the model to capture animatable wrinkle details across diverse facial expressions. However, a generic model often fails to adequately represent the unique characteristics of specific individuals. In this paper, we propose a method to train a generic base model and then transfer it to yield person-specific models by integrating lightweight adapters within the large-parameter ViT-MAE base model. These person-specific models excel at capturing individual facial shapes and detailed features while preserving the robustness and prior knowledge of detail variations from the base model. During training, we introduce a silhouette vertex re-projection loss to address boundary "landmark marching" issues on the 3D face caused by pose variations. Additionally, we employ an innovative teacher-student loss to leverage the inherent strengths of UNet in feature boundary localization for training our detail MAE. Quantitative and qualitative experiments demonstrate that our approach achieves state-of-the-art performance in face alignment, detail accuracy, and richness. The source code is available at https://github.com/danielmao2000/person-specific-animatable-face.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mao_Learning_Person-Specific_Animatable_Face_Models_from_In-the-Wild_Images_via_a_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mao_Learning_Person-Specific_Animatable_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Mao_2025_CVPR,
    author    = {Mao, Yuxiang and Fan, Zhenfeng and Zhang, ZhiJie and Zhang, Zhiheng and Xia, Shihong},
    title     = {Learning Person-Specific Animatable Face Models from In-the-Wild Images via a Shared Base Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5602-5613}
}

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"}]

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


title: TIMotion: Temporal and Interactive Framework for Efficient Human-Human Motion Generation
authors: Yabiao Wang, Shuo Wang, Jiangning Zhang, Ke Fan, Jiafu Wu, Zhucun Xue, Yong Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7169-7178
affiliations: ["Zhejiang University", "Youtu Lab, Tencent", "Shanghai Jiao Tong University"]
cities: ["Hangzhou", "Shenzhen", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_TIMotion_Temporal_and_Interactive_Framework_for_Efficient_Human-Human_Motion_Generation_CVPR_2025_paper.html

abstract:
Human-human motion generation is essential for understanding humans as social beings. Current methods fall into two main categories: single-person-based methods and separate modeling-based methods. To delve into this field, we abstract the overall generation process into a general framework MetaMotion, which consists of two phases: temporal modeling and interaction mixing. For temporal modeling, the single-person-based methods concatenate two people into a single one directly, while the separate modeling-based methods skip the modeling of interaction sequences. The inadequate modeling described above resulted in sub-optimal performance and redundant model parameters. In this paper, we introduce TIMotion (Temporal and Interactive Modeling), an efficient and effective framework for human-human motion generation. Specifically, we first propose Causal Interactive Injection to model two separate sequences as a causal sequence leveraging the temporal and causal properties. Then we present Role-Evolving Scanning to adjust to the change in the active and passive roles throughout the interaction. Finally, to generate smoother and more rational motion, we design Localized Pattern Amplification to capture short-term motion patterns.Extensive experiments on InterHuman and InterX demonstrate that our method achieves superior performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_TIMotion_Temporal_and_Interactive_Framework_for_Efficient_Human-Human_Motion_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_TIMotion_Temporal_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yabiao and Wang, Shuo and Zhang, Jiangning and Fan, Ke and Wu, Jiafu and Xue, Zhucun and Liu, Yong},
    title     = {TIMotion: Temporal and Interactive Framework for Efficient Human-Human Motion Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7169-7178}
}

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


authors: Sagnik Majumder, Tushar Nagarajan, Ziad Al-Halah, Reina Pradhan, Kristen Grauman
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29016-29028
affiliations: ["UT Austin", "FAIR, Meta", "University of Utah"]
cities: ["Austin", "Menlo Park", "Salt Lake City"]
countries: ["USA", "USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Majumder_Which_Viewpoint_Shows_it_Best_Language_for_Weakly_Supervising_View_CVPR_2025_paper.html

title:
Which Viewpoint Shows it Best? Language for Weakly Supervising View Selection in Multi-view Instructional Videos

abstract:
Given a multi-view video, which viewpoint is most informative for a human observer? Existing methods rely on heuristics or expensive "best-view" supervision to answer this question, limiting their applicability. We propose a weakly supervised approach that leverages language accompanying an instructional multi-view video as a means to recover its most informative viewpoint(s). Our key hypothesis is that the more accurately an individual view can predict a view-agnostic text summary, the more informative it is. To put this into action, we propose LangView, a framework that uses the relative accuracy of view dependent caption predictions as a proxy for best view pseudo-labels. Then, those pseudo-labels are used to train a view selector, together with an auxiliary camera pose predictor that enhances view-sensitivity. During inference, our model takes as input only a multi-view video--no language or camera poses--and returns the best viewpoint to watch at each timestep. On two challenging datasets comprised of diverse multi-camera setups and how-to activities, our model consistently outperforms state-of-the-art baselines, both with quantitative metrics and human evaluation. Project: https://vision.cs.utexas.edu/projects/which-view-shows-it-best.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Majumder_Which_Viewpoint_Shows_it_Best_Language_for_Weakly_Supervising_View_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Majumder_Which_Viewpoint_Shows_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Majumder_2025_CVPR,
    author    = {Majumder, Sagnik and Nagarajan, Tushar and Al-Halah, Ziad and Pradhan, Reina and Grauman, Kristen},
    title     = {Which Viewpoint Shows it Best? Language for Weakly Supervising View Selection in Multi-view Instructional Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29016-29028}
}

raw_affiliation:
[{"affiliation": "UT Austin", "city": "Austin", "country": "USA"}, {"affiliation": "FAIR, Meta", "city": "Menlo Park", "country": "USA"}, {"affiliation": "University of Utah", "city": "Salt Lake City", "country": "USA"}]


title: RaCFormer: Towards High-Quality 3D Object Detection via Query-based Radar-Camera Fusion
authors: Xiaomeng Chu, Jiajun Deng, Guoliang You, Yifan Duan, Houqiang Li, Yanyong Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17081-17091
cities: ["Hefei", "Adelaide", "Hefei"]
countries: ["China", "Australia", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chu_RaCFormer_Towards_High-Quality_3D_Object_Detection_via_Query-based_Radar-Camera_Fusion_CVPR_2025_paper.html

abstract:
We propose Radar-Camera fusion transformer (RaCFormer) to boost the accuracy of 3D object detection by the following insight. The Radar-Camera fusion in outdoor 3D scene perception is capped by the image-to-BEV transformation-if the depth of pixels is not accurately estimated, the naive combination of BEV features actually integrates unaligned visual content. To avoid this problem, we propose a query-based framework that enables adaptive sampling of instance-relevant features from both the bird's-eye view (BEV) and the original image view. Furthermore, we enhance system performance by two key designs: optimizing query initialization and strengthening the representational capacity of BEV. For the former, we introduce an adaptive circular distribution in polar coordinates to refine the initialization of object queries, allowing for a distance-based adjustment of query density. For the latter, we initially incorporate a radar-guided depth head to refine the transformation from image view to BEV. Subsequently, we focus on leveraging the Doppler effect of radar and introduce an implicit dynamic catcher to capture the temporal elements within the BEV. Extensive experiments on nuScenes and View-of-Delft (VoD) datasets validate the merits of our design. Remarkably, our method achieves superior results of 64.9% mAP and 70.2% NDS on nuScenes. RaCFormer also secures the state-of-the-art performance on the VoD dataset. Code is available at https://github.com/cxmomo/RaCFormer.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chu_RaCFormer_Towards_High-Quality_3D_Object_Detection_via_Query-based_Radar-Camera_Fusion_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Chu_2025_CVPR,
    author    = {Chu, Xiaomeng and Deng, Jiajun and You, Guoliang and Duan, Yifan and Li, Houqiang and Zhang, Yanyong},
    title     = {RaCFormer: Towards High-Quality 3D Object Detection via Query-based Radar-Camera Fusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17081-17091}
}

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

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


title: Hybrid Reciprocal Transformer with Triplet Feature Alignment for Scene Graph Generation
authors: Jiawei Fu, Tiantian Zhang, Kai Chen, Qi Dou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8953-8963
affiliations: ["Department of Computer Science and Engineering, The Chinese University of Hong Kong"]
cities: ["Hong Kong"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fu_Hybrid_Reciprocal_Transformer_with_Triplet_Feature_Alignment_for_Scene_Graph_CVPR_2025_paper.html

abstract:
Scene graph generation is a pivotal task in computer vision, focusing on comprehensive identification of visual relation tuples embedded within images. The advancement of methods involving triplets has sought to enhance task performance by integrating triplets as contextual features for more precise predicate identification from component level. However, challenges remain due to interference from multi-role objects in overlapping tuples within complex environments, which impairs the model's ability to distinguish and align specific triplet features for reasoning diverse semantics of multi-role objects. To address these issues, we introduce a novel framework that incorporates a triplet alignment model into a hybrid reciprocal transformer architecture, starting from using triplet mask features to guide the learning of component-level relation graphs. To effectively distinguish multi-role objects characterized by overlapping visual relation tuples, we introduce a triplet alignment loss, which provides multi-role objects with aligned features from triplet and helps customize them. Additionally, we explore the inherent connectivity between hybrid aligned triplet and component features through a bidirectional refinement module, which enhances feature interaction and reciprocal reinforcement. Experimental results demonstrate that our model achieves state-of-the-art performance on the Visual Genome and Action Genome datasets, underscoring its effectiveness and adaptability. Project page: hq-sg.github.io.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fu_Hybrid_Reciprocal_Transformer_with_Triplet_Feature_Alignment_for_Scene_Graph_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fu_Hybrid_Reciprocal_Transformer_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fu_2025_CVPR,
    author    = {Fu, Jiawei and Zhang, Tiantian and Chen, Kai and Dou, Qi},
    title     = {Hybrid Reciprocal Transformer with Triplet Feature Alignment for Scene Graph Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8953-8963}
}

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


title: Understanding Multi-Task Activities from Single-Task Videos
authors: Yuhan Shen, Ehsan Elhamifar
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19120-19131
affiliations: ["Northeastern University", "Northeastern University"]
cities: ["Boston", "Boston"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shen_Understanding_Multi-Task_Activities_from_Single-Task_Videos_CVPR_2025_paper.html

abstract:
(MT-TAS), a novel paradigm that addresses the challenges of interleaved actions when performing multiple tasks simultaneously. Traditional action segmentation models, trained on single-task videos, struggle to handle task switches and complex scenes inherent in multi-task scenarios. To overcome these challenges, our MT-TAS approach synthesizes multi-task video data from single-task sources using our Multi-task Sequence Blending and Segment Boundary Learning modules. Additionally, we propose to dynamically isolate foreground and background elements within video frames, addressing the intricacies of object layouts in multi-task scenarios and enabling a new two-stage temporal action segmentation framework with Foreground-Aware Action Refinement. Also, we introduce the Multi-task Egocentric Kitchen Activities (MEKA) dataset, containing 12 hours of egocentric multi-task videos, to rigorously benchmark MT-TAS models. Extensive experiments demonstrate that our framework effectively bridges the gap between single-task training and multi-task testing, advancing temporal action segmentation with state-of-the-art performance in complex environments.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_Understanding_Multi-Task_Activities_from_Single-Task_Videos_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shen_Understanding_Multi-Task_Activities_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shen_2025_CVPR,
    author    = {Shen, Yuhan and Elhamifar, Ehsan},
    title     = {Understanding Multi-Task Activities from Single-Task Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19120-19131}
}

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


title: Co-Speech Gesture Video Generation with Implicit Motion-Audio Entanglement
authors: Xinjie Li, Ziyi Chen, Xinlu Yu, Iek-Heng Chu, Peng Chang, Jing Xiao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11384-11394
affiliations: ["Pennsylvania State University, USA", "PAII Inc., USA", "PingAn Technology, China"]
cities: ["State College", "USA", "Shenzhen"]
countries: ["USA", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Co-Speech_Gesture_Video_Generation_with_Implicit_Motion-Audio_Entanglement_CVPR_2025_paper.html

abstract:
Co-speech gestures are essential to non-verbal communication, enhancing both the naturalness and effectiveness of human interaction. Although recent methods have made progress in generating co-speech gesture videos, many rely on strong visual controls, such as pose images or TPS keypoint movements, which often lead to artifacts like blurry hands and distorted fingers. In response to these challenges, we present the Implicit Motion-Audio Entanglement (IMAE) method for co-speech gesture video generation. IMAE strengthens audio control by entangling implicit motion parameters, including pose and expression, with audio inputs. Our method utilizes a two-branch framework that combines an audio-to-motion generation branch with a video diffusion branch, enabling realistic gesture generation without requiring additional inputs during inference. To improve training efficiency, we propose a two-stage slow-fast training strategy that balances memory constraints while facilitating the learning of meaningful gestures from long frame sequences.Extensive experimental results demonstrate that our method achieves state-of-the-art performance across multiple metrics.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Co-Speech_Gesture_Video_Generation_with_Implicit_Motion-Audio_Entanglement_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Co-Speech_Gesture_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Xinjie and Chen, Ziyi and Yu, Xinlu and Chu, Iek-Heng and Chang, Peng and Xiao, Jing},
    title     = {Co-Speech Gesture Video Generation with Implicit Motion-Audio Entanglement},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11384-11394}
}

raw_affiliation:
[{"affiliation": "Pennsylvania State University, USA", "city": "State College", "country": "USA"}, {"affiliation": "PAII Inc., USA", "city": "USA", "country": "USA"}, {"affiliation": "PingAn Technology, China", "city": "Shenzhen", "country": "China"}]


title: TransPixeler: Advancing Text-to-Video Generation with Transparency
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18229-18239
affiliations: ["HKUST(GZ)", "HKUST", "Adobe Research"]
cities: ["Guangzhou", "Hong Kong", "San Jose"]
countries: ["China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_TransPixeler_Advancing_Text-to-Video_Generation_with_Transparency_CVPR_2025_paper.html

authors:
Luozhou Wang, Yijun Li, Zhifei Chen, Jui-Hsien Wang, Zhifei Zhang, He Zhang, Zhe Lin, Ying-Cong Chen

abstract:
Text-to-video generative models have made significant strides, enabling diverse applications in entertainment, advertising, and education. However, generating RGBA video, which includes alpha channels for transparency, remains a challenge due to limited datasets and the difficulty of adapting existing models. Alpha channels are crucial for visual effects (VFX), allowing transparent elements like smoke and reflections to blend seamlessly into scenes.We introduce TransPixeler, a method to extend pretrained video models for RGBA generation while retaining the original RGB capabilities. TransPixar leverages a diffusion transformer (DiT) architecture, incorporating alpha-specific tokens and using LoRA-based fine-tuning to jointly generate RGB and alpha channels with high consistency. By optimizing attention mechanisms, TransPixeler preserves the strengths of the original RGB model and achieves strong alignment between RGB and alpha channels despite limited training data.Our approach effectively generates diverse and consistent RGBA videos, advancing the possibilities for VFX and interactive content creation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_TransPixeler_Advancing_Text-to-Video_Generation_with_Transparency_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Luozhou and Li, Yijun and Chen, Zhifei and Wang, Jui-Hsien and Zhang, Zhifei and Zhang, He and Lin, Zhe and Chen, Ying-Cong},
    title     = {TransPixeler: Advancing Text-to-Video Generation with Transparency},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18229-18239}
}

raw_affiliation:
[{"affiliation": "HKUST(GZ)", "city": "Guangzhou", "country": "China"}, {"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}]


title: Adaptive Keyframe Sampling for Long Video Understanding
authors: Xi Tang, Jihao Qiu, Lingxi Xie, Yunjie Tian, Jianbin Jiao, Qixiang Ye
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29118-29128
affiliations: ["University of Chinese Academy of Sciences", "University at Buffalo, SUNY"]
cities: ["Beijing", "Buffalo"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_Adaptive_Keyframe_Sampling_for_Long_Video_Understanding_CVPR_2025_paper.html

abstract:
Multimodal large language models (MLLMs) have enabled open-world visual understanding by injecting visual input as extra tokens into large language models (LLMs) as contexts. However, when the visual input changes from a single image to a long video, the above paradigm encounters difficulty because the vast amount of video tokens has significantly exceeded the maximal capacity of MLLMs. Therefore, existing video-based MLLMs are mostly established upon sampling a small portion of tokens from input data, which can cause key information to be lost and thus produce incorrect answers. This paper presents a simple yet effective algorithm named Adaptive Keyframe Sampling (AKS). It inserts a plug-and-play module known as keyframe selection, which aims to maximize the useful information with a fixed number of video tokens. We formulate keyframe selection as an optimization involving (1) the relevance between the keyframes and the prompt, and (2) the coverage of the keyframes over the video, and present an adaptive algorithm to approximate the best solution. Experiments on two long video understanding benchmarks validate that AKS improves video QA accuracy (beyond strong baselines) upon selecting informative keyframes. Our study reveals the importance of information pre-filtering in video-based MLLMs. Our code are available at https://github.com/ncTimTang/AKS

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_Adaptive_Keyframe_Sampling_for_Long_Video_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tang_Adaptive_Keyframe_Sampling_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Xi and Qiu, Jihao and Xie, Lingxi and Tian, Yunjie and Jiao, Jianbin and Ye, Qixiang},
    title     = {Adaptive Keyframe Sampling for Long Video Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29118-29128}
}

raw_affiliation:
[{"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "University at Buffalo, SUNY", "city": "Buffalo", "country": "USA"}]


title: What's in the Image? A Deep-Dive into the Vision of Vision Language Models
authors: Omri Kaduri, Shai Bagon, Tali Dekel
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14549-14558
raw_affiliation: [{"affiliation": "Weizmann Institute of Science", "city": "Rehovot", "country": "Israel"}]
affiliations: ["Weizmann Institute of Science"]
cities: ["Rehovot"]
countries: ["Israel"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kaduri_Whats_in_the_Image_A_Deep-Dive_into_the_Vision_of_CVPR_2025_paper.html

abstract:
Vision-Language Models (VLMs) have recently demonstrated remarkable capabilities in comprehending complex visual content. However, the mechanisms underlying how VLMs process visual information remain largely unexplored. In this paper,  we conduct a thorough empirical analysis, focusing on the attention modules across layers, by which we reveal several key insights about how these models process visual data: (i) the internal representation of the query tokens (e.g., representations of "describe the image"), is utilized by the model to store global image information; we demonstrate that the model generates surprisingly descriptive responses solely from these tokens, without direct access to image tokens.  (ii) Cross-modal information flow is predominantly influenced by the middle layers (approximately 25% of all layers), while early and late layers contribute only marginally. (iii) Fine-grained visual attributes and object details are directly extracted from image tokens in a spatially localized manner, i.e., the generated tokens associated with a specific object or attribute attend strongly to their corresponding regions in the image.  We propose novel quantitative evaluation to validate our observations, leveraging real-world complex visual scenes. Finally, we demonstrate the potential of our findings in facilitating efficient visual processing in state-of-the-art VLMs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kaduri_Whats_in_the_Image_A_Deep-Dive_into_the_Vision_of_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kaduri_Whats_in_the_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kaduri_2025_CVPR,
    author    = {Kaduri, Omri and Bagon, Shai and Dekel, Tali},
    title     = {What's in the Image? A Deep-Dive into the Vision of Vision Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14549-14558}
}


title: Person De-reidentification: A Variation-guided Identity Shift Modeling
authors: Yi-Xing Peng, Yu-Ming Tang, Kun-Yu Lin, Qize Yang, Jingke Meng, Xihan Wei, Wei-Shi Zheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29331-29341
cities: ["Guangzhou", "Shenzhen", "Hangzhou", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Peng_Person_De-reidentification_A_Variation-guided_Identity_Shift_Modeling_CVPR_2025_paper.html

abstract:
Person re-identification (ReID) is to associate images of individuals from different camera views against cross-view variations. Like other surveillance technologies, Re-ID faces serious privacy challenges, particularly the potential for unauthorized tracking. Although various tasks (e.g., face recognition) have developed machine unlearning techniques to address privacy concerns, such methods have not yet been explored within the Re-ID field. In this work, we pioneer the exploration of the person de-reidentification (De-ReID) problem and present its inherent challenges. In the context of ReID, De-ReID is to unlearn the knowledge about accurately matching specific persons so that these "unlearned persons" cannot be re-identified across cameras for privacy guarantee. The primary challenge is to achieve the unlearning without degrading the identity-discriminative feature embeddings to ensure the model's utility. To address this, we formulate a De-ReID framework that utilizes a labeled dataset of unlearned persons for unlearning and an unlabeled dataset of accessible persons for knowledge preservation. Instead of unlearning based on (pseudo) identity labels, we introduce a variation-guided identity shift mechanism that unlearns the specific persons by fitting the variations in their images while preserving ReID ability on other persons by overcoming the variations in images of accessible persons. As a result, the model shifts the unlearned persons to a feature space that is vulnerable to cross-view variations. Extensive experiments on benchmarks demonstrate the superiority of our method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Peng_Person_De-reidentification_A_Variation-guided_Identity_Shift_Modeling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Peng_Person_De-reidentification_A_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Peng_2025_CVPR,
    author    = {Peng, Yi-Xing and Tang, Yu-Ming and Lin, Kun-Yu and Yang, Qize and Meng, Jingke and Wei, Xihan and Zheng, Wei-Shi},
    title     = {Person De-reidentification: A Variation-guided Identity Shift Modeling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29331-29341}
}

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

affiliations:
["School of Computer Science and Engineering, Sun Yat-sen University, China", "Peng Cheng Laboratory, Shenzhen, China", "Tongyi Lab, Alibaba Group", "Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China"]


title: FreeSim: Toward Free-viewpoint Camera Simulation in Driving Scenes
authors: Lue Fan, Hao Zhang, Qitai Wang, Hongsheng Li, Zhaoxiang Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12004-12014
affiliations: ["NLPR & MAIS, CASIA", "MMLab, CUHK", "CPII under InnoHK"]
cities: ["Beijing", "Hong Kong", "Hong Kong"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fan_FreeSim_Toward_Free-viewpoint_Camera_Simulation_in_Driving_Scenes_CVPR_2025_paper.html

abstract:
We propose FreeSim, a camera simulation method for driving scenes via 3D Gaussian Splatting and diffusion-based image generation. FreeSim emphasizes high-quality rendering from viewpoints beyond the recorded ego trajectories. In such viewpoints, previous methods have unacceptable degradation because the training data of these viewpoints is unavailable. To address such data scarcity, we first propose a generative enhancement model with a matched data construction strategy. The resulting model can generate high-quality images in a viewpoint slightly deviated from the recorded trajectories,  conditioned on the degraded rendering of this viewpoint. We then propose a progressive reconstruction strategy, which progressively adds generated images in unrecorded views into the reconstruction process, starting from slightly off-trajectory viewpoints and moving progressively farther away.  With this progressive generation-reconstruction pipeline, FreeSim supports high-quality off-trajectory view synthesis under large deviations of more than 3 meters.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fan_FreeSim_Toward_Free-viewpoint_Camera_Simulation_in_Driving_Scenes_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Fan_2025_CVPR,
    author    = {Fan, Lue and Zhang, Hao and Wang, Qitai and Li, Hongsheng and Zhang, Zhaoxiang},
    title     = {FreeSim: Toward Free-viewpoint Camera Simulation in Driving Scenes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12004-12014}
}

raw_affiliation:
[{"affiliation": "NLPR & MAIS, CASIA", "city": "Beijing", "country": "China"}, {"affiliation": "MMLab, CUHK", "city": "Hong Kong", "country": "China"}, {"affiliation": "CPII under InnoHK", "city": "Hong Kong", "country": "China"}]


title: Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning
authors: Hasin Us Sami, Swapneel Sen, Amit K. Roy-Chowdhury, Srikanth V. Krishnamurthy, Basak Guler
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10224-10234
raw_affiliation: [{"affiliation": "University of California, Riverside, CA", "city": "Riverside", "country": "USA"}]
affiliations: ["University of California, Riverside, CA"]
cities: ["Riverside"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sami_Gradient_Inversion_Attacks_on_Parameter-Efficient_Fine-Tuning_CVPR_2025_paper.html

abstract:
Federated learning (FL) allows multiple data-owners to collaboratively train machine learning models by exchanging local gradients, while keeping their private data on-device. To simultaneously enhance privacy and training efficiency, recently parameter-efficient fine-tuning (PEFT) of large-scale pretrained models has gained substantial attention in FL. While keeping a  pretrained (backbone) model frozen, each user fine-tunes only a few lightweight modules to be used in conjunction, to fit specific downstream applications. Accordingly, only the gradients with respect to these lightweight modules are shared with the server. In this work, we investigate how the privacy of the fine-tuning data of the users can be compromised via a malicious design of the pretrained model and trainable adapter modules. We demonstrate gradient inversion attacks on a popular PEFT mechanism, the adapter, which allow an attacker to reconstruct local data samples of a target user, using only the accessible adapter gradients. Via extensive experiments, we demonstrate that a large batch of fine-tuning images can be retrieved with high fidelity. Our attack highlights the need for privacy-preserving mechanisms for PEFT, while opening up several future directions. Our code is available at https://github.com/info-ucr/PEFTLeak.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sami_Gradient_Inversion_Attacks_on_Parameter-Efficient_Fine-Tuning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sami_Gradient_Inversion_Attacks_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sami_2025_CVPR,
    author    = {Sami, Hasin Us and Sen, Swapneel and Roy-Chowdhury, Amit K. and Krishnamurthy, Srikanth V. and Guler, Basak},
    title     = {Gradient Inversion Attacks on Parameter-Efficient Fine-Tuning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10224-10234}
}


title: UPME: An Unsupervised Peer Review Framework for Multimodal Large Language Model Evaluation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9165-9174
cities: ["Beijing", "Hangzhou", "Hangzhou", "Notre Dame", "Beijing"]
countries: ["China", "China", "China", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_UPME_An_Unsupervised_Peer_Review_Framework_for_Multimodal_Large_Language_CVPR_2025_paper.html

authors:
Qihui Zhang, Munan Ning, Zheyuan Liu, Yue Huang, Shuo Yang, Yanbo Wang, Jiayi Ye, Xiao Chen, Yibing Song, Li Yuan

abstract:
Multimodal Large Language Models (MLLMs) have emerged to tackle the challenges of Visual Question Answering (VQA), sparking a new research focus on conducting objective evaluations of these models. Existing evaluation mechanisms face limitations due to the significant human workload required to design Q&A pairs for visual images, which inherently restricts the scale and scope of evaluations. Although automated MLLM-as-judge approaches attempt to reduce the human workload through mutual model evaluations, they often introduce biases. To address these problems, we propose an unsupervised evaluation method -- an Unsupervised Peer review MLLM Evaluation framework. This framework utilizes only image data, allowing models to automatically generate questions and conduct peer review assessments of answers from other models, effectively alleviating the reliance on human workload. Additionally, we introduce the vision-language scoring system to mitigate the bias issues, which focuses on three aspects: (i) response correctness; (ii) the model capability of visual understanding and reasoning; (iii) relevance of text-image matching. Experimental results demonstrate that UPME achieves a Pearson correlation of 0.944 with human evaluations on the MMstar dataset and 0.814 on the ScienceQA dataset, indicating that our UPME framework closely aligns with human-designed QA benchmarks and inherent human preferences.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_UPME_An_Unsupervised_Peer_Review_Framework_for_Multimodal_Large_Language_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_UPME_An_Unsupervised_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Qihui and Ning, Munan and Liu, Zheyuan and Huang, Yue and Yang, Shuo and Wang, Yanbo and Ye, Jiayi and Chen, Xiao and Song, Yibing and Yuan, Li},
    title     = {UPME: An Unsupervised Peer Review Framework for Multimodal Large Language Model Evaluation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9165-9174}
}

raw_affiliation:
[{"affiliation": "School of Electrical and Computer Engineering, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Hupan Lab", "city": "Hangzhou", "country": "China"}, {"affiliation": "DAMO Academy, Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "University of Notre Dame", "city": "Notre Dame", "country": "USA"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["School of Electrical and Computer Engineering, Peking University", "Hupan Lab", "DAMO Academy, Alibaba Group", "University of Notre Dame", "Tsinghua University"]


authors: Ho-Joong Kim, Yearang Lee, Jung-Ho Hong, Seong-Whan Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24286-24296
affiliations: ["Dept. of Arti\ufb01cial Intelligence, Korea University"]
cities: ["Seoul"]
countries: ["Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_DiGIT_Multi-Dilated_Gated_Encoder_and_Central-Adjacent_Region_Integrated_Decoder_for_CVPR_2025_paper.html

title:
DiGIT: Multi-Dilated Gated Encoder and Central-Adjacent Region Integrated Decoder for Temporal Action Detection Transformer

abstract:
In this paper, we examine a key limitation in query-based detectors for temporal action detection (TAD), which arises from their direct adaptation of originally designed architectures for object detection. Despite the effectiveness of the existing models, they struggle to fully address the unique challenges of TAD, such as the redundancy in multi-scale features and the limited ability to capture sufficient temporal context. To address these issues, we propose a multi-dilated gated encoder and central-adjacent region integrated decoder for temporal action detection transformer (DiGIT). Our approach replaces the existing encoder that consists of multi-scale deformable attention and feedforward network with our multi-dilated gated encoder. Our proposed encoder reduces the redundant information caused by multi-level features while maintaining the ability to capture fine-grained and long-range temporal information. Furthermore, we introduce a central-adjacent region integrated decoder that leverages a more comprehensive sampling strategy for deformable cross-attention to capture the essential information. Extensive experiments demonstrate that DiGIT achieves state-of-the-art performance on THUMOS14, ActivityNet v1.3, and HACS-Segment.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_DiGIT_Multi-Dilated_Gated_Encoder_and_Central-Adjacent_Region_Integrated_Decoder_for_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_DiGIT_Multi-Dilated_Gated_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Ho-Joong and Lee, Yearang and Hong, Jung-Ho and Lee, Seong-Whan},
    title     = {DiGIT: Multi-Dilated Gated Encoder and Central-Adjacent Region Integrated Decoder for Temporal Action Detection Transformer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24286-24296}
}

raw_affiliation:
[{"affiliation": "Dept. of Arti\ufb01cial Intelligence, Korea University", "city": "Seoul", "country": "Korea"}]


title: MBQ: Modality-Balanced Quantization for Large Vision-Language Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4167-4177
cities: ["Beijing", "Beijing", "Beijing", "Qingdao", "Hong Kong", "Beijing", "Shanghai"]
countries: ["China", "China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_MBQ_Modality-Balanced_Quantization_for_Large_Vision-Language_Models_CVPR_2025_paper.html

authors:
Shiyao Li, Yingchun Hu, Xuefei Ning, Xihui Liu, Ke Hong, Xiaotao Jia, Xiuhong Li, Yaqi Yan, Pei Ran, Guohao Dai, Shengen Yan, Huazhong Yang, Yu Wang

abstract:
Vision-Language Models (VLMs) have already enabled a variety of real-world applications. The large parameter size of VLMs brings large memory and computation overhead which poses significant challenges for deployment. Post-Training Quantization (PTQ) is an effective technique to reduce the memory and computation overhead. Existing PTQ methods mainly focus on the language modality in large language models (LLMs), without considering the differences across other modalities. In this paper, we discover that there is a significant difference in sensitivity between language and vision tokens in large VLMs. Therefore, treating tokens from different modalities equally, as in existing PTQ methods, may over-emphasize the insensitive modalities, leading to significant accuracy loss. To deal with the above issue, we propose a simple yet effective method, Modality-Balanced Quantization (MBQ), for large VLMs. Specifically, MBQ incorporates the different sensitivities across modalities during the calibration process to minimize the reconstruction loss for better quantization parameters. Extensive experiments show that MBQ can significantly improve task accuracy by up to 4.4% and 11.6% under W3A16 and W4A8 quantization for 7B to 70B VLMs, compared to SOTA baselines. Additionally, we implement a W3A16 GPU kernel that fuses the dequantization and GEMV operators, achieving a 1.4x speedup on LLaVA-onevision-7B on the RTX 4090. We will release the code.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_MBQ_Modality-Balanced_Quantization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Shiyao and Hu, Yingchun and Ning, Xuefei and Liu, Xihui and Hong, Ke and Jia, Xiaotao and Li, Xiuhong and Yan, Yaqi and Ran, Pei and Dai, Guohao and Yan, Shengen and Yang, Huazhong and Wang, Yu},
    title     = {MBQ: Modality-Balanced Quantization for Large Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4167-4177}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "In\ufb01nigence-AI", "city": "Beijing", "country": "China"}, {"affiliation": "Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "Qingdao Research Institute, Beihang University", "city": "Qingdao", "country": "China"}, {"affiliation": "University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Chinatower", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]

affiliations:
["Tsinghua University", "In\ufb01nigence-AI", "Beihang University", "Qingdao Research Institute, Beihang University", "University of Hong Kong", "Chinatower", "Shanghai Jiao Tong University"]


authors: Jiuhai Chen, Jianwei Yang, Haiping Wu, Dianqi Li, Jianfeng Gao, Tianyi Zhou, Bin Xiao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24928-24938
affiliations: ["University of Maryland", "Microsoft Research"]
cities: ["College Park", "Redmond"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Florence-VL_Enhancing_Vision-Language_Models_with_Generative_Vision_Encoder_and_Depth-Breadth_CVPR_2025_paper.html

title:
Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion

abstract:
We present Florence-VL, a new family of multimodal large language models (MLLMs) with enriched visual representations produced by Florence-2, a generative vision foundation model. Unlike the widely used CLIP-style vision transformer trained by contrastive learning, Florence-2 can capture different levels and aspects of visual features, which are more versatile to be adapted to diverse downstream tasks. We propose a novel feature-fusion architecture and an innovative training recipe that effectively integrates Florence-2's visual features into pretrained LLMs, such as Phi 3.5 and LLama 3. In particular, we propose "depth-breath fusion (DBFusion)" to fuse the visual features extracted from different depths and under multiple prompts. Our model training is composed of end-to-end pretraining of the whole model followed by finetuning of the projection layer and the LLM, on a carefully designed recipe of diverse open-source datasets that include high-quality image captions and instruction-tuning pairs. Our quantitative analysis and visualization of Florence-VL's visual features show its advantages over popular vision encoders on vision-language alignment, where the enriched depth and breath play important roles. Florence-VL achieves significant improvements over existing state-of-the-art MLLMs across various multi-modal and vision-centric benchmarks covering general VQA, perception, hallucination, OCR, Chart, knowledge-intensive understanding, etc. To facilitate future research, our models and the complete training recipe are open-sourced.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Florence-VL_Enhancing_Vision-Language_Models_with_Generative_Vision_Encoder_and_Depth-Breadth_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Jiuhai and Yang, Jianwei and Wu, Haiping and Li, Dianqi and Gao, Jianfeng and Zhou, Tianyi and Xiao, Bin},
    title     = {Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24928-24938}
}

raw_affiliation:
[{"affiliation": "University of Maryland", "city": "College Park", "country": "USA"}, {"affiliation": "Microsoft Research", "city": "Redmond", "country": "USA"}]


title: VideoDPO: Omni-Preference Alignment for Video Diffusion Generation
authors: Runtao Liu, Haoyu Wu, Ziqiang Zheng, Chen Wei, Yingqing He, Renjie Pi, Qifeng Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8009-8019
affiliations: ["HKUST", "Renmin University of China", "Johns Hopkins University"]
cities: ["Hong Kong", "Beijing", "Baltimore"]
countries: ["China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_VideoDPO_Omni-Preference_Alignment_for_Video_Diffusion_Generation_CVPR_2025_paper.html

abstract:
Recent progress in generative diffusion models has greatly advanced text-to-video generation. While text-to-video models trained on large-scale, diverse datasets can produce varied outputs, these generations often deviate from user preferences, highlighting the need for preference alignment on pre-trained models. Although Direct Preference Optimization (DPO) has demonstrated significant improvements in language and image generation, we pioneer its adaptation to video diffusion models and propose a VideoDPO pipeline by making several key adjustments. Unlike previous image alignment methods that focus solely on either (i) visual quality or (ii) semantic alignment between text and videos, we comprehensively consider both dimensions and construct a preference score accordingly, which we term the OmniScore. We design a pipeline to automatically collect preference pair data based on the proposed OmniScore and discover that re-weighting these pairs based on the score significantly impacts overall preference alignment. Our experiments demonstrate substantial improvements in both visual quality and semantic alignment, ensuring that no preference aspect is neglected. Code and data will be shared at https://videodpo.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_VideoDPO_Omni-Preference_Alignment_for_Video_Diffusion_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_VideoDPO_Omni-Preference_Alignment_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Runtao and Wu, Haoyu and Zheng, Ziqiang and Wei, Chen and He, Yingqing and Pi, Renjie and Chen, Qifeng},
    title     = {VideoDPO: Omni-Preference Alignment for Video Diffusion Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8009-8019}
}

raw_affiliation:
[{"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}, {"affiliation": "Renmin University of China", "city": "Beijing", "country": "China"}, {"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}]


title: Seq2Time: Sequential Knowledge Transfer for Video LLM Temporal Grounding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13766-13775
affiliations: ["University of Central Florida", "United Imaging Intelligence", "Northwestern University"]
cities: ["Orlando", "Shanghai", "Evanston"]
countries: ["USA", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Deng_Seq2Time_Sequential_Knowledge_Transfer_for_Video_LLM_Temporal_Grounding_CVPR_2025_paper.html

authors:
Andong Deng, Zhongpai Gao, Anwesa Choudhuri, Benjamin Planche, Meng Zheng, Bin Wang, Terrence Chen, Chen Chen, Ziyan Wu

abstract:
Temporal awareness is essential for video large language models (LLMs) to understand and reason about events within long videos, enabling applications like dense video captioning and temporal video grounding in a unified system. However, the scarcity of long videos with detailed captions and precise temporal annotations limits their temporal awareness. In this paper, we propose Seq2Time, a data-oriented training paradigm that leverages sequences of images and short video clips to enhance temporal awareness in long videos. By converting sequence positions into temporal annotations, we transform large-scale image and clip captioning datasets into sequences that mimic the temporal structure of long videos, enabling self-supervised training with abundant time-sensitive data. To enable sequence-to-time knowledge transfer, we introduce a novel time representation that unifies positional information across image sequences, clip sequences, and long videos. Experiments demonstrate the effectiveness of our method, achieving a 27.6% improvement in F1 score and 44.8% in CIDEr on the YouCook2 benchmark and a 14.7% increase in recall on the Charades-STA benchmark compared to the baseline.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Deng_Seq2Time_Sequential_Knowledge_Transfer_for_Video_LLM_Temporal_Grounding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Deng_Seq2Time_Sequential_Knowledge_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Deng_2025_CVPR,
    author    = {Deng, Andong and Gao, Zhongpai and Choudhuri, Anwesa and Planche, Benjamin and Zheng, Meng and Wang, Bin and Chen, Terrence and Chen, Chen and Wu, Ziyan},
    title     = {Seq2Time: Sequential Knowledge Transfer for Video LLM Temporal Grounding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13766-13775}
}

raw_affiliation:
[{"affiliation": "University of Central Florida", "city": "Orlando", "country": "USA"}, {"affiliation": "United Imaging Intelligence", "city": "Shanghai", "country": "China"}, {"affiliation": "Northwestern University", "city": "Evanston", "country": "USA"}]


authors: Yunxuan Li, Lei Fan, Xiaoying Xing, Jianxiong Zhou, Ying Wu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16728-16738
raw_affiliation: [{"affiliation": "Northwestern University", "city": "Evanston", "country": "USA"}]
affiliations: ["Northwestern University"]
cities: ["Evanston"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_GPVK-VL_Geometry-Preserving_Virtual_Keyframes_for_Visual_Localization_under_Large_Viewpoint_CVPR_2025_paper.html

title:
GPVK-VL: Geometry-Preserving Virtual Keyframes for Visual Localization under Large Viewpoint Changes

abstract:
Visual localization, the task of determining the position and orientation of a camera, typically involves three core components: offline construction of a keyframe database, efficient online keyframes retrieval, and robust local feature matching. However, significant challenges arise when there are large viewpoint disparities between the query view and the database, such as attempting localization in a corridor previously build from an opposing direction. Intuitively, this issue can be addressed by synthesizing a set of virtual keyframes that cover all viewpoints. However, existing methods for synthesizing novel views to assist localization often fail to ensure geometric accuracy under large viewpoint changes. In this paper, we introduce a confidence-aware geometric prior into 2D Gaussian splatting to ensure the geometric accuracy of the scene. Then we can render novel views through the mesh with clear structures and accurate geometry, even under significant viewpoint changes, enabling the synthesis of a comprehensive set of virtual keyframes. Incorporating this geometry-preserving virtual keyframe database into the localization pipeline significantly enhances the robustness of visual localization.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_GPVK-VL_Geometry-Preserving_Virtual_Keyframes_for_Visual_Localization_under_Large_Viewpoint_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_GPVK-VL_Geometry-Preserving_Virtual_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Yunxuan and Fan, Lei and Xing, Xiaoying and Zhou, Jianxiong and Wu, Ying},
    title     = {GPVK-VL: Geometry-Preserving Virtual Keyframes for Visual Localization under Large Viewpoint Changes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16728-16738}
}


title: Realistic Test-Time Adaptation of Vision-Language Models
authors: Maxime Zanella, Clément Fuchs, Christophe De Vleeschouwer, Ismail Ben Ayed
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25103-25112
affiliations: ["UCLouvain", "UMons", "\u00b4ETS Montreal"]
cities: ["Louvain-la-Neuve", "Mons", "Montreal"]
countries: ["Belgium", "Belgium", "Canada"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zanella_Realistic_Test-Time_Adaptation_of_Vision-Language_Models_CVPR_2025_paper.html

abstract:
The zero-shot capabilities of Vision-Language Models (VLMs) have been widely leveraged to improve predictive performance. However, previous works on transductive or test-time adaptation (TTA) often make strong assumptions about the data distribution, such as the presence of all classes. Our work challenges these favorable deployment scenarios and introduces a more realistic evaluation framework, including (i) a variable number of effective classes for adaptation within a single batch, and (ii) non-i.i.d. batches of test samples in online adaptation settings. We provide comprehensive evaluations, comparisons, and ablation studies that demonstrate how current transductive or TTA methods for VLMs systematically compromise the models' initial zero-shot robustness across various realistic scenarios, favoring performance gains under advantageous assumptions about the test sample distributions. Furthermore, we introduce StatA, a versatile method that can handle a wide range of deployment scenarios, including those with a variable number of effective classes at test time. Our approach incorporates a novel regularization term designed specifically for VLMs, which acts as a statistical anchor preserving the initial text-encoder knowledge, particularly in low-data regimes. Code available at https://github.com/MaxZanella/StatA.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zanella_Realistic_Test-Time_Adaptation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zanella_2025_CVPR,
    author    = {Zanella, Maxime and Fuchs, Cl\'ement and De Vleeschouwer, Christophe and Ben Ayed, Ismail},
    title     = {Realistic Test-Time Adaptation of Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25103-25112}
}

raw_affiliation:
[{"affiliation": "UCLouvain", "city": "Louvain-la-Neuve", "country": "Belgium"}, {"affiliation": "UMons", "city": "Mons", "country": "Belgium"}, {"affiliation": "\u00b4ETS Montreal", "city": "Montreal", "country": "Canada"}]


title: SelfSplat: Pose-Free and 3D Prior-Free Generalizable 3D Gaussian Splatting
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22012-22022
cities: ["Suwon", "Suwon", "Seoul", "Seoul", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kang_SelfSplat_Pose-Free_and_3D_Prior-Free_Generalizable_3D_Gaussian_Splatting_CVPR_2025_paper.html

authors:
Gyeongjin Kang, Jisang Yoo, Jihyeon Park, Seungtae Nam, Hyeonsoo Im, Sangheon Shin, Sangpil Kim, Eunbyung Park

abstract:
We propose SelfSplat, a novel 3D Gaussian Splatting model designed to perform pose-free and 3D prior-free generalizable 3D reconstruction from unposed multi-view images. These settings are inherently ill-posed due to the lack of ground-truth data, learned geometric information, and the need to achieve accurate 3D reconstruction without finetuning, making it difficult for conventional methods to achieve high-quality results. Our model addresses these challenges by effectively integrating explicit 3D representations with self-supervised depth and pose estimation techniques, resulting in reciprocal improvements in both pose accuracy and 3D reconstruction quality. Furthermore, we incorporate a matching-aware pose estimation network and a depth refinement module to enhance geometry consistency across views, ensuring more accurate and stable 3D reconstructions. To present the performance of our method, we evaluated it on large-scale real-world datasets, including RealEstate10K, ACID, and DL3DV. SelfSplat achieves superior results over previous state-of-the-art methods in both appearance and geometry quality, also demonstrates strong cross-dataset generalization capabilities. Extensive ablation studies and analysis also validate the effectiveness of our proposed methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kang_SelfSplat_Pose-Free_and_3D_Prior-Free_Generalizable_3D_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kang_SelfSplat_Pose-Free_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kang_2025_CVPR,
    author    = {Kang, Gyeongjin and Yoo, Jisang and Park, Jihyeon and Nam, Seungtae and Im, Hyeonsoo and Shin, Sangheon and Kim, Sangpil and Park, Eunbyung},
    title     = {SelfSplat: Pose-Free and 3D Prior-Free Generalizable 3D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22012-22022}
}

raw_affiliation:
[{"affiliation": "Department of Electrical and Computer Engineering, Sungkyunkwan University1", "city": "Suwon", "country": "South Korea"}, {"affiliation": "Department of Intelligent Robotics, Sungkyunkwan University2", "city": "Suwon", "country": "South Korea"}, {"affiliation": "Department of Artificial Intelligence, Yonsei University3", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Hanhwa Systems4", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Korea University5", "city": "Seoul", "country": "South Korea"}]

affiliations:
["Department of Electrical and Computer Engineering, Sungkyunkwan University1", "Department of Intelligent Robotics, Sungkyunkwan University2", "Department of Artificial Intelligence, Yonsei University3", "Hanhwa Systems4", "Korea University5"]


title: Enhancing Virtual Try-On with Synthetic Pairs and Error-Aware Noise Scheduling
authors: Nannan Li, Kevin J. Shih, Bryan A. Plummer
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21238-21247
affiliations: ["Boston University", "NVIDIA", "Boston University"]
cities: ["Boston", "Santa Clara", "Boston"]
countries: ["USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Enhancing_Virtual_Try-On_with_Synthetic_Pairs_and_Error-Aware_Noise_Scheduling_CVPR_2025_paper.html

abstract:
Given an isolated garment image in a canonical product view and a separate image of a person, the virtual try-on task aims to generate a new image of the person wearing the target garment.Prior virtual try-on works face two major challenges in achieving this goal: a) the paired (human, garment) training data has limited availability; b) generating textures on the human that perfectly match that of the prompted garment is difficult, often resulting in distorted text and faded textures. Our work explores ways to tackle these issues through both synthetic data as well as model refinement. We introduce a garment extraction model that generates (human, synthetic garment) pairs from a single image of a clothed individual. The synthetic pairs can then be used to augment the training of virtual try-on. We also propose an Error-Aware Refinement-based Schrodinger Bridge (EARSB) that surgically targets localized generation errors for correcting the output of a base virtual try-on model. To identify likely errors, we propose a weakly-supervised error classifier that localizes regions for refinement, subsequently augmenting the Schrodinger Bridge's noise schedule with its confidence heatmap. Experiments on VITON-HD and DressCode-Upper demonstrate that our synthetic data augmentation enhances the performance of prior work, while EARSB improves the overall image quality. In user studies, our model is preferred by the users in an average of 59% of cases.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Enhancing_Virtual_Try-On_with_Synthetic_Pairs_and_Error-Aware_Noise_Scheduling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Enhancing_Virtual_Try-On_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Nannan and Shih, Kevin J. and Plummer, Bryan A.},
    title     = {Enhancing Virtual Try-On with Synthetic Pairs and Error-Aware Noise Scheduling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21238-21247}
}

raw_affiliation:
[{"affiliation": "Boston University", "city": "Boston", "country": "USA"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "Boston University", "city": "Boston", "country": "USA"}]


title: Exploring Simple Open-Vocabulary Semantic Segmentation
authors: Zihang Lai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30221-30230
affiliations: ["Visual Geometry Group (VGG), University of Oxford"]
cities: ["Oxford"]
countries: ["UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lai_Exploring_Simple_Open-Vocabulary_Semantic_Segmentation_CVPR_2025_paper.html

abstract:
Open-vocabulary semantic segmentation models aim to accurately assign a semantic label to each pixel in an image from a set of arbitrary open-vocabulary texts. In order to learn such pixel-level alignment, current approaches typically rely on a combination of (i) image-level VL model (e.g. CLIP), (ii) ground truth masks, (iii) custom grouping encoders, and (iv) the Segment Anything Model (SAM). In this paper, we introduce S-Seg, a simple model that can achieve surprisingly strong performance without depending on any of the above elements. S-Seg leverages pseudo-mask and language to train a MaskFormer, and can be easily trained from publicly available image-text datasets. Contrary to prior works, our model directly trains for pixel-level features and language alignment. Once trained, S-Seg generalizes well to multiple testing datasets without requiring fine-tuning. In addition, S-Seg has the extra benefits of scalability with data and consistently improving when augmented with self-training. We believe that our simple yet effective approach will serve as a solid baseline for future research. Our code and demo will be made publicly available soon.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lai_Exploring_Simple_Open-Vocabulary_Semantic_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lai_Exploring_Simple_Open-Vocabulary_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lai_2025_CVPR,
    author    = {Lai, Zihang},
    title     = {Exploring Simple Open-Vocabulary Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30221-30230}
}

raw_affiliation:
[{"affiliation": "Visual Geometry Group (VGG), University of Oxford", "city": "Oxford", "country": "UK"}]


title: MP-GUI: Modality Perception with MLLMs for GUI Understanding
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29711-29721
cities: ["Hangzhou", "Hangzhou", "Hangzhou"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_MP-GUI_Modality_Perception_with_MLLMs_for_GUI_Understanding_CVPR_2025_paper.html

authors:
Ziwei Wang, Weizhi Chen, Leyang Yang, Sheng Zhou, Shengchu Zhao, Hanbei Zhan, Jiongchao Jin, Liangcheng Li, Zirui Shao, Jiajun Bu

abstract:
Graphical user interface (GUI) has become integral to modern society, making it crucial to be understood for human-centric systems. However, unlike natural images or documents, GUIs comprise artificially designed graphical elements arranged to convey specific semantic meanings. Current multi-modal large language models (MLLMs) already proficient in processing graphical and textual components suffer from hurdles in GUI understanding due to the lack of explicit spatial structure modeling. Moreover, obtaining high-quality spatial structure data is challenging due to privacy issues and noisy environments. To address these challenges, we present MP-GUI, a specially designed MLLM for GUI understanding. MP-GUI features three precisely specialized perceivers to extract graphical, textual, and spatial modalities from the screen as GUI-tailored visual clues, with spatial structure refinement strategy and adaptively combined via a fusion gate to meet the specific preferences of different GUI understanding tasks. To cope with the scarcity of training data, we also introduce a pipeline for automatically data collecting. Extensive experiments demonstrate that MP-GUI achieves impressive results on various GUI understanding tasks with limited data. Our codes and datasets are publicly available at https://github.com/BigTaige/MP-GUI.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_MP-GUI_Modality_Perception_with_MLLMs_for_GUI_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_MP-GUI_Modality_Perception_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Ziwei and Chen, Weizhi and Yang, Leyang and Zhou, Sheng and Zhao, Shengchu and Zhan, Hanbei and Jin, Jiongchao and Li, Liangcheng and Shao, Zirui and Bu, Jiajun},
    title     = {MP-GUI: Modality Perception with MLLMs for GUI Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29711-29721}
}

raw_affiliation:
[{"affiliation": "College of Computer Science and Technology, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}]

affiliations:
["College of Computer Science and Technology, Zhejiang University", "Zhejiang Key Laboratory of Accessible Perception and Intelligent Systems, Zhejiang University", "Ant Group"]


title: Associative Transformer
authors: Yuwei Sun, Hideya Ochiai, Zhirong Wu, Stephen Lin, Ryota Kanai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4518-4527
affiliations: ["Araya Research", "RIKEN AIP", "The University of Tokyo", "Microsoft Research"]
cities: ["Tokyo", "Tokyo", "Tokyo", "Redmond"]
countries: ["Japan", "Japan", "Japan", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_Associative_Transformer_CVPR_2025_paper.html

abstract:
Emerging from the pairwise attention in conventional Transformers, there is a growing interest in sparse attention mechanisms that align more closely with localized, contextual learning in the biological brain. Existing studies such as the Coordination method employ iterative cross-attention mechanisms with a bottleneck to enable the sparse association of inputs. However, these methods are parameter inefficient and fail in more complex relational reasoning tasks. To this end, we propose Associative Transformer (AiT) to enhance the association among sparsely attended input tokens, improving parameter efficiency and performance in various vision tasks such as classification and relational reasoning. AiT leverages a learnable explicit memory comprising specialized priors that guide bottleneck attentions to facilitate the extraction of diverse localized tokens. Moreover, AiT employs an associative memory-based token reconstruction using a Hopfield energy function. The extensive empirical experiments demonstrate that AiT requires significantly fewer parameters and attention layers outperforming a broad range of sparse Transformer models. Additionally, AiT outperforms the SOTA sparse Transformer models including the Coordination method on the Sort-of-CLEVR dataset.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_Associative_Transformer_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_Associative_Transformer_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Yuwei and Ochiai, Hideya and Wu, Zhirong and Lin, Stephen and Kanai, Ryota},
    title     = {Associative Transformer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4518-4527}
}

raw_affiliation:
[{"affiliation": "Araya Research", "city": "Tokyo", "country": "Japan"}, {"affiliation": "RIKEN AIP", "city": "Tokyo", "country": "Japan"}, {"affiliation": "The University of Tokyo", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Microsoft Research", "city": "Redmond", "country": "USA"}]


title: Improving Adversarial Transferability on Vision Transformers via Forward Propagation Refinement
authors: Yuchen Ren, Zhengyu Zhao, Chenhao Lin, Bo Yang, Lu Zhou, Zhe Liu, Chao Shen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25071-25080
cities: ["Xi'an", "Zhengzhou", "Nanjing", "Hangzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ren_Improving_Adversarial_Transferability_on_Vision_Transformers_via_Forward_Propagation_Refinement_CVPR_2025_paper.html

abstract:
Vision Transformers (ViTs) have been widely applied in various computer vision and vision-language tasks. To gain insights into their robustness in practical scenarios, transferable adversarial examples on ViTs have been extensively studied. A typical approach to improving adversarial transferability is by refining the surrogate model. However, existing work on ViTs has restricted their surrogate refinement to backward propagation. In this work, we instead focus on Forward Propagation Refinement (FPR) and specifically refine two key modules of ViTs: attention maps and token embeddings. For attention maps, we propose Attention Map Diversification (AMD), which diversifies certain attention maps and also implicitly imposes beneficial gradient vanishing during backward propagation. For token embeddings, we propose Momentum Token Embedding (MTE), which accumulates historical token embeddings to stabilize the forward updates in both the Attention and MLP blocks. We conduct extensive experiments with adversarial examples transferred from ViTs to various CNNs and ViTs, demonstrating that our FPR outperforms the current best (backward) surrogate refinement by up to 7.0% on average. We also validate its superiority against popular defenses and its compatibility with other transfer methods. Codes and appendix are available at https://github.com/RYC-98/FPR.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ren_Improving_Adversarial_Transferability_on_Vision_Transformers_via_Forward_Propagation_Refinement_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Ren_2025_CVPR,
    author    = {Ren, Yuchen and Zhao, Zhengyu and Lin, Chenhao and Yang, Bo and Zhou, Lu and Liu, Zhe and Shen, Chao},
    title     = {Improving Adversarial Transferability on Vision Transformers via Forward Propagation Refinement},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25071-25080}
}

raw_affiliation:
[{"affiliation": "Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "Information Engineering University", "city": "Zhengzhou", "country": "China"}, {"affiliation": "Nanjing University of Aeronautics and Astronautics", "city": "Nanjing", "country": "China"}, {"affiliation": "Zhejiang Lab", "city": "Hangzhou", "country": "China"}]

affiliations:
["Xi\u2019an Jiaotong University", "Information Engineering University", "Nanjing University of Aeronautics and Astronautics", "Zhejiang Lab"]


title: Seeing What Matters: Empowering CLIP with Patch Generation-to-Selection
authors: Gensheng Pei, Tao Chen, Yujia Wang, Xinhao Cai, Xiangbo Shu, Tianfei Zhou, Yazhou Yao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24862-24872
cities: ["Nanjing", "Hangzhou", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pei_Seeing_What_Matters_Empowering_CLIP_with_Patch_Generation-to-Selection_CVPR_2025_paper.html

abstract:
The CLIP model has demonstrated significant advancements in aligning visual and language modalities through large-scale pre-training on image-text pairs, enabling strong zero-shot classification and retrieval capabilities on various domains. However, CLIP's training remains computationally intensive, with high demands on both data processing and memory. To address these challenges, recent masking strategies have emerged, focusing on the selective removal of image patches to improve training efficiency. Although effective, these methods often compromise key semantic information, resulting in suboptimal alignment between visual features and text descriptions.In this work, we present a concise yet effective approach called Patch Generation-to-Selection (CLIP-PGS) to enhance CLIP's training efficiency while preserving critical semantic content. Our method introduces a gradual masking process in which a small set of candidate patches is first pre-selected as potential mask regions. Then, we apply Sobel edge detection across the entire image to generate an edge mask that prioritizes the retention of the primary object areas. Finally, similarity scores between the candidate mask patches and their neighboring patches are computed, with optimal transport normalization refining the selection process to ensure a balanced similarity matrix.Our approach, CLIP-PGS, sets new state-of-the-art results in zero-shot classification and retrieval tasks, achieving superior performance in robustness evaluation and language compositionality benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pei_Seeing_What_Matters_Empowering_CLIP_with_Patch_Generation-to-Selection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pei_Seeing_What_Matters_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Pei_2025_CVPR,
    author    = {Pei, Gensheng and Chen, Tao and Wang, Yujia and Cai, Xinhao and Shu, Xiangbo and Zhou, Tianfei and Yao, Yazhou},
    title     = {Seeing What Matters: Empowering CLIP with Patch Generation-to-Selection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24862-24872}
}

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

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


title: ChatGarment: Garment Estimation, Generation and Editing via Large Language Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2924-2934
countries: ["Germany", "China", "Switzerland", "China", "Switzerland", "Canada", "Germany", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bian_ChatGarment_Garment_Estimation_Generation_and_Editing_via_Large_Language_Models_CVPR_2025_paper.html

authors:
Siyuan Bian, Chenghao Xu, Yuliang Xiu, Artur Grigorev, Zhen Liu, Cewu Lu, Michael J. Black, Yao Feng

abstract:
We introduce ChatGarment, a novel approach that leverages large vision-language models (VLMs) to automate the estimation, generation, and editing of 3D garment sewing patterns from images or text descriptions. Unlike previous methods that often lack robustness and interactive editing capabilities, ChatGarment finetunes a VLM to produce GarmentCode, a JSON-based, language-friendly format for 2D sewing patterns, enabling both estimating and editing from images and text instructions. To optimize performance, we refine GarmentCode by expanding its support for more diverse garment types and simplifying its structure, making it more efficient for VLM finetuning. Additionally, we develop an automated data construction pipeline to generate a large-scale dataset of image-to-sewing-pattern and text-to-sewing-pattern pairs, empowering ChatGarment with strong generalization across various garment types.  Extensive evaluations demonstrate ChatGarment's ability to accurately reconstruct, generate, and edit garments from multimodal inputs, highlighting its potential to revolutionize workflows in fashion and gaming applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bian_ChatGarment_Garment_Estimation_Generation_and_Editing_via_Large_Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bian_ChatGarment_Garment_Estimation_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Bian_2025_CVPR,
    author    = {Bian, Siyuan and Xu, Chenghao and Xiu, Yuliang and Grigorev, Artur and Liu, Zhen and Lu, Cewu and Black, Michael J. and Feng, Yao},
    title     = {ChatGarment: Garment Estimation, Generation and Editing via Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2924-2934}
}

raw_affiliation:
[{"affiliation": "Max Planck Institute for Intelligent Systems", "city": "T\u00fcbingen", "country": "Germany"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "EPFL", "city": "Lausanne", "country": "Switzerland"}, {"affiliation": "Westlake University", "city": "Hangzhou", "country": "China"}, {"affiliation": "ETH Z\u00fcrich", "city": "Z\u00fcrich", "country": "Switzerland"}, {"affiliation": "Mila, University of Montreal", "city": "Montreal", "country": "Canada"}, {"affiliation": "Meshcapade", "city": "T\u00fcbingen", "country": "Germany"}, {"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}]

affiliations:
["Max Planck Institute for Intelligent Systems", "Shanghai Jiao Tong University", "EPFL", "Westlake University", "ETH Z\u00fcrich", "Mila, University of Montreal", "Meshcapade", "Stanford University"]

cities:
["T\u00fcbingen", "Shanghai", "Lausanne", "Hangzhou", "Z\u00fcrich", "Montreal", "T\u00fcbingen", "Stanford"]


authors: Sihao Liu, Yibo Yang, Xiaojie Li, David A. Clifton, Bernard Ghanem
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20502-20511
cities: ["Harbin", "Thuwal", "Shenzhen", "Oxford"]
countries: ["China", "Saudi Arabia", "China", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_Enhancing_Online_Continual_Learning_with_Plug-and-Play_State_Space_Model_and_CVPR_2025_paper.html

title:
Enhancing Online Continual Learning with Plug-and-Play State Space Model and Class-Conditional Mixture of Discretization

abstract:
Online continual learning (OCL) seeks to learn new tasks from data streams that appear only once, while retaining knowledge of previously learned tasks. Most existing methods rely on replay, focusing on enhancing memory retention through regularization or distillation. However, they often overlook the adaptability of the model, limiting the ability to learn generalizable and discriminative features incrementally from online training data.To address this, we introduce a plug-and-play module, S6MOD, which can be integrated into most existing methods and directly improve adaptability. Specifically, S6MOD introduces an extra branch after the backbone, where a mixture of discretization selectively adjusts parameters in a selective state space model, enriching selective scan patterns such that the model can adaptively select the most sensitive discretization method for current dynamics.We further design a class-conditional routing algorithm for dynamic, uncertainty-based adjustment and implement a contrastive discretization loss to optimize it. Extensive experiments combining our module with various models demonstrate that S6MOD significantly enhances model adaptability, leading to substantial performance gains and achieving the state-of-the-art results. The code is available at https://github.com/MyToumaKazusa/S6MOD.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_Enhancing_Online_Continual_Learning_with_Plug-and-Play_State_Space_Model_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_Enhancing_Online_Continual_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Sihao and Yang, Yibo and Li, Xiaojie and Clifton, David A. and Ghanem, Bernard},
    title     = {Enhancing Online Continual Learning with Plug-and-Play State Space Model and Class-Conditional Mixture of Discretization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20502-20511}
}

raw_affiliation:
[{"affiliation": "Harbin Institute of T echnology", "city": "Harbin", "country": "China"}, {"affiliation": "King Abdullah University of Science and T echnology", "city": "Thuwal", "country": "Saudi Arabia"}, {"affiliation": "Harbin Institute of T echnology (Shenzhen)", "city": "Shenzhen", "country": "China"}, {"affiliation": "University of Oxford", "city": "Oxford", "country": "UK"}]

affiliations:
["Harbin Institute of T echnology", "King Abdullah University of Science and T echnology", "Harbin Institute of T echnology (Shenzhen)", "University of Oxford"]


title: RDD: Robust Feature Detector and Descriptor using Deformable Transformer
authors: Gonglin Chen, Tianwen Fu, Haiwei Chen, Wenbin Teng, Hanyuan Xiao, Yajie Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6394-6403
affiliations: ["Institute for Creative Technologies", "University of Southern California"]
cities: ["Los Angeles", "Los Angeles"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_RDD_Robust_Feature_Detector_and_Descriptor_using_Deformable_Transformer_CVPR_2025_paper.html

abstract:
As a core step in structure-from-motion and SLAM, robust feature detection and description under challenging scenarios such as significant viewpoint changes remain unresolved despite their ubiquity. While recent works have identified the importance of local features in modeling geometric transformations, these methods fail to learn the visual cues present in long-range relationships. We present Robust Deformable Detector (RDD), a novel and robust keypoint detector/descriptor leveraging the deformable transformer, which captures global context and geometric invariance through deformable self-attention mechanisms. Specifically, we observed that deformable attention focuses on key locations, effectively reducing the search space complexity and modeling the geometric invariance. Furthermore, we collected an Air-to-Ground dataset for training in addition to the standard MegaDepth dataset. Our proposed method outperforms all state-of-the-art keypoint detection/description methods in sparse matching tasks and is also capable of semi-dense matching. To ensure comprehensive evaluation, we introduce two challenging benchmarks: one emphasizing large viewpoint and scale variations, and the other being an Air-to-Ground benchmark -- an evaluation setting that has recently gaining popularity for 3D reconstruction across different altitudes.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_RDD_Robust_Feature_Detector_and_Descriptor_using_Deformable_Transformer_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_RDD_Robust_Feature_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Gonglin and Fu, Tianwen and Chen, Haiwei and Teng, Wenbin and Xiao, Hanyuan and Zhao, Yajie},
    title     = {RDD: Robust Feature Detector and Descriptor using Deformable Transformer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6394-6403}
}

raw_affiliation:
[{"affiliation": "Institute for Creative Technologies", "city": "Los Angeles", "country": "USA"}, {"affiliation": "University of Southern California", "city": "Los Angeles", "country": "USA"}]


title: Building Vision Models upon Heat Conduction
authors: Zhaozhi Wang, Yue Liu, Yunjie Tian, Yunfan Liu, Yaowei Wang, Qixiang Ye
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9707-9717
cities: ["Beijing", "Shenzhen", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Building_Vision_Models_upon_Heat_Conduction_CVPR_2025_paper.html

abstract:
Visual representation models leveraging attention mechanisms are challenged by significant computational overhead, particularly when pursuing large receptive fields. In this study, we aim to mitigate this challenge by introducing the Heat Conduction Operator (HCO) built upon the physical heat conduction principle. HCO conceptualizes image patches as heat sources and models their correlations through adaptive thermal energy diffusion, enabling robust visual representations. HCO enjoys a computational complexity of O(N^1.5), as it can be implemented using discrete cosine transformation (DCT) operations. HCO is plug-and-play, combining with deep learning backbones produces visual representation models (termed vHeat) with global receptive fields. Experiments across vision tasks demonstrate that, beyond the stronger performance, vHeat achieves up to a 3x throughput, 80% less GPU memory allocation, and 35% fewer computational FLOPs compared to the Swin-Transformer.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Building_Vision_Models_upon_Heat_Conduction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Building_Vision_Models_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zhaozhi and Liu, Yue and Tian, Yunjie and Liu, Yunfan and Wang, Yaowei and Ye, Qixiang},
    title     = {Building Vision Models upon Heat Conduction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9707-9717}
}

raw_affiliation:
[{"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Peng Cheng Lab", "city": "Shenzhen", "country": "China"}, {"affiliation": "Harbin Institute of Technology (Shenzhen)", "city": "Shenzhen", "country": "China"}]

affiliations:
["University of Chinese Academy of Sciences", "Peng Cheng Lab", "Harbin Institute of Technology (Shenzhen)"]


title: GRAPHGPT-O: Synergistic Multimodal Comprehension and Generation on Graphs
authors: Yi Fang, Bowen Jin, Jiacheng Shen, Sirui Ding, Qiaoyu Tan, Jiawei Han
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19467-19476
cities: ["Shanghai", "Champaign", "San Francisco"]
countries: ["China", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fang_GRAPHGPT-O_Synergistic_Multimodal_Comprehension_and_Generation_on_Graphs_CVPR_2025_paper.html

abstract:
The rapid development of Multimodal Large Language Models (MLLMs) has enabled the integration of multiple modalities, including texts and images, within the large language model (LLM) framework.However, texts and images are usually interconnected, forming a multimodal attributed graph (MMAG).It is underexplored how MLLMs can incorporate the relational information (i.e., graph structure) and semantic information (i.e., texts and images) on such graphs for multimodal comprehension and generation.In this paper, we propose GraphGPT-o, which supports omni-multimodal understanding and creation on MMAGs.We first comprehensively study linearization variants to transform semantic and structural information as input for MLLMs.Then, we propose a hierarchical aligner that enables deep graph encoding, bridging the gap between MMAGs and MLLMs.Finally, we explore the inference choices, adapting MLLM to interleaved text and image generation in graph scenarios. Extensive experiments on three datasets from different domains demonstrate the effectiveness of our proposed method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fang_GRAPHGPT-O_Synergistic_Multimodal_Comprehension_and_Generation_on_Graphs_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fang_GRAPHGPT-O_Synergistic_Multimodal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fang_2025_CVPR,
    author    = {Fang, Yi and Jin, Bowen and Shen, Jiacheng and Ding, Sirui and Tan, Qiaoyu and Han, Jiawei},
    title     = {GRAPHGPT-O: Synergistic Multimodal Comprehension and Generation on Graphs},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19467-19476}
}

raw_affiliation:
[{"affiliation": "New York University Shanghai", "city": "Shanghai", "country": "China"}, {"affiliation": "University of Illinois at Urbana-Champaign", "city": "Champaign", "country": "USA"}, {"affiliation": "University of California San Francisco", "city": "San Francisco", "country": "USA"}]

affiliations:
["New York University Shanghai", "University of Illinois at Urbana-Champaign", "University of California San Francisco"]


title: Model Poisoning Attacks to Federated Learning via Multi-Round Consistency
authors: Yueqi Xie, Minghong Fang, Neil Zhenqiang Gong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15454-15463
affiliations: ["HKUST", "University of Louisville", "Duke University"]
cities: ["Hong Kong", "Louisville", "Durham"]
countries: ["China", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_Model_Poisoning_Attacks_to_Federated_Learning_via_Multi-Round_Consistency_CVPR_2025_paper.html

abstract:
Model poisoning attacks are critical security threats to Federated Learning (FL). Existing model poisoning attacks suffer from two key limitations: 1) they achieve suboptimal effectiveness when defenses are deployed, and/or 2) they require knowledge of the model updates or local training data on genuine clients. In this work, we make a key observation that their suboptimal effectiveness arises from only leveraging model-update consistency among malicious clients within individual training rounds, making the attack effect self-cancel across training rounds. In light of this observation, we propose PoisonedFL, which enforces multi-round consistency among the malicious clients' model updates while not requiring any knowledge about the genuine clients. Our empirical evaluation on five benchmark datasets shows that PoisonedFL breaks eight state-of-the-art defenses and outperforms seven existing model poisoning attacks. Our study shows that FL systems are considerably less robust than previously thought, underlining the urgency for the development of new defense mechanisms. Our source code is available at https://github.com/xyq7/PoisonedFL/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_Model_Poisoning_Attacks_to_Federated_Learning_via_Multi-Round_Consistency_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_Model_Poisoning_Attacks_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Yueqi and Fang, Minghong and Gong, Neil Zhenqiang},
    title     = {Model Poisoning Attacks to Federated Learning via Multi-Round Consistency},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15454-15463}
}

raw_affiliation:
[{"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}, {"affiliation": "University of Louisville", "city": "Louisville", "country": "USA"}, {"affiliation": "Duke University", "city": "Durham", "country": "USA"}]


authors: Jianchuan Chen, Jingchuan Hu, Gaige Wang, Zhonghua Jiang, Tiansong Zhou, Zhiwen Chen, Chengfei Lv
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10723-10734
raw_affiliation: [{"affiliation": "Alibaba Group", "city": "Hangzhou", "country": "China"}]
affiliations: ["Alibaba Group"]
cities: ["Hangzhou"]
countries: ["China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_TaoAvatar_Real-Time_Lifelike_Full-Body_Talking_Avatars_for_Augmented_Reality_via_CVPR_2025_paper.html

title:
TaoAvatar: Real-Time Lifelike Full-Body Talking Avatars for Augmented Reality via 3D Gaussian Splatting

abstract:
Realistic 3D full-body talking avatars hold great potential in AR, with applications ranging from e-commerce live streaming to holographic communication. Despite advances in 3D Gaussian Splatting (3DGS) for lifelike avatar creation, existing methods struggle with fine-grained control of facial expressions and body movements in full-body talking tasks. Additionally, they often lack sufficient details and cannot run in real-time on mobile devices. We present TaoAvatar, a high-fidelity, lightweight, 3DGS-based full-body talking avatar driven by various signals. Our approach starts by creating a personalized clothed human parametric template that binds Gaussians to represent appearances. We then pre-train a StyleUnet-based network to handle complex pose-dependent non-rigid deformation, which can capture high-frequency appearance details but is too resource-intensive for mobile devices. To overcome this, we "bake" the non-rigid deformations into a lightweight MLP-based network using a distillation technique and develop blend shapes to compensate for details. Extensive experiments show that TaoAvatar achieves state-of-the-art rendering quality while running in real-time across various devices, maintaining 90 FPS on high-definition stereo devices such as the Apple Vision Pro.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_TaoAvatar_Real-Time_Lifelike_Full-Body_Talking_Avatars_for_Augmented_Reality_via_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_TaoAvatar_Real-Time_Lifelike_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Jianchuan and Hu, Jingchuan and Wang, Gaige and Jiang, Zhonghua and Zhou, Tiansong and Chen, Zhiwen and Lv, Chengfei},
    title     = {TaoAvatar: Real-Time Lifelike Full-Body Talking Avatars for Augmented Reality via 3D Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10723-10734}
}


title: Erasing Undesirable Influence in Diffusion Models
authors: Jing Wu, Trung Le, Munawar Hayat, Mehrtash Harandi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28263-28273
affiliations: ["Monash University", "Qualcomm"]
cities: ["Melbourne", "San Diego"]
countries: ["Australia", "US"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_Erasing_Undesirable_Influence_in_Diffusion_Models_CVPR_2025_paper.html

abstract:
Diffusion models are highly effective at generating high-quality images but pose risks, such as the unintentional generation of NSFW (not safe for work) content.Although various techniques have been proposed to mitigate unwanted influences in diffusion models while preserving overall performance, achieving a balance between these goals remains challenging.In this work, we introduce EraseDiff, an algorithm designed to preserve the utility of the diffusion model on retained data while removing the unwanted information associated with the data to be forgotten.Our approach formulates this task as a constrained optimization problem using the value function, resulting in a natural first-order algorithm for solving the optimization problem.By altering the generative process to deviate away from the ground-truth denoising trajectory, we update parameters for preservation while controlling constraint reduction to ensure effective erasure, striking an optimal trade-off.Extensive experiments and thorough comparisons with state-of-the-art algorithms demonstrate that EraseDiff effectively preserves the model's utility, efficacy, and efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_Erasing_Undesirable_Influence_in_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_Erasing_Undesirable_Influence_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Jing and Le, Trung and Hayat, Munawar and Harandi, Mehrtash},
    title     = {Erasing Undesirable Influence in Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28263-28273}
}

raw_affiliation:
[{"affiliation": "Monash University", "city": "Melbourne", "country": "Australia"}, {"affiliation": "Qualcomm", "city": "San Diego", "country": "US"}]


title: LT3SD: Latent Trees for 3D Scene Diffusion
authors: Quan Meng, Lei Li, Matthias Nießner, Angela Dai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 650-660
raw_affiliation: [{"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}]
affiliations: ["Technical University of Munich"]
cities: ["Munich"]
countries: ["Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Meng_LT3SD_Latent_Trees_for_3D_Scene_Diffusion_CVPR_2025_paper.html

abstract:
We present LT3SD, a novel latent diffusion model for large-scale 3D scene generation. Recent advances in diffusion models have shown impressive results in 3D object generation, but are limited in spatial extent and quality when extended to 3D scenes. To generate complex and diverse 3D scene structures, we introduce a latent tree representation to effectively encode both lower-frequency geometry and higher-frequency detail in a coarse-to-fine hierarchy. We can then learn a generative diffusion process in this latent 3D scene space, modeling the latent components of a scene at each resolution level. To synthesize large-scale scenes with varying sizes, we train our diffusion model on scene patches and synthesize arbitrary-sized output 3D scenes through shared diffusion generation across multiple scene patches. Through extensive experiments, we demonstrate the efficacy and benefits of LT3SD for large-scale, high-quality unconditional 3D scene generation and for probabilistic completion for partial scene observations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Meng_LT3SD_Latent_Trees_for_3D_Scene_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Meng_LT3SD_Latent_Trees_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Meng_2025_CVPR,
    author    = {Meng, Quan and Li, Lei and Nie{\ss}ner, Matthias and Dai, Angela},
    title     = {LT3SD: Latent Trees for 3D Scene Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {650-660}
}


title: Stacking Brick by Brick: Aligned Feature Isolation for Incremental Face Forgery Detection
authors: Jikang Cheng, Zhiyuan Yan, Ying Zhang, Li Hao, Jiaxin Ai, Qin Zou, Chen Li, Zhongyuan Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13927-13936
cities: ["Wuhan", "Shenzhen", "Unknown"]
countries: ["China", "China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cheng_Stacking_Brick_by_Brick_Aligned_Feature_Isolation_for_Incremental_Face_CVPR_2025_paper.html

abstract:
The rapid advancement of face forgery techniques has introduced a growing variety of forgeries.Incremental Face Forgery Detection (IFFD), involvinggradually adding new forgery data to fine-tune the previously trained model, has been introduced as a promising strategy to deal with evolving forgery methods.However, a naively trained IFFD model is prone to catastrophic forgetting when new forgeries are integrated, as treating all forgeries as a single "Fake" class in the Real/Fake classification can cause different forgery types overriding one another, thereby resulting in the forgetting of unique characteristics from earlier tasks and limiting the model's effectiveness in learning forgery specificity and generality.In this paper, we propose to stack the latent feature distributions of previous and new tasks brick by brick, i.e.,  achieving aligned feature isolation. In this manner, we aim to preserve learned forgery information and accumulate new knowledge by minimizing distribution overriding, thereby mitigating catastrophic forgetting.To achieve this, we first introduce Sparse Uniform Replay (SUR) to obtain the representative subsets that could be treated as the uniformly sparse versions of the previous global distributions.We then propose a Latent-space Incremental Detector (LID) that leverages SUR data to isolate and align distributions. For evaluation, we construct a more advanced and comprehensive benchmark tailored for IFFD. The leading experimental results validate the superiority of our method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cheng_Stacking_Brick_by_Brick_Aligned_Feature_Isolation_for_Incremental_Face_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cheng_Stacking_Brick_by_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cheng_2025_CVPR,
    author    = {Cheng, Jikang and Yan, Zhiyuan and Zhang, Ying and Hao, Li and Ai, Jiaxin and Zou, Qin and Li, Chen and Wang, Zhongyuan},
    title     = {Stacking Brick by Brick: Aligned Feature Isolation for Incremental Face Forgery Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13927-13936}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School", "city": "Shenzhen", "country": "China"}, {"affiliation": "WeChat Vision, Tencent Inc.", "city": "Unknown", "country": "Unknown"}]

affiliations:
["School of Computer Science, Wuhan University", "School of Electronic and Computer Engineering, Peking University Shenzhen Graduate School", "WeChat Vision, Tencent Inc."]


title: CO-SPY: Combining Semantic and Pixel Features to Detect Synthetic Images by AI
authors: Siyuan Cheng, Lingjuan Lyu, Zhenting Wang, Xiangyu Zhang, Vikash Sehwag
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13455-13465
affiliations: ["Purdue University", "Sony AI", "Rutgers University"]
cities: ["West Lafayette", "Tokyo", "New Brunswick"]
countries: ["USA", "Japan", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cheng_CO-SPY_Combining_Semantic_and_Pixel_Features_to_Detect_Synthetic_Images_CVPR_2025_paper.html

abstract:
With the rapid advancement of generative AI, it is now possible to synthesize high-quality images in a few seconds. Despite the power of these technologies, they raise significant concerns regarding misuse. Current efforts to distinguish between real and AI-generated images may lack generalization, being effective for only certain types of generative models and susceptible to post-processing techniques like JPEG compression. To overcome these limitations, we propose a novel framework, CO-SPY, that first enhances existing semantic features (e.g., the number of fingers in a hand) and artifact features (e.g., pixel value differences), and then adaptively integrates them to achieve more general and robust synthetic image detection. Additionally, we create CO-SPYBench, a comprehensive dataset comprising 5 real image datasets and 22 state-of-the-art generative models, including the latest models like FLUX. We also collect 50k synthetic images in the wild from the Internet to enable evaluation in a more practical setting. Our extensive evaluations demonstrate that our detector outperforms existing methods under identical training conditions, achieving an average accuracy improvement of approximately 11% to 34%.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cheng_CO-SPY_Combining_Semantic_and_Pixel_Features_to_Detect_Synthetic_Images_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cheng_CO-SPY_Combining_Semantic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cheng_2025_CVPR,
    author    = {Cheng, Siyuan and Lyu, Lingjuan and Wang, Zhenting and Zhang, Xiangyu and Sehwag, Vikash},
    title     = {CO-SPY: Combining Semantic and Pixel Features to Detect Synthetic Images by AI},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13455-13465}
}

raw_affiliation:
[{"affiliation": "Purdue University", "city": "West Lafayette", "country": "USA"}, {"affiliation": "Sony AI", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Rutgers University", "city": "New Brunswick", "country": "USA"}]


title: Closest Neighbors are Harmful for Lightweight Masked Auto-encoders
authors: Jian Meng, Ahmed Hasssan, Li Yang, Deliang Fan, Jinwoo Shin, Jae-sun Seo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25230-25239
cities: ["Ithaca", "Charlotte", "Tempe", "Daejeon"]
countries: ["USA", "USA", "USA", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Meng_Closest_Neighbors_are_Harmful_for_Lightweight_Masked_Auto-encoders_CVPR_2025_paper.html

abstract:
Learning the visual representation via masked auto-encoder (MAE) training has been proven to be a powerful technique. Transferring the pre-trained vision transformer (ViT) to downstream tasks leads to superior performance compared to conventional task-by-task supervised learning.  Recent research works on MAE focus on large-sized vision transformers (>50 million parameters) with outstanding performance. However, improving the generality of the under-parametrized lightweight model has been widely ignored.  In practice, downstream applications are commonly intended for resource-constrained platforms, where large-scale ViT cannot easily meet the resource budget. Current lightweight MAE training heavily relies on knowledge distillation with a pre-trained teacher, whereas the root cause behind the poor performance remains under-explored.  Motivated by that, this paper first introduces the concept of "closest neighbor patch" to characterize the local semantics among the input tokens. Our discovery shows that the lightweight model failed to distinguish different local information, leading to aliased understanding and poor accuracy. Motivated by this finding, we propose NoR-MAE, a novel MAE training algorithm for lightweight vision transformers. NoR-MAE elegantly repels the semantic aliasing between patches and their closest neighboring patch (semantic centroid) with negligible training cost overhead. With the ViT-Tiny model, NoR-MAE achieves up to 7.22%/3.64% accuracy improvements on ImageNet-100/ImageNet-1K datasets, as well as up to 5.13% accuracy improvements in tested downstream tasks. https://github.com/SeoLabCornell/NoR-MAE

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Meng_Closest_Neighbors_are_Harmful_for_Lightweight_Masked_Auto-encoders_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Meng_Closest_Neighbors_are_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Meng_2025_CVPR,
    author    = {Meng, Jian and Hasssan, Ahmed and Yang, Li and Fan, Deliang and Shin, Jinwoo and Seo, Jae-sun},
    title     = {Closest Neighbors are Harmful for Lightweight Masked Auto-encoders},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25230-25239}
}

raw_affiliation:
[{"affiliation": "Cornell University", "city": "Ithaca", "country": "USA"}, {"affiliation": "University of North Carolina at Charlotte", "city": "Charlotte", "country": "USA"}, {"affiliation": "Arizona State University", "city": "Tempe", "country": "USA"}, {"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}]

affiliations:
["Cornell University", "University of North Carolina at Charlotte", "Arizona State University", "KAIST"]


authors: Weiyu Li, Jiarui Liu, Hongyu Yan, Rui Chen, Yixun Liang, Xuelin Chen, Ping Tan, Xiaoxiao Long
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5307-5317
affiliations: ["HKUST", "LightIllusions", "Adobe Research"]
cities: ["Hong Kong", "Unknown", "Unknown"]
countries: ["China", "Unknown", "Unknown"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_CraftsMan3D_High-fidelity_Mesh_Generation_with_3D_Native_Diffusion_and_Interactive_CVPR_2025_paper.html

title:
CraftsMan3D: High-fidelity Mesh Generation with 3D Native Diffusion and Interactive Geometry Refiner

abstract:
We present a novel generative 3D modeling system, coined CraftsMan, which can generate high-fidelity 3D geometries with highly varied shapes, regular mesh topologies, and detailed surfaces, and, notably, allows for refining the geometry in an interactive manner. Despite the significant advancements in 3D generation, existing methods still struggle with lengthy optimization processes, self-occlusion, irregular mesh topologies, and difficulties in accommodating user edits, consequently impeding their widespread adoption and implementation in 3D modeling softwares. Our work is inspired by the craftsman, who usually roughs out the holistic figure of the work first and elaborates the surface details subsequently. Specifically, we first introduce a robust data preprocessing pipeline that utilizes visibility check and winding mumber to maximize the use of existing 3D data. Leveraging this data, we employ a 3D-native DiT model that directly models the distribution of 3D data in latent space, generating coarse geometries with regular mesh topology in seconds. Subsequently, a normal-based geometry refiner enhances local surface details, which can be applied automatically or interactively with user input. Extensive experiments demonstrate that our method achieves high efficacy in producing superior quality 3D assets compared to existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_CraftsMan3D_High-fidelity_Mesh_Generation_with_3D_Native_Diffusion_and_Interactive_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_CraftsMan3D_High-fidelity_Mesh_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Weiyu and Liu, Jiarui and Yan, Hongyu and Chen, Rui and Liang, Yixun and Chen, Xuelin and Tan, Ping and Long, Xiaoxiao},
    title     = {CraftsMan3D: High-fidelity Mesh Generation with 3D Native Diffusion and Interactive Geometry Refiner},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5307-5317}
}

raw_affiliation:
[{"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}, {"affiliation": "LightIllusions", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Adobe Research", "city": "Unknown", "country": "Unknown"}]


title: Decouple-Then-Merge: Finetune Diffusion Models as Multi-Task Learning
authors: Qianli Ma, Xuefei Ning, Dongrui Liu, Li Niu, Linfeng Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23281-23291
affiliations: ["Shanghai Jiao Tong University", "Tsinghua University", "Shanghai AI Laboratory", "miguo.ai"]
cities: ["Shanghai", "Beijing", "Shanghai", "Shanghai"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ma_Decouple-Then-Merge_Finetune_Diffusion_Models_as_Multi-Task_Learning_CVPR_2025_paper.html

abstract:
Diffusion models are trained by learning a sequence of models that reverse each step of noise corruption. Typically, the model parameters are fully shared across multiple timesteps to enhance training efficiency. However, since the denoising tasks differ at each timestep, the gradients computed at different timesteps may conflict, potentially degrading the overall performance of image generation. To solve this issue, this work proposes a Decouple-then-Merge (DeMe) framework, which begins with a pretrained model and finetunes separate models tailored to specific timesteps. We introduce several improved techniques during the finetuning stage to promote effective knowledge sharing while minimizing training interference across timesteps. Finally, after finetuning, these separate models can be merged into a single model in the parameter space, ensuring efficient and practical inference. Experimental results show significant generation quality improvements upon 6 benchmarks including Stable Diffusion on COCO30K, ImageNet1K, PartiPrompts, and DDPM on LSUN Church, LSUN Bedroom, and CIFAR10.  Code is included in the supplementary material and will be released on Github.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ma_Decouple-Then-Merge_Finetune_Diffusion_Models_as_Multi-Task_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ma_Decouple-Then-Merge_Finetune_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ma_2025_CVPR,
    author    = {Ma, Qianli and Ning, Xuefei and Liu, Dongrui and Niu, Li and Zhang, Linfeng},
    title     = {Decouple-Then-Merge: Finetune Diffusion Models as Multi-Task Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23281-23291}
}

raw_affiliation:
[{"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "miguo.ai", "city": "Shanghai", "country": "China"}]


title: GIF: Generative Inspiration for Face Recognition at Scale
authors: Saeed Ebrahimi, Sahar Rahimi, Ali Dabouei, Srinjoy Das, Jeremy M. Dawson, Nasser M. Nasrabadi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3528-3539
raw_affiliation: [{"affiliation": "West Virginia University", "city": "Morgantown", "country": "USA"}]
affiliations: ["West Virginia University"]
cities: ["Morgantown"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ebrahimi_GIF_Generative_Inspiration_for_Face_Recognition_at_Scale_CVPR_2025_paper.html

abstract:
Aiming to reduce the computational cost of Softmax in massive label space of Face Recognition (FR) benchmarks, recent studies estimate the output using a subset of identities. Although promising, the association between the computation cost and the number of identities in the dataset remains linear only with a reduced ratio. A shared characteristic among available FR methods is the employment of atomic scalar labels during training. Consequently, the input to label matching is through a dot product between the feature vector of the input and the Softmax centroids. Inspired by generative modeling, we present a simple yet effective method that substitutes scalar labels with structured identity code, i.e., a sequence of integers. Specifically, we propose a tokenization scheme that transforms atomic scalar labels into structured identity codes. Then, we train an FR backbone to predict the code for each input instead of its scalar label. As a result, the associated computational cost becomes logarithmic \wrt number of identities.We demonstrate the benefits of the proposed method by conducting experiments. In particular, our method outperforms its competitors by 1.52%, and 0.6% at TAR@FAR=1e-4 on IJB-B and IJB-C, respectively, while transforming the association between computational cost and the number of identities from linear to logarithmic. \href https://github.com/msed-Ebrahimi/GIF  Code

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ebrahimi_GIF_Generative_Inspiration_for_Face_Recognition_at_Scale_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ebrahimi_GIF_Generative_Inspiration_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ebrahimi_2025_CVPR,
    author    = {Ebrahimi, Saeed and Rahimi, Sahar and Dabouei, Ali and Das, Srinjoy and Dawson, Jeremy M. and Nasrabadi, Nasser M.},
    title     = {GIF: Generative Inspiration for Face Recognition at Scale},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3528-3539}
}


title: HELVIPAD: A Real-World Dataset for Omnidirectional Stereo Depth Estimation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26975-26984
affiliations: ["EPFL", "TU Darmstadt"]
cities: ["Lausanne", "Darmstadt"]
countries: ["Switzerland", "Germany"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zayene_HELVIPAD_A_Real-World_Dataset_for_Omnidirectional_Stereo_Depth_Estimation_CVPR_2025_paper.html

authors:
Mehdi Zayene, Jannik Endres, Albias Havolli, Charles Corbière, Salim Cherkaoui, Alexandre Kontouli, Alexandre Alahi

abstract:
Despite progress in stereo depth estimation, omnidirectional imaging remains underexplored, mainly due to the lack of appropriate data. We introduce Helvipad, a real-world dataset for omnidirectional stereo depth estimation, featuring 40K video frames from video sequences across diverse environments, including crowded indoor and outdoor scenes with various lighting conditions. Collected using two 360deg cameras in a top-bottom setup and a LiDAR sensor, the dataset includes accurate depth and disparity labels by projecting 3D point clouds onto equirectangular images. Additionally, we provide an augmented training set with an increased label density by using depth completion. We benchmark leading stereo depth estimation models for both standard and omnidirectional images. The results show that while recent stereo methods perform decently, a challenge persists in accurately estimating depth in omnidirectional imaging. To address this, we introduce necessary adaptations to stereo models, leading to improved performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zayene_HELVIPAD_A_Real-World_Dataset_for_Omnidirectional_Stereo_Depth_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zayene_HELVIPAD_A_Real-World_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zayene_2025_CVPR,
    author    = {Zayene, Mehdi and Endres, Jannik and Havolli, Albias and Corbi\`ere, Charles and Cherkaoui, Salim and Kontouli, Alexandre and Alahi, Alexandre},
    title     = {HELVIPAD: A Real-World Dataset for Omnidirectional Stereo Depth Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26975-26984}
}

raw_affiliation:
[{"affiliation": "EPFL", "city": "Lausanne", "country": "Switzerland"}, {"affiliation": "TU Darmstadt", "city": "Darmstadt", "country": "Germany"}]


title: GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12187-12198
cities: ["Shanghai", "Xi'an", "Hangzhou", "Nanjing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_GENMANIP_LLM-driven_Simulation_for_Generalizable_Instruction-Following_Manipulation_CVPR_2025_paper.html

authors:
Ning Gao, Yilun Chen, Shuai Yang, Xinyi Chen, Yang Tian, Hao Li, Haifeng Huang, Hanqing Wang, Tai Wang, Jiangmiao Pang

abstract:
Robotic manipulation in real-world settings remains challenging, especially regarding robust generalization. Existing simulation platforms lack sufficient support for exploring how policies adapt to varied instructions and scenarios. Thus, they lag behind the growing interest in instruction-following foundation models like LLMs, whose adaptability is crucial yet remains underexplored in fair comparisons. To bridge this gap, we introduce GenManip, a realistic tabletop simulation platform tailored for policy generalization studies. It features an automatic pipeline via LLM-driven task-oriented scene graph to synthesize large-scale, diverse tasks using 10K annotated 3D object assets. To systematically assess generalization, we present GenManip-Bench, a benchmark of 200 scenarios refined via human-in-the-loop corrections. We evaluate two policy types: (1) modular manipulation systems integrating foundation models for perception, reasoning, and planning, and (2) end-to-end policies trained through scalable data collection. Results show that while data scaling benefits end-to-end methods, modular systems enhanced with foundation models generalize more effectively across diverse scenarios. We anticipate this platform to facilitate critical insights for advancing policy generalization in realistic conditions. All code will be made publicly available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_GENMANIP_LLM-driven_Simulation_for_Generalizable_Instruction-Following_Manipulation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_GENMANIP_LLM-driven_Simulation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gao_2025_CVPR,
    author    = {Gao, Ning and Chen, Yilun and Yang, Shuai and Chen, Xinyi and Tian, Yang and Li, Hao and Huang, Haifeng and Wang, Hanqing and Wang, Tai and Pang, Jiangmiao},
    title     = {GENMANIP: LLM-driven Simulation for Generalizable Instruction-Following Manipulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12187-12198}
}

raw_affiliation:
[{"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Xi\u2019an Jiaotong University", "city": "Xi'an", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}]

affiliations:
["Shanghai AI Laboratory", "Xi\u2019an Jiaotong University", "Zhejiang University", "Nanjing University"]


title: SKDream: Controllable Multi-view and 3D Generation with Arbitrary Skeletons
authors: Yuanyou Xu, Zongxin Yang, Yi Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 314-325
affiliations: ["ReLER, CCAI, Zhejiang University", "DBMI, HMS, Harvard University"]
cities: ["Hangzhou", "Boston"]
countries: ["China", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_SKDream_Controllable_Multi-view_and_3D_Generation_with_Arbitrary_Skeletons_CVPR_2025_paper.html

abstract:
Controllable generation has achieved substantial progress in both 2D and 3D domains, yet current conditional generation methods still face limitations in describing detailed shape structures. Skeletons can effectively represent and describe object anatomy and pose. Unfortunately, past studies are often limited to human skeletons. In this work, we generalize skeletal conditioned generation to arbitrary structures. First, we design a reliable mesh skeletonization pipeline to generate a large-scale mesh-skeleton paired dataset.Based on the dataset, a multi-view and 3D generation pipeline is built. We propose to represent 3D skeletons by Coordinate Color Encoding as 2D conditional images. A Skeletal Correlation Module is designed to extract global skeletal features for condition injection. After multi-view images are generated, 3D assets can be obtained by incorporating a large reconstruction model, followed by a UV texture refinement stage. As a result, our method achieves instant generation of multi-view and 3D contents that are aligned with given skeletons. The proposed techniques largely improve the object-skeleton alignment and generation quality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_SKDream_Controllable_Multi-view_and_3D_Generation_with_Arbitrary_Skeletons_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_SKDream_Controllable_Multi-view_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Yuanyou and Yang, Zongxin and Yang, Yi},
    title     = {SKDream: Controllable Multi-view and 3D Generation with Arbitrary Skeletons},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {314-325}
}

raw_affiliation:
[{"affiliation": "ReLER, CCAI, Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "DBMI, HMS, Harvard University", "city": "Boston", "country": "USA"}]


authors: Yikai Wang, Chenjie Cao, Junqiu Yu, Ke Fan, Xiangyang Xue, Yanwei Fu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23237-23248
affiliations: ["Fudan University", "Nanyang Technological University", "Alibaba DAMO Academy", "Hupan Lab"]
cities: ["Shanghai", "Singapore", "Hangzhou", "Hangzhou"]
countries: ["China", "Singapore", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Towards_Enhanced_Image_Inpainting_Mitigating_Unwanted_Object_Insertion_and_Preserving_CVPR_2025_paper.html

title:
Towards Enhanced Image Inpainting: Mitigating Unwanted Object Insertion and Preserving Color Consistency

abstract:
Recent advances in image inpainting increasingly use generative models to handle large irregular masks. However, these models can create unrealistic inpainted images due to two main issues: (1) Unwanted object insertion: Even with unmasked areas as context, generative models may still generate arbitrary objects in the masked region that don't align with the rest of the image. (2) Color inconsistency: Inpainted regions often have color shifts that causes a smeared appearance, reducing image quality. Retraining the generative model could help solve these issues, but it's costly since state-of-the-art latent-based diffusion and rectified flow models require a three-stage training process: training a VAE, training a generative U-Net or transformer, and fine-tuning for inpainting. Instead, this paper proposes a post-processing approach, dubbed as ASUKA (Aligned Stable inpainting with UnKnown Areas prior), to improve inpainting models. To address unwanted object insertion, we leverage a Masked Auto-Encoder (MAE) for reconstruction-based priors. This mitigates object hallucination while maintaining the model's generation capabilities. To address color inconsistency, we propose a specialized VAE decoder that treats latent-to-image decoding as a local harmonization task, significantly reducing color shifts for color-consistent inpainting. We validate ASUKA on SD 1.5 and FLUX inpainting variants with Places2 and MISATO, our proposed diverse collection of datasets. Results show that ASUKA mitigates object hallucination and improves color consistency over standard diffusion and rectified flow models and other inpainting methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Towards_Enhanced_Image_Inpainting_Mitigating_Unwanted_Object_Insertion_and_Preserving_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Towards_Enhanced_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yikai and Cao, Chenjie and Yu, Junqiu and Fan, Ke and Xue, Xiangyang and Fu, Yanwei},
    title     = {Towards Enhanced Image Inpainting: Mitigating Unwanted Object Insertion and Preserving Color Consistency},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23237-23248}
}

raw_affiliation:
[{"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Alibaba DAMO Academy", "city": "Hangzhou", "country": "China"}, {"affiliation": "Hupan Lab", "city": "Hangzhou", "country": "China"}]


title: Optimus-2: Multimodal Minecraft Agent with Goal-Observation-Action Conditioned Policy
authors: Zaijing Li, Yuquan Xie, Rui Shao, Gongwei Chen, Dongmei Jiang, Liqiang Nie
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9039-9049
affiliations: ["Harbin Institute of Technology, Shenzhen", "Peng Cheng Laboratory"]
cities: ["Shenzhen", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Optimus-2_Multimodal_Minecraft_Agent_with_Goal-Observation-Action_Conditioned_Policy_CVPR_2025_paper.html

abstract:
Building an agent that can mimic human behavior patterns to accomplish various open-world tasks  is a long-term goal. To enable agents to effectively learn behavioral patterns across diverse tasks, a key challenge lies in modeling the intricate relationships among observations, actions, and language. To this end, we propose Optimus-2, a novel Minecraft agent that incorporates a Multimodal Large Language Model (MLLM) for high-level planning, alongside a Goal-Observation-Action Conditioned Policy (GOAP) for low-level control. GOAP contains (1) an Action-guided Behavior Encoder that models causal relationships between observations and actions at each timestep, then dynamically interacts with the historical observation-action sequence, consolidating it into fixed-length behavior tokens, and (2) an MLLM that aligns behavior tokens with open-ended language instructions to predict actions auto-regressively. Moreover, we introduce a high-quality Minecraft Goal-Observation-Action (MGOA) dataset, which contains 25,000 videos across 8 atomic tasks, providing about 30M goal-observation-action pairs. The automated construction method, along with the MGOA dataset, can contribute to the community's efforts in training Minecraft agents. Extensive experimental results demonstrate that Optimus-2 exhibits superior performance across atomic tasks, long-horizon tasks, and open-ended instruction tasks in Minecraft.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Optimus-2_Multimodal_Minecraft_Agent_with_Goal-Observation-Action_Conditioned_Policy_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Optimus-2_Multimodal_Minecraft_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Zaijing and Xie, Yuquan and Shao, Rui and Chen, Gongwei and Jiang, Dongmei and Nie, Liqiang},
    title     = {Optimus-2: Multimodal Minecraft Agent with Goal-Observation-Action Conditioned Policy},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9039-9049}
}

raw_affiliation:
[{"affiliation": "Harbin Institute of Technology, Shenzhen", "city": "Shenzhen", "country": "China"}, {"affiliation": "Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}]


title: Classic Video Denoising in a Machine Learning World: Robust, Fast, and Controllable
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2084-2093
affiliations: ["VCIP, CS, Nankai University", "Adobe Research", "Adobe", "NKIARI, Shenzhen Futian"]
cities: ["Tianjin", "San Jose", "San Jose", "Shenzhen"]
countries: ["China", "USA", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jin_Classic_Video_Denoising_in_a_Machine_Learning_World_Robust_Fast_CVPR_2025_paper.html

authors:
Xin Jin, Simon Niklaus, Zhoutong Zhang, Zhihao Xia, Chunle Guo, Yuting Yang, Jiawen Chen, Chongyi Li

abstract:
Denoising is a crucial step in many video processing pipelines such as in interactive editing, where high quality, speed, and user control are essential. While recent approaches achieve significant improvements in denoising quality by leveraging deep learning, they are prone to unexpected failures due to discrepancies between training data distributions and the wide variety of noise patterns found in real-world videos. These methods also tend to be slow and lack user control. In contrast, traditional denoising methods perform reliably on in-the-wild videos and run relatively quickly on modern hardware. However, they require manually tuning parameters for each input video, which is not only tedious but also requires skill. We bridge the gap between these two paradigms by proposing a differentiable denoising pipeline based on traditional methods. A neural network is then trained to predict the optimal denoising parameters for each specific input, resulting in a robust and efficient approach that also supports user control.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jin_Classic_Video_Denoising_in_a_Machine_Learning_World_Robust_Fast_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jin_Classic_Video_Denoising_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jin_2025_CVPR,
    author    = {Jin, Xin and Niklaus, Simon and Zhang, Zhoutong and Xia, Zhihao and Guo, Chunle and Yang, Yuting and Chen, Jiawen and Li, Chongyi},
    title     = {Classic Video Denoising in a Machine Learning World: Robust, Fast, and Controllable},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2084-2093}
}

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


title: Practical Solutions to the Relative Pose of Three Calibrated Cameras
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21913-21923
cities: ["Prague", "Bratislava", "Zurich", "Prague"]
countries: ["Czech Republic", "Slovakia", "Switzerland", "Czech Republic"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tzamos_Practical_Solutions_to_the_Relative_Pose_of_Three_Calibrated_Cameras_CVPR_2025_paper.html

authors:
Charalambos Tzamos, Viktor Kocur, Yaqing Ding, Daniel Barath, Zuzana Berger Haladova, Torsten Sattler, Zuzana Kukelova

abstract:
We study the challenging problem of estimating the relative pose of three calibrated cameras from four point correspondences. We propose novel efficient solutions to this problem that are based on the simple idea of using four correspondences to estimate an approximate geometry of the first two views. We model this geometry either as an affine or a fully perspective geometry estimated using one additional approximate correspondence. We generate such an approximate correspondence using a very simple and efficient strategy, where the new point is the mean point of three corresponding input points. The new solvers are efficient and easy to implement, since they are based on existing efficient minimal solvers, i.e., the 4-point affine fundamental matrix, the well-known 5-point relative pose solver, and the \texttt P3P  solver. Extensive experiments on real data show that the proposed solvers, when properly coupled with local optimization, achieve state-of-the-art results, with the novel solver based on approximate mean-point correspondences being more robust and accurate than the affine-based solver.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tzamos_Practical_Solutions_to_the_Relative_Pose_of_Three_Calibrated_Cameras_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tzamos_Practical_Solutions_to_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tzamos_2025_CVPR,
    author    = {Tzamos, Charalambos and Kocur, Viktor and Ding, Yaqing and Barath, Daniel and Haladova, Zuzana Berger and Sattler, Torsten and Kukelova, Zuzana},
    title     = {Practical Solutions to the Relative Pose of Three Calibrated Cameras},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21913-21923}
}

raw_affiliation:
[{"affiliation": "Visual Recognition Group, Faculty of Electrical Engineering, Czech Technical University in Prague", "city": "Prague", "country": "Czech Republic"}, {"affiliation": "Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava", "city": "Bratislava", "country": "Slovakia"}, {"affiliation": "ETH Z\u00a8urich (Zurich), HUN-REN SZTAKI (Budapest)", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague", "city": "Prague", "country": "Czech Republic"}]

affiliations:
["Visual Recognition Group, Faculty of Electrical Engineering, Czech Technical University in Prague", "Faculty of Mathematics, Physics and Informatics, Comenius University in Bratislava", "ETH Z\u00a8urich (Zurich), HUN-REN SZTAKI (Budapest)", "Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague"]


authors: Byung Hyun Lee, Sungjin Lim, Se Young Chun
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18596-18606
cities: ["Seoul", "Seoul", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Localized_Concept_Erasure_for_Text-to-Image_Diffusion_Models_Using_Training-Free_Gated_CVPR_2025_paper.html

title:
Localized Concept Erasure for Text-to-Image Diffusion Models Using Training-Free Gated Low-Rank Adaptation

abstract:
Fine-tuning based concept erasing has demonstrated promising results in preventing generation of harmful contents from text-to-image diffusion models by removing target concepts while preserving remaining concepts. To maintain the generation capability of diffusion models after concept erasure, it is necessary to remove only the image region containing the target concept when it locally appears in an image, leaving other regions intact. However, prior arts often compromise fidelity of the other image regions in order to erase the localized target concept appearing in a specific area, thereby reducing the overall performance of image generation. To address these limitations, we first introduce a framework called localized concept erasure, which allows for the deletion of only the specific area containing the target concept in the image while preserving the other regions. As a solution for the localized concept erasure, we propose a training-free approach, dubbed Gated Low-rank adaptation for Concept Erasure (GLoCE), that injects a lightweight module into the diffusion model. GLoCE consists of low-rank matrices and a simple gate, determined only by several generation steps for concepts without training. By directly applying GLoCE to image embeddings and designing the gate to activate only for target concepts, GLoCE can selectively remove only the region of the target concepts, even when target and remaining concepts coexist within an image. Extensive experiments demonstrated GLoCE not only improves the image fidelity to text prompts after erasing the localized target concepts, but also outperforms prior arts in efficacy, specificity, and robustness by large margin and can be extended to mass concept erasure.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_Localized_Concept_Erasure_for_Text-to-Image_Diffusion_Models_Using_Training-Free_Gated_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_Localized_Concept_Erasure_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Byung Hyun and Lim, Sungjin and Chun, Se Young},
    title     = {Localized Concept Erasure for Text-to-Image Diffusion Models Using Training-Free Gated Low-Rank Adaptation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18596-18606}
}

raw_affiliation:
[{"affiliation": "Department of ECE, Seoul National University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "IPAI, Seoul National University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "INMC, Seoul National University", "city": "Seoul", "country": "South Korea"}]

affiliations:
["Department of ECE, Seoul National University", "IPAI, Seoul National University", "INMC, Seoul National University"]


title: PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models
authors: Jenny Schmalfuss, Nadine Chang, Vibashan VS, Maying Shen, Andres Bruhn, Jose M. Alvarez
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25081-25091
affiliations: ["University of Stuttgart", "NVIDIA", "Johns Hopkins University"]
cities: ["Stuttgart", "Santa Clara", "Baltimore"]
countries: ["Germany", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Schmalfuss_PARC_A_Quantitative_Framework_Uncovering_the_Symmetries_within_Vision_Language_CVPR_2025_paper.html

abstract:
Vision language models (VLMs) respond to user-crafted text prompts and visual inputs, and are applied to numerous real-world problems. VLMs integrate visual modalities with large language models (LLMs), which are well known to be prompt-sensitive. Hence, it is crucial to determine whether VLMs inherit this instability to varying prompts. We therefore investigate which prompt variations VLMs are most sensitive to and which VLMs are most agnostic to prompt variations. To this end, we introduce PARC (Prompt Analysis via Reliability and Calibration), a VLM prompt sensitivity analysis framework built on three pillars: (1) plausible prompt variations in both the language and vision domain, (2) a novel model reliability score with built-in guarantees, and (3) a calibration step that enables dataset- and prompt-spanning prompt variation analysis. Regarding prompt variations, PARC's evaluation shows that VLMs mirror LLM language prompt sensitivity in the vision domain, and most destructive variations change the expected answer. Regarding models, outstandingly robust VLMs among 22 evaluated models come from the InternVL2 family. We further find indications that prompt sensitivity is linked to training data. https://github.com/NVlabs/PARC

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Schmalfuss_PARC_A_Quantitative_Framework_Uncovering_the_Symmetries_within_Vision_Language_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Schmalfuss_PARC_A_Quantitative_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Schmalfuss_2025_CVPR,
    author    = {Schmalfuss, Jenny and Chang, Nadine and VS, Vibashan and Shen, Maying and Bruhn, Andres and Alvarez, Jose M.},
    title     = {PARC: A Quantitative Framework Uncovering the Symmetries within Vision Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25081-25091}
}

raw_affiliation:
[{"affiliation": "University of Stuttgart", "city": "Stuttgart", "country": "Germany"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}, {"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}]


title: RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27649-27660
countries: ["China", "China", "China", "China", "China", "USA", "China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Mu_RoboTwin_Dual-Arm_Robot_Benchmark_with_Generative_Digital_Twins_CVPR_2025_paper.html

authors:
Yao Mu, Tianxing Chen, Zanxin Chen, Shijia Peng, Zhiqian Lan, Zeyu Gao, Zhixuan Liang, Qiaojun Yu, Yude Zou, Mingkun Xu, Lunkai Lin, Zhiqiang Xie, Mingyu Ding, Ping Luo

abstract:
In the rapidly advancing field of robotics, dual-arm coordination and complex object manipulation are essential capabilities for developing advanced autonomous systems. However, the scarcity of diverse, high-quality demonstration data and real-world-aligned evaluation benchmarks severely limits such development. To address this, we introduce RoboTwin, a generative digital twin framework that uses 3D generative foundation models and large language models to produce diverse expert datasets and provide a real-world-aligned evaluation platform for dual-arm robotic tasks. Specifically, RoboTwin creates varied digital twins of objects from single 2D images, generating realistic and interactive scenarios. It also introduces a spatial relation-aware code generation framework that combines object annotations with large language models to break down tasks, determine spatial constraints, and generate precise robotic movement code. Our framework offers a comprehensive benchmark with both simulated and real-world data, enabling standardized evaluation and better alignment between simulated training and real-world performance. We validated our approach using the open-source COBOT Magic Robot platform. Policies pre-trained on RoboTwin-generated data and fine-tuned with limited real-world samples demonstrate significant potential for enhancing dual-arm robotic manipulation systems by improving success rates by over 70% for single-arm tasks and over 40% for dual-arm tasks compared to models trained solely on real-world data.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Mu_RoboTwin_Dual-Arm_Robot_Benchmark_with_Generative_Digital_Twins_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Mu_RoboTwin_Dual-Arm_Robot_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Mu_2025_CVPR,
    author    = {Mu, Yao and Chen, Tianxing and Chen, Zanxin and Peng, Shijia and Lan, Zhiqian and Gao, Zeyu and Liang, Zhixuan and Yu, Qiaojun and Zou, Yude and Xu, Mingkun and Lin, Lunkai and Xie, Zhiqiang and Ding, Mingyu and Luo, Ping},
    title     = {RoboTwin: Dual-Arm Robot Benchmark with Generative Digital Twins},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27649-27660}
}

raw_affiliation:
[{"affiliation": "HKU", "city": "Hong Kong", "country": "China"}, {"affiliation": "Agilex Robotics", "city": "Nanjing", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "SZU", "city": "Shenzhen", "country": "China"}, {"affiliation": "CASIA", "city": "Beijing", "country": "China"}, {"affiliation": "UNC-Chapel Hill", "city": "Chapel Hill", "country": "USA"}, {"affiliation": "GDIIST", "city": "Guangzhou", "country": "China"}, {"affiliation": "HKU-Shanghai ICRC", "city": "Shanghai", "country": "China"}, {"affiliation": "SJTU", "city": "Shanghai", "country": "China"}]

affiliations:
["HKU", "Agilex Robotics", "Shanghai AI Laboratory", "SZU", "CASIA", "UNC-Chapel Hill", "GDIIST", "HKU-Shanghai ICRC", "SJTU"]

cities:
["Hong Kong", "Nanjing", "Shanghai", "Shenzhen", "Beijing", "Chapel Hill", "Guangzhou", "Shanghai", "Shanghai"]


title: Population Normalization for Federated Learning
authors: Zhuoyao Wang, Fan Yi, Peizhu Gong, Caitou He, Cheng Jin, Weizhong Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10214-10223
cities: ["Shanghai", "Wenzhou", "Shanghai", "Shanghai"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Population_Normalization_for_Federated_Learning_CVPR_2025_paper.html

abstract:
Batch normalization (BN) is widely recognized as an essential method in training deep neural networks, facilitating convergence and enhancing model stability. However, in Federated Learning (FL) contexts, where training data are typically heterogeneous and clients often face resource constraints, the effectiveness of BN is considerably limited for two primary reasons. First, the population statistics, specifically the mean and variance of these heterogeneous datasets, vary substantially, resulting in inconsistent BN layers across client models, which ultimately drives these models to diverge further. Second, estimating statistics from a mini-batch is often imprecise since the batch size has to be small in resource-limited clients. This paper introduces Population Normalization, a novel technique for FL, in which the statistics are learned as trainable parameters rather than calculated from mini-batches as in BN. Thus, our normalization layers are homogeneous among the clients and the adverse impact of small batch size is eliminated as the model can be well-trained even when the batch size equals to one. To enhance the flexibility of our method in practical applications, we investigate the role of stochastic uncertainty in BN's statistical estimation. When larger batch sizes are available, we demonstrate that injecting simple artificial noise can effectively mimic this stochastic uncertainty and improve the model's generalization capability. Experimental results validate the efficacy of our approach across various FL tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Population_Normalization_for_Federated_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Population_Normalization_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Zhuoyao and Yi, Fan and Gong, Peizhu and He, Caitou and Jin, Cheng and Zhang, Weizhong},
    title     = {Population Normalization for Federated Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10214-10223}
}

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

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


title: AnimateAnything: Consistent and Controllable Animation for Video Generation
authors: Guojun Lei, Chi Wang, Rong Zhang, Yikai Wang, Hong Li, Weiwei Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27946-27956
cities: ["Hangzhou", "Beijing", "Beijing", "Hangzhou", "Unknown"]
countries: ["China", "China", "China", "China", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lei_AnimateAnything_Consistent_and_Controllable_Animation_for_Video_Generation_CVPR_2025_paper.html

abstract:
We propose a unified approach for video-controlled generation, enabling text-based guidance and manual annotations to control the generation of videos, similar to camera direction guidance. Specifically, we designed a two-stage algorithm. In the first stage, we convert all control information into frame-by-frame motion flows. In the second stage, we use these motion flows as guidance to control the final video generation. Additionally, to reduce instability in the generated videos caused by large motion variations (such as those from camera movement, object motion, or manual inputs), which can result in flickering or the intermittent disappearance of objects, we transform the temporal feature computation in the video model into frequency-domain feature computation. This is because frequency-domain signals better capture the essential characteristics of an image, and by ensuring consistency in the video's frequency-domain features, we can enhance temporal coherence and reduce flickering in the final generated video.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lei_AnimateAnything_Consistent_and_Controllable_Animation_for_Video_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lei_AnimateAnything_Consistent_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lei_2025_CVPR,
    author    = {Lei, Guojun and Wang, Chi and Zhang, Rong and Wang, Yikai and Li, Hong and Xu, Weiwei},
    title     = {AnimateAnything: Consistent and Controllable Animation for Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27946-27956}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "Zhejiang Gongshang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "ShengShu", "city": "Unknown", "country": "Unknown"}]

affiliations:
["Zhejiang University", "Tsinghua University", "Beihang University", "Zhejiang Gongshang University", "ShengShu"]


title: PRaDA: Projective Radial Distortion Averaging
authors: Daniil Sinitsyn, Linus Härenstam-Nielsen, Daniel Cremers
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21902-21912
affiliations: ["Technical University of Munich", "Munich Center for Machine Learning"]
cities: ["Munich", "Munich"]
countries: ["Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sinitsyn_PRaDA_Projective_Radial_Distortion_Averaging_CVPR_2025_paper.html

abstract:
We tackle the problem of automatic calibration of radially distorted cameras in challenging conditions. Accurately determining distortion parameters typically requires either 1) solving the full Structure from Motion (SfM) problem involving camera poses, 3D points, and the distortion parameters, which is only possible if many images with sufficient overlap are provided, or 2) relying heavily on learning-based methods that are comparatively less accurate. In this work, we demonstrate that distortion calibration can be decoupled from 3D reconstruction, maintaining the accuracy of SfM-based methods while avoiding many of the associated complexities. This is achieved by working in Projective Space, where the geometry is unique up to a homography, which encapsulates all camera parameters except for distortion. Our proposed method, Projective Radial Distortion Averaging, averages multiple distortion estimates in a fully projective framework without creating 3d points and full bundle adjustment. By relying on pairwise projective relations, our methods support any feature-matching approaches without constructing point tracks across multiple images.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sinitsyn_PRaDA_Projective_Radial_Distortion_Averaging_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sinitsyn_PRaDA_Projective_Radial_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sinitsyn_2025_CVPR,
    author    = {Sinitsyn, Daniil and H\"arenstam-Nielsen, Linus and Cremers, Daniel},
    title     = {PRaDA: Projective Radial Distortion Averaging},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21902-21912}
}

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


title: GenAssets: Generating in-the-wild 3D Assets in Latent Space
authors: Ze Yang, Jingkang Wang, Haowei Zhang, Sivabalan Manivasagam, Yun Chen, Raquel Urtasun
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22392-22403
affiliations: ["Waabi", "University of Toronto"]
cities: ["Toronto", "Toronto"]
countries: ["Canada", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_GenAssets_Generating_in-the-wild_3D_Assets_in_Latent_Space_CVPR_2025_paper.html

abstract:
High-quality 3D assets for traffic participants are critical for multi-sensor simulation, which is essential for the safe end-to-end development of autonomy. Building assets from in-the-wild data is key for diversity and realism, but existing neural-rendering based reconstruction methods are slow and generate assets that render well only from viewpoints close to the original observations, limiting their usefulness in simulation. Recent diffusion-based generative models build complete and diverse assets, but perform poorly on in-the-wild driving scenes, where observed actors are captured under sparse and limited fields of view, and are partially occluded. In this work, we propose a 3D latent diffusion model that learns on in-the-wild LiDAR and camera data captured by a sensor platform and generates high-quality 3D assets with complete geometry and appearance. Key to our method is a "reconstruct-then-generate" approach that first leverages occlusion-aware neural rendering trained over multiple scenes to build a high-quality latent space for objects, and then trains a diffusion model that operates on the latent space. We show our method outperforms existing reconstruction and generation based methods, unlocking diverse and scalable content creation for simulation. Please visit https://waabi.ai/genassets for more details.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_GenAssets_Generating_in-the-wild_3D_Assets_in_Latent_Space_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_GenAssets_Generating_in-the-wild_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Ze and Wang, Jingkang and Zhang, Haowei and Manivasagam, Sivabalan and Chen, Yun and Urtasun, Raquel},
    title     = {GenAssets: Generating in-the-wild 3D Assets in Latent Space},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22392-22403}
}

raw_affiliation:
[{"affiliation": "Waabi", "city": "Toronto", "country": "Canada"}, {"affiliation": "University of Toronto", "city": "Toronto", "country": "Canada"}]


title: RipVIS: Rip Currents Video Instance Segmentation Benchmark for Beach Monitoring and Safety
authors: Andrei Dumitriu, Florin Tatui, Florin Miron, Aakash Ralhan, Radu Tudor Ionescu, Radu Timofte
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3427-3437
cities: ["W\u00fcrzburg", "Bucharest"]
countries: ["Germany", "Romania"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dumitriu_RipVIS_Rip_Currents_Video_Instance_Segmentation_Benchmark_for_Beach_Monitoring_CVPR_2025_paper.html

abstract:
Rip currents are strong, localized and narrow currents of water that flow outwards into the sea, causing numerous beach-related injuries and fatalities worldwide. Accurate identification of rip currents remains challenging due to their amorphous nature and the lack of annotated data, which often requires expert knowledge. To address these issues, we present RipVIS, a large-scale video instance segmentation benchmark explicitly designed for rip current segmentation. RipVIS is an order of magnitude larger than previous datasets, featuring 184 videos (212,328 frames), of which 150 videos (163,528 frames) are with rip currents, collected from various sources, including drones, mobile phones, and fixed beach cameras. Our dataset encompasses diverse visual contexts, such as wave-breaking patterns, sediment flows, and water color variations, across multiple global locations, including USA, Mexico, Costa Rica, Portugal, Italy, Greece, Romania, Sri Lanka, Australia and New Zealand. Most videos are annotated at 5 FPS to ensure accuracy in dynamic scenarios, supplemented by an additional 34 videos (48,800 frames) without rip currents. We conduct comprehensive experiments with Mask R-CNN, Cascade Mask R-CNN, SparseInst and YOLO11, fine-tuning these models for the task of rip current segmentation. Results are reported in terms of multiple metrics, with a particular focus on the F2 score to prioritize recall and reduce false negatives. To enhance segmentation performance, we introduce a novel post-processing step based on Temporal Confidence Aggregation (TCA). RipVIS aims to set a new standard for rip current segmentation, contributing towards safer beach environments. We offer a benchmark website to share data, models, and results with the research community, encouraging ongoing collaboration and future contributions, at https://ripvis.ai.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dumitriu_RipVIS_Rip_Currents_Video_Instance_Segmentation_Benchmark_for_Beach_Monitoring_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dumitriu_RipVIS_Rip_Currents_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dumitriu_2025_CVPR,
    author    = {Dumitriu, Andrei and Tatui, Florin and Miron, Florin and Ralhan, Aakash and Ionescu, Radu Tudor and Timofte, Radu},
    title     = {RipVIS: Rip Currents Video Instance Segmentation Benchmark for Beach Monitoring and Safety},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3427-3437}
}

raw_affiliation:
[{"affiliation": "Computer Vision Lab, CAIDAS & IFI, University of W\u00a8urzburg, Germany", "city": "W\u00fcrzburg", "country": "Germany"}, {"affiliation": "University of Bucharest, Romania", "city": "Bucharest", "country": "Romania"}]

affiliations:
["Computer Vision Lab, CAIDAS & IFI, University of W\u00a8urzburg, Germany", "University of Bucharest, Romania"]


title: Low-Rank Adaptation in Multilinear Operator Networks for Security-Preserving Incremental Learning
authors: Huu Binh Ta, Duc Nguyen, Quyen Tran, Toan Tran, Tung Pham
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24341-24350
raw_affiliation: [{"affiliation": "Qualcomm AI Research+", "city": "San Diego", "country": "USA"}]
affiliations: ["Qualcomm AI Research+"]
cities: ["San Diego"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ta_Low-Rank_Adaptation_in_Multilinear_Operator_Networks_for_Security-Preserving_Incremental_Learning_CVPR_2025_paper.html

abstract:
In security-sensitive fields, data should be encrypted to protect against unauthorized access and maintain confidentiality throughout processing. However, traditional networks like ViTs and CNNs return different results when processing original data versus its encrypted form, meaning that they require data to be decrypted, posing a security risk by exposing sensitive information. One solution for this issue is using polynomial networks, including state-of-the-art Multilinear Operator Networks, which return the same outputs given the real data and their encrypted forms under Leveled Fully Homomorphic Encryption. Nevertheless, these models are susceptible to catastrophic forgetting in incremental learning settings. Thus, this paper will present a new low-rank adaptation method combined with the Gradient Projection Memory mechanism to minimize the issue. Our proposal is compatible with Leveled Fully Homomorphic Encryption while achieving a sharp improvement in performance compared to existing models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ta_Low-Rank_Adaptation_in_Multilinear_Operator_Networks_for_Security-Preserving_Incremental_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ta_Low-Rank_Adaptation_in_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ta_2025_CVPR,
    author    = {Ta, Huu Binh and Nguyen, Duc and Tran, Quyen and Tran, Toan and Pham, Tung},
    title     = {Low-Rank Adaptation in Multilinear Operator Networks for Security-Preserving Incremental Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24341-24350}
}


title: Camera Resection from Known Line Pencils and a Radially Distorted Scanline
authors: Juan C. Dibene, Enrique Dunn
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15843-15851
raw_affiliation: [{"affiliation": "Stevens Institute of Technology", "city": "Hoboken", "country": "USA"}]
affiliations: ["Stevens Institute of Technology"]
cities: ["Hoboken"]
countries: ["USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dibene_Camera_Resection_from_Known_Line_Pencils_and_a_Radially_Distorted_CVPR_2025_paper.html

abstract:
We present a marker-based geometric estimation framework for the absolute pose of a camera by analyzing the 1D observations in a single radially distorted pixel scanline.We leverage a pair of known co-planar pencils of lines, along with lens distortion parameters, to propose an ensemble of solvers exploring the space of estimation strategies applicable to our setup.First, we present a minimal algebraic solver requiring only six measurements and yielding eight solutions, which relies on the intersection of two conics defined by one of the pencils of lines.Then, we present a unique closed-form geometric solver from seven measurements.Finally, we present an homography-based formulation amenable to linear least-squares from eight or more measurements.Our geometric framework constitutes a theoretical analysis on the minimum geometric context necessary to solve in closed form for the absolute pose of a single camera from a single radially distorted scanline.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dibene_Camera_Resection_from_Known_Line_Pencils_and_a_Radially_Distorted_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Dibene_2025_CVPR,
    author    = {Dibene, Juan C. and Dunn, Enrique},
    title     = {Camera Resection from Known Line Pencils and a Radially Distorted Scanline},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15843-15851}
}


title: ESCAPE: Equivariant Shape Completion via Anchor Point Encoding
authors: Burak Bekci, Nassir Navab, Federico Tombari, Mahdi Saleh
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6480-6489
affiliations: ["Technical University Munich (TUM)", "Google"]
cities: ["Munich", "Unknown"]
countries: ["Germany", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bekci_ESCAPE_Equivariant_Shape_Completion_via_Anchor_Point_Encoding_CVPR_2025_paper.html

abstract:
Shape completion, a crucial task in 3D computer vision, involves predicting and filling the missing regions of scanned or partially observed objects. Current methods expect known pose or canonical coordinates and do not perform well under varying rotations, limiting their real-world applicability. We introduce ESCAPE (Equivariant Shape Completion via Anchor Point Encoding), a novel framework designed to achieve rotation-equivariant shape completion. Our approach employs a distinctive encoding strategy by selecting anchor points from a shape and representing all points as a distance to all anchor points. This enables the model to capture a consistent, rotation-equivariant understanding of the object's geometry. ESCAPE leverages a transformer architecture to encode and decode the distance transformations, ensuring that generated shape completions remain accurate and equivariant under rotational transformations. Subsequently, we perform optimization to calculate the predicted shapes from the encodings. Experimental evaluations demonstrate that ESCAPE achieves robust, high-quality reconstructions across arbitrary rotations and translations, showcasing its effectiveness in real-world applications without additional pose estimation modules.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bekci_ESCAPE_Equivariant_Shape_Completion_via_Anchor_Point_Encoding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bekci_ESCAPE_Equivariant_Shape_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Bekci_2025_CVPR,
    author    = {Bekci, Burak and Navab, Nassir and Tombari, Federico and Saleh, Mahdi},
    title     = {ESCAPE: Equivariant Shape Completion via Anchor Point Encoding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6480-6489}
}

raw_affiliation:
[{"affiliation": "Technical University Munich (TUM)", "city": "Munich", "country": "Germany"}, {"affiliation": "Google", "city": "Unknown", "country": "Unknown"}]


authors: Guibiao Liao, Qing Li, Zhenyu Bao, Guoping Qiu, Kanglin Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11264-11274
cities: ["Beijing", "Shenzhen", "Nottingham"]
countries: ["China", "China", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liao_SPC-GS_Gaussian_Splatting_with_Semantic-Prompt_Consistency_for_Indoor_Open-World_Free-view_CVPR_2025_paper.html

title:
SPC-GS: Gaussian Splatting with Semantic-Prompt Consistency for Indoor Open-World Free-view Synthesis from Sparse Inputs

abstract:
3D Gaussian Splatting-based indoor open-world free-view synthesis approaches have shown significant performance with dense input images. However, they exhibit poor performance when confronted with sparse inputs, primarily due to the sparse distribution of Gaussian points and insufficient view supervision. To relieve these challenges, we propose SPC-GS, leveraging Scene-layout-based Gaussian Initialization (SGI) and Semantic-Prompt Consistency (SPC) Regularization for open-world free view synthesis with sparse inputs. Specifically, SGI provides a dense, scene-layout-based Gaussian distribution by utilizing view-changed images generated from the video generation model and view-constraint Gaussian points densification. Additionally, SPC mitigates limited view supervision by employing semantic-prompt-based consistency constraints developed by SAM2. This approach leverages available semantics from training views, serving as instructive prompts, to optimize visually overlapping regions in novel views with 2D and 3D consistency constraints. Extensive experiments demonstrate the superior performance of SPC-GS across Replica and ScanNet benchmarks. Notably, our SPC-GS achieves a 3.06 dB gain in PSNR for reconstruction quality and a 7.3% improvement in mIoU for open-world semantic segmentation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liao_SPC-GS_Gaussian_Splatting_with_Semantic-Prompt_Consistency_for_Indoor_Open-World_Free-view_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liao_SPC-GS_Gaussian_Splatting_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Liao_2025_CVPR,
    author    = {Liao, Guibiao and Li, Qing and Bao, Zhenyu and Qiu, Guoping and Liu, Kanglin},
    title     = {SPC-GS: Gaussian Splatting with Semantic-Prompt Consistency for Indoor Open-World Free-view Synthesis from Sparse Inputs},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11264-11274}
}

raw_affiliation:
[{"affiliation": "School of Electronic and Computer Engineering, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Pengcheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "University of Nottingham", "city": "Nottingham", "country": "UK"}]

affiliations:
["School of Electronic and Computer Engineering, Peking University", "Pengcheng Laboratory", "University of Nottingham"]


title: M3amba: Memory Mamba is All You Need for Whole Slide Image Classification
authors: Tingting Zheng, Kui Jiang, Yi Xiao, Sicheng Zhao, Hongxun Yao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15601-15610
cities: ["Harbin", "Harbin", "Wuhan", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zheng_M3amba_Memory_Mamba_is_All_You_Need_for_Whole_Slide_CVPR_2025_paper.html

abstract:
Multi-instance learning (MIL) has demonstrated impressive performance in whole slide image (WSI) analysis. However, existing approaches struggle with undesirable results and unbearable computational overhead due to the quadratic complexity of Transformers. Recently, Mamba has offered a feasible solution for modeling long-range dependencies with linear complexity. However, vanilla Mamba inherently suffers from contextual forgetting issues, making it ill-suited for capturing global dependencies across instances in large-scale WSIs. To address this, we propose a memory-driven Mamba network, dubbed M3amba, to fully explore the global latent relations among instances. Specifically, M3amba retains and iteratively updates historical information with a dynamic memory bank (DMB), thus overcoming the catastrophic forgetting defects of Mamba for long-term context representation. For better feature representation, M3amba involves an intra-group bidirectional Mamba (BiMamba) block to refine local interactions within groups. Meanwhile, we additionally perform cross-attention fusion to incorporate relevant historical information across groups, facilitating richer inter-group connections. The joint learning of inter- and intra-group representations with memory merits enables M3amba with a more powerful capability for achieving accurate and comprehensive WSI representation. Extensive experiments on four datasets demonstrate that M3amba outperforms the state-of-the-art by 6.2% and 7.0% in accuracy on the TCGA BRAC and TCGA Lung datasets while maintaining low computational costs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zheng_M3amba_Memory_Mamba_is_All_You_Need_for_Whole_Slide_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zheng_2025_CVPR,
    author    = {Zheng, Tingting and Jiang, Kui and Xiao, Yi and Zhao, Sicheng and Yao, Hongxun},
    title     = {M3amba: Memory Mamba is All You Need for Whole Slide Image Classification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15601-15610}
}

raw_affiliation:
[{"affiliation": "Harbin Institute of Technology", "city": "Harbin", "country": "China"}, {"affiliation": "China Mobile 5G Application Innovation Joint Research, Harbin Institute of Technology", "city": "Harbin", "country": "China"}, {"affiliation": "Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]

affiliations:
["Harbin Institute of Technology", "China Mobile 5G Application Innovation Joint Research, Harbin Institute of Technology", "Wuhan University", "Tsinghua University"]


title: Satellite to GroundScape - Large-scale Consistent Ground View Generation from Satellite Views
authors: Ningli Xu, Rongjun Qin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6068-6077
raw_affiliation: [{"affiliation": "The Ohio State University", "city": "Columbus", "country": "USA"}]
affiliations: ["The Ohio State University"]
cities: ["Columbus"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Satellite_to_GroundScape_-_Large-scale_Consistent_Ground_View_Generation_from_CVPR_2025_paper.html

abstract:
Generating consistent ground-view images from satellite imagery is challenging, primarily due to the large discrepancies in viewing angles and resolution between satellite and ground-level domains. Previous efforts mainly concentrated on single-view generation, often resulting in inconsistencies across neighboring ground views. In this work, we propose a novel cross-view synthesis approach designed to overcome these challenges by ensuring consistency across ground-view images generated from satellite views. Our method, based on a fixed latent diffusion model, introduces two conditioning modules: satellite-guided denoising, which extracts high-level scene layout to guide the denoising process, and satellite-temporal denoising, which captures camera motion to maintain consistency across multiple generated views. We further contribute a large-scale satellite-ground dataset containing over 100,000 perspective pairs to facilitate extensive ground scene or video generation. Experimental results demonstrate that our approach outperforms existing methods on perceptual and temporal metrics, achieving high photorealism and consistency in multi-view outputs. The project page is at https://gdaosu.github.io/sat2groundscape

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_Satellite_to_GroundScape_-_Large-scale_Consistent_Ground_View_Generation_from_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_Satellite_to_GroundScape_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Ningli and Qin, Rongjun},
    title     = {Satellite to GroundScape - Large-scale Consistent Ground View Generation from Satellite Views},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6068-6077}
}


title: Variance-Based Membership Inference Attacks Against Large-Scale Image Captioning Models
authors: Daniel Samira, Edan Habler, Yuval Elovici, Asaf Shabtai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9210-9219
affiliations: ["Ben-Gurion University of the Negev, Israel"]
cities: ["Beersheba"]
countries: ["Israel"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Samira_Variance-Based_Membership_Inference_Attacks_Against_Large-Scale_Image_Captioning_Models_CVPR_2025_paper.html

abstract:
The proliferation of multi-modal generative models has introduced new privacy and security challenges, especially due to the risks of memorization and unintentional disclosure of sensitive information. This paper focuses on the vulnerability of multi-modal image captioning models to membership inference attacks (MIAs). These models, which synthesize textual descriptions from visual content, could inadvertently reveal personal or proprietary data embedded in their training datasets. We explore the feasibility of MIAs in the context of such models. Specifically, our approach leverages a variance-based strategy tailored for image captioning models, utilizing only image data without knowing the corresponding caption. We introduce the means-of-variance threshold attack (MVTA) and confidence-based weakly supervised attack (C-WSA) based on the metric, means-of-variance (MV), to assess variability among vector embeddings. Our experiments demonstrate that these models are susceptible to MIAs, indicating substantial privacy risks. The effectiveness of our methods is validated through rigorous evaluations on these real-world models, confirming the practical implications of our findings.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Samira_Variance-Based_Membership_Inference_Attacks_Against_Large-Scale_Image_Captioning_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Samira_Variance-Based_Membership_Inference_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Samira_2025_CVPR,
    author    = {Samira, Daniel and Habler, Edan and Elovici, Yuval and Shabtai, Asaf},
    title     = {Variance-Based Membership Inference Attacks Against Large-Scale Image Captioning Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9210-9219}
}

raw_affiliation:
[{"affiliation": "Ben-Gurion University of the Negev, Israel", "city": "Beersheba", "country": "Israel"}]


authors: Fu Feng, Yucheng Xie, Xu Yang, Jing Wang, Xin Geng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18444-18454
cities: ["Nanjing", "Nanjing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Feng_Redefining_Creative_in_Dictionary_Towards_an_Enhanced_Semantic_Understanding_of_CVPR_2025_paper.html

title:
Redefining <Creative> in Dictionary: Towards an Enhanced Semantic Understanding of Creative Generation

abstract:
Creative remains an inherently abstract concept for both humans and diffusion models. While text-to-image (T2I) diffusion models can easily generate out-of-distribution concepts like "a blue banana", they struggle with generating combinatorial objects such as "a creative mixture that resembles a lettuce and a mantis", due to difficulties in understanding the semantic depth of "creative". Current methods rely heavily on synthesizing reference prompts or images to achieve a creative effect, typically requiring retraining for each unique creative output-a process that is computationally intensive and limits practical applications. To address this, we introduce CreTok, which brings meta-creativity to diffusion models by redefining "creative" as a new token, <CreTok>, thus enhancing models' semantic understanding for combinatorial creativity. CreTok achieves such redefinition by iteratively sampling diverse text pairs from our proposed CangJie dataset to form adaptive prompts and restrictive prompts, and then optimizing the similarity between their respective text embeddings. Extensive experiments demonstrate that <CreTok> enables the universal and direct generation of combinatorial creativity across diverse concepts without additional training, achieving state-of-the-art performance with improved text-image alignment and higher human preference ratings. Code will be made available at https://github.com/fu-feng/CreTok.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Feng_Redefining_Creative_in_Dictionary_Towards_an_Enhanced_Semantic_Understanding_of_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Feng_Redefining_Creative_in_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Feng_2025_CVPR,
    author    = {Feng, Fu and Xie, Yucheng and Yang, Xu and Wang, Jing and Geng, Xin},
    title     = {Redefining \ensuremath{<}Creative\ensuremath{>} in Dictionary: Towards an Enhanced Semantic Understanding of Creative Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18444-18454}
}

raw_affiliation:
[{"affiliation": "School of Computer Science and Engineering, Southeast University", "city": "Nanjing", "country": "China"}, {"affiliation": "Key Laboratory of New Generation Arti\ufb01cial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education", "city": "Nanjing", "country": "China"}]

affiliations:
["School of Computer Science and Engineering, Southeast University", "Key Laboratory of New Generation Arti\ufb01cial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education"]


title: FiRe: Fixed-points of Restoration Priors for Solving Inverse Problems
authors: Matthieu Terris, Ulugbek S. Kamilov, Thomas Moreau
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23185-23194
affiliations: ["Universit\u00b4e Paris-Saclay, Inria, CEA", "Washington University in St. Louis"]
cities: ["Palaiseau", "St. Louis"]
countries: ["France", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Terris_FiRe_Fixed-points_of_Restoration_Priors_for_Solving_Inverse_Problems_CVPR_2025_paper.html

abstract:
Selecting an appropriate prior to compensate for information loss due to the measurement operator is a fundamental challenge in imaging inverse problems. Implicit priors based on denoising neural networks have become central to widely-used frameworks such as Plug-and-Play (PnP) algorithms. In this work, we introduce Fixed-points of Restoration (FiRe) priors as a new framework for expanding the notion of priors in PnP to general restoration models beyond traditional denoising models. The key insight behind FiRe is that smooth images emerge as fixed points of the composition of a degradation operator with the corresponding restoration model. This enables us to derive an explicit formula for our implicit prior by quantifying invariance of images under this composite operation. Adopting this fixed-point perspective, we show how various restoration networks can effectively serve as priors for solving inverse problems. The FiRe framework further enables ensemble-like combinations of multiple restoration models as well as acquisition-informed restoration networks, all within a unified optimization approach. Experimental results validate the effectiveness of FiRe across various inverse problems, establishing a new paradigm for incorporating pretrained restoration models into PnP-like algorithms. Code available at https://github.com/matthieutrs/fire.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Terris_FiRe_Fixed-points_of_Restoration_Priors_for_Solving_Inverse_Problems_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Terris_FiRe_Fixed-points_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Terris_2025_CVPR,
    author    = {Terris, Matthieu and Kamilov, Ulugbek S. and Moreau, Thomas},
    title     = {FiRe: Fixed-points of Restoration Priors for Solving Inverse Problems},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23185-23194}
}

raw_affiliation:
[{"affiliation": "Universit\u00b4e Paris-Saclay, Inria, CEA", "city": "Palaiseau", "country": "France"}, {"affiliation": "Washington University in St. Louis", "city": "St. Louis", "country": "USA"}]


title: Learning Dynamic Collaborative Network for Semi-supervised 3D Vessel Segmentation
authors: Jiao Xu, Xin Chen, Lihe Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10445-10454
affiliations: ["Dalian University of Technology", "City University of Hong Kong"]
cities: ["Dalian", "Hong Kong"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Learning_Dynamic_Collaborative_Network_for_Semi-supervised_3D_Vessel_Segmentation_CVPR_2025_paper.html

abstract:
In this paper, we present a new dynamic collaborative network for semi-supervised 3D vessel segmentation, termed DiCo. Conventional mean teacher (MT) methods typically employ a static approach, where the roles of the teacher and student models are fixed. However, due to the complexity of 3D vessel data, the teacher model may not always outperform the student model, leading to cognitive biases that can limit performance. To address this issue, we propose a dynamic collaborative network that allows the two models to dynamically switch their teacher-student roles. Additionally, we introduce a multi-view integration module to capture various perspectives of the inputs, mirroring the way doctors conduct medical analysis. We also incorporate adversarial supervision to constrain the shape of the segmented vessels in unlabeled data. In this process, the 3D volume is projected into 2D views to mitigate the impact of label inconsistencies. Experiments demonstrate that our DiCo method sets new state-of-the-art performance on three 3D vessel segmentation benchmarks.  The code repository address is https://github.com/xujiaommcome/DiCo.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_Learning_Dynamic_Collaborative_Network_for_Semi-supervised_3D_Vessel_Segmentation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Jiao and Chen, Xin and Zhang, Lihe},
    title     = {Learning Dynamic Collaborative Network for Semi-supervised 3D Vessel Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10445-10454}
}

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


title: Temporal Alignment-Free Video Matching for Few-shot Action Recognition
authors: SuBeen Lee, WonJun Moon, Hyun Seok Seong, Jae-Pil Heo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5412-5421
raw_affiliation: [{"affiliation": "Sungkyunkwan University", "city": "Seoul", "country": "South Korea"}]
affiliations: ["Sungkyunkwan University"]
cities: ["Seoul"]
countries: ["South Korea"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Temporal_Alignment-Free_Video_Matching_for_Few-shot_Action_Recognition_CVPR_2025_paper.html

abstract:
Few-Shot Action Recognition (FSAR) aims to train a model with only a few labeled video instances. A key challenge in FSAR is handling divergent narrative trajectories for precise video matching. While the frame- and tuple-level alignment approaches have been promising, their methods heavily rely on pre-defined and length-dependent alignment units (e.g., frames or tuples), which limits flexibility for actions of varying lengths and speeds. In this work, we introduce a novel TEmporal Alignment-free Matching (TEAM) approach, which eliminates the need for temporal units in action representation and brute-force alignment during matching. Specifically, TEAM represents each video with a fixed set of pattern tokens that capture globally discriminative clues within the video instance regardless of action length or speed, ensuring its flexibility. Furthermore, TEAM is inherently efficient, using token-wise comparisons to measure similarity between videos, unlike existing methods that rely on pairwise comparisons for temporal alignment. Additionally, we propose an adaptation process that identifies and removes common information across classes, establishing clear boundaries even between novel categories. Extensive experiments demonstrate the effectiveness of TEAM. Codes are available at github.com/leesb7426/TEAM.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_Temporal_Alignment-Free_Video_Matching_for_Few-shot_Action_Recognition_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_Temporal_Alignment-Free_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, SuBeen and Moon, WonJun and Seong, Hyun Seok and Heo, Jae-Pil},
    title     = {Temporal Alignment-Free Video Matching for Few-shot Action Recognition},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5412-5421}
}


title: OSLoPrompt: Bridging Low-Supervision Challenges and Open-Set Domain Generalization in CLIP
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10110-10120
cities: ["Mumbai", "Jaipur", "Abu Dhabi"]
countries: ["India", "India", "UAE"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/C_OSLoPrompt_Bridging_Low-Supervision_Challenges_and_Open-Set_Domain_Generalization_in_CLIP_CVPR_2025_paper.html

authors:
Mohamad Hassan N C, Divyam Gupta, Mainak Singha, Sai Bhargav Rongali, Ankit Jha, Muhammad Haris Khan, Biplab Banerjee

abstract:
We introduce Low-Shot Open-Set Domain Generalization (LSOSDG), a novel paradigm unifying low-shot learning with open-set domain generalization (ODG). While prompt-based methods using models like CLIP have advanced DG, they falter in low-data regimes (e.g., 1-shot) and lack precision in detecting open-set samples with fine-grained semantics related to training classes. To address these challenges, we propose OSLoPrompt, an advanced prompt-learning framework for CLIP with two core innovations. First, to manage limited supervision across source domains and improve DG, we introduce a domain-agnostic prompt-learning mechanism that integrates adaptable domain-specific cues and visually guided semantic attributes through a novel cross-attention module, besides being supported by learnable domain- and class-generic visual prompts to enhance cross-modal adaptability. Second, to improve outlier rejection during inference, we classify unfamiliar samples as "unknown" and train specialized prompts with systematically synthesized pseudo-open samples that maintain fine-grained relationships to known classes, generated through a targeted query strategy with off-the-shelf foundation models. This strategy enhances feature learning, enabling our model to detect open samples with varied granularity more effectively. Extensive evaluations across five benchmarks demonstrate that OSLoPrompt establishes a new state-of-the-art in LSOSDG, significantly outperforming existing methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/C_OSLoPrompt_Bridging_Low-Supervision_Challenges_and_Open-Set_Domain_Generalization_in_CLIP_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/C_OSLoPrompt_Bridging_Low-Supervision_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{C_2025_CVPR,
    author    = {C, Mohamad Hassan N and Gupta, Divyam and Singha, Mainak and Rongali, Sai Bhargav and Jha, Ankit and Khan, Muhammad Haris and Banerjee, Biplab},
    title     = {OSLoPrompt: Bridging Low-Supervision Challenges and Open-Set Domain Generalization in CLIP},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10110-10120}
}

raw_affiliation:
[{"affiliation": "Indian Institute of T echnology Bombay", "city": "Mumbai", "country": "India"}, {"affiliation": "The LNM Institute of Information T echnology (LNMIIT)", "city": "Jaipur", "country": "India"}, {"affiliation": "Mohamed Bin Zayed University of Arti\ufb01cial Intelligence", "city": "Abu Dhabi", "country": "UAE"}]

affiliations:
["Indian Institute of T echnology Bombay", "The LNM Institute of Information T echnology (LNMIIT)", "Mohamed Bin Zayed University of Arti\ufb01cial Intelligence"]


title: Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection
authors: Jia Guo, Shuai Lu, Weihang Zhang, Fang Chen, Huiqi Li, Hongen Liao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20405-20415
cities: ["Beijing", "Beijing", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_Dinomaly_The_Less_Is_More_Philosophy_in_Multi-Class_Unsupervised_Anomaly_CVPR_2025_paper.html

abstract:
Recent studies highlighted a practical setting of unsupervised anomaly detection (UAD) that builds a unified model for multi-class images. Despite various advancements addressing this challenging task, the detection performance under the multi-class setting still lags far behind state-of-the-art class-separated models. Our research aims to bridge this substantial performance gap. In this paper, we present Dinomaly, a minimalist reconstruction-based anomaly detection framework that harnesses pure Transformer architectures without relying on complex designs, additional modules, or specialized tricks. Given this powerful framework consisting of only Attentions and MLPs, we found four simple components that are essential to multi-class anomaly detection: (1) Scalable foundation Transformers that extracts universal and discriminative features, (2) Noisy Bottleneck where pre-existing Dropouts do all the noise injection tricks, (3) Linear Attention that naturally cannot focus, and (4) Loose Reconstruction that does not force layer-to-layer and point-by-point reconstruction. Extensive experiments are conducted across popular anomaly detection benchmarks including MVTec-AD, VisA, and Real-IAD. Our proposed Dinomaly achieves impressive image-level AUROC of 99.6%, 98.7%, and 89.3% on the three datasets respectively, which is not only superior to state-of-the-art multi-class UAD methods, but also achieves the most advanced class-separated UAD records.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_Dinomaly_The_Less_Is_More_Philosophy_in_Multi-Class_Unsupervised_Anomaly_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_Dinomaly_The_Less_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Jia and Lu, Shuai and Zhang, Weihang and Chen, Fang and Li, Huiqi and Liao, Hongen},
    title     = {Dinomaly: The Less Is More Philosophy in Multi-Class Unsupervised Anomaly Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20405-20415}
}

raw_affiliation:
[{"affiliation": "School of Biomedical Engineering, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing Institute of Technology", "city": "Beijing", "country": "China"}, {"affiliation": "School of Biomedical Engineering, Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]

affiliations:
["School of Biomedical Engineering, Tsinghua University", "Beijing Institute of Technology", "School of Biomedical Engineering, Shanghai Jiao Tong University"]


title: VLog: Video-Language Models by Generative Retrieval of Narration Vocabulary
authors: Kevin Qinghong Lin, Mike Zheng Shou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3218-3228
affiliations: ["Show Lab, National University of Singapore"]
cities: ["Singapore"]
countries: ["Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_VLog_Video-Language_Models_by_Generative_Retrieval_of_Narration_Vocabulary_CVPR_2025_paper.html

abstract:
Human daily activities can be concisely narrated as sequences of routine events (e.g., turning off an alarm) in video streams, forming an event vocabulary. Motivated by this, we introduce **VLog**, a novel video understanding framework that defines video narrations as a vocabulary, going beyond the typical subword vocabularies in existing generative video-language models. Built on the lightweight language model GPT-2, **VLog** features three key innovations:1. **A Generative Retrieval Model**     Marrying the language model's complex reasoning capabilities with contrastive retrieval's efficient similarity search.2. **A Hierarchical Vocabulary**     Derived from large-scale video narrations using our narration pair encoding algorithm, enabling efficient indexing of specific events (e.g., cutting a tomato) by identifying broader scenarios (e.g., kitchen) with expressive postfixes (e.g., by the left hand).3. **A Vocabulary Update Strategy**     Leveraging generative models to extend the vocabulary for novel events encountered during inference.To validate our approach, we introduce **VidCab-Eval**, a development set requiring concise narrations with reasoning relationships (e.g., before and after). Experiments on **EgoSchema**, **COIN**, and **HiREST** further demonstrate the effectiveness of **VLog**, highlighting its ability to generate concise, contextually accurate, and efficient narrations. This offers a novel perspective on video understanding.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_VLog_Video-Language_Models_by_Generative_Retrieval_of_Narration_Vocabulary_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_VLog_Video-Language_Models_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Kevin Qinghong and Shou, Mike Zheng},
    title     = {VLog: Video-Language Models by Generative Retrieval of Narration Vocabulary},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3218-3228}
}

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


title: CoMBO: Conflict Mitigation via Branched Optimization for Class Incremental Segmentation
authors: Kai Fang, Anqi Zhang, Guangyu Gao, Jianbo Jiao, Chi Harold Liu, Yunchao Wei
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25667-25676
affiliations: ["Beijing Institute of Technology", "University of Birmingham", "Beijing Jiaotong University"]
cities: ["Beijing", "Birmingham", "Beijing"]
countries: ["China", "UK", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fang_CoMBO_Conflict_Mitigation_via_Branched_Optimization_for_Class_Incremental_Segmentation_CVPR_2025_paper.html

abstract:
Effective Class Incremental Segmentation (CIS) requires simultaneously mitigating catastrophic forgetting and ensuring sufficient plasticity to integrate new classes. The inherent conflict above often leads to a back-and-forth, which turns the objective into finding the balance between the performance of previous (old) and incremental (new) classes. To address this conflict, we introduce a novel approach, Conflict Mitigation via Branched Optimization (CoMBO). Within this approach, we present the Query Conflict Reduction module, designed to explicitly refine queries for new classes through lightweight, class-specific adapters. This module provides an additional branch for the acquisition of new classes while preserving the original queries for distillation. Moreover, we develop two strategies to further mitigate the conflict following the branched structure, i.e., the Half-Learning Half-Distillation (HDHL) over classification probabilities, and the Importance-Based Knowledge Distillation (IKD) over query features. HDHL selectively engages in learning for classification probabilities of queries that match the ground truth of new classes, while aligning unmatched ones to the corresponding old probabilities, thus ensuring retention of old knowledge while absorbing new classes via learning negative samples. Meanwhile, IKD assesses the importance of queries based on their matching degree to old classes, prioritizing the distillation of important features and allowing less critical features to evolve. Extensive experiments in Class Incremental Panoptic and Semantic Segmentation settings have demonstrated the superior performance of CoMBO. Project page: https://guangyu-ryan.github.io/CoMBO.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fang_CoMBO_Conflict_Mitigation_via_Branched_Optimization_for_Class_Incremental_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fang_CoMBO_Conflict_Mitigation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Fang_2025_CVPR,
    author    = {Fang, Kai and Zhang, Anqi and Gao, Guangyu and Jiao, Jianbo and Liu, Chi Harold and Wei, Yunchao},
    title     = {CoMBO: Conflict Mitigation via Branched Optimization for Class Incremental Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25667-25676}
}

raw_affiliation:
[{"affiliation": "Beijing Institute of Technology", "city": "Beijing", "country": "China"}, {"affiliation": "University of Birmingham", "city": "Birmingham", "country": "UK"}, {"affiliation": "Beijing Jiaotong University", "city": "Beijing", "country": "China"}]


title: Forensics-Bench: A Comprehensive Forgery Detection Benchmark Suite for Large Vision Language Models
authors: Jin Wang, Chenghui Lv, Xian Li, Shichao Dong, Huadong Li, Kelu Yao, Chao Li, Wenqi Shao, Ping Luo
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4233-4245
cities: ["Hong Kong", "Shanghai", "Shanghai", "Hangzhou", "Hangzhou", "Hangzhou", "Beijing", "Unknown"]
countries: ["China", "China", "China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Forensics-Bench_A_Comprehensive_Forgery_Detection_Benchmark_Suite_for_Large_Vision_CVPR_2025_paper.html

abstract:
Recently, the rapid development of AIGC has significantly boosted the diversities of fake media spread in the Internet, posing unprecedented threats to social security, politics, law, and etc.To detect the ever-increasingly **diverse** malicious fake media in the new era of AIGC, recent studies have proposed to exploit Large Vision Language Models (LVLMs) to design **robust** forgery detectors due to their impressive performance on a **wide** range of multimodal tasks.However, it still lacks a comprehensive benchmark designed to comprehensively assess LVLMs' discerning capabilities on forgery media.To fill this gap, we present Forensics-Bench, a new forgery detection evaluation benchmark suite to assess LVLMs across massive forgery detection tasks, requiring comprehensive recognition, location and reasoning capabilities on diverse forgeries.Forensics-Bench comprises 63,292 meticulously curated multi-choicevisual questions, covering 112 unique forgery detection types from 5 perspectives: forgery semantics, forgery modalities, forgery tasks, forgery types and forgery models.We conduct thorough evaluations on 22 open-sourced LVLMs and 3 proprietary models GPT-4o, Gemini 1.5 Pro, and Claude 3.5 Sonnet, highlighting the significant challenges of comprehensive forgery detection posed by Forensics-Bench.We anticipate that Forensics-Bench will motivate the community to advance the frontier of LVLMs, striving for all-around forgery detectors in the era of AIGC.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Forensics-Bench_A_Comprehensive_Forgery_Detection_Benchmark_Suite_for_Large_Vision_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Forensics-Bench_A_Comprehensive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Jin and Lv, Chenghui and Li, Xian and Dong, Shichao and Li, Huadong and Yao, Kelu and Li, Chao and Shao, Wenqi and Luo, Ping},
    title     = {Forensics-Bench: A Comprehensive Forgery Detection Benchmark Suite for Large Vision Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4233-4245}
}

raw_affiliation:
[{"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "HKU Shanghai Intelligent Computing Research Center", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Zhejiang Laboratory", "city": "Hangzhou", "country": "China"}, {"affiliation": "Hangzhou Institute for Advanced Study", "city": "Hangzhou", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Alibaba", "city": "Beijing", "country": "China"}, {"affiliation": "MEGVII Technology", "city": "Unknown", "country": "China"}]

affiliations:
["The University of Hong Kong", "HKU Shanghai Intelligent Computing Research Center", "Shanghai AI Laboratory", "Zhejiang Laboratory", "Hangzhou Institute for Advanced Study", "Zhejiang University", "Alibaba", "MEGVII Technology"]


authors: Feifei Li, Mi Zhang, Yiming Sun, Min Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13252-13262
affiliations: ["School of Computer Science, Fudan University"]
cities: ["Shanghai"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Detect-and-Guide_Self-regulation_of_Diffusion_Models_for_Safe_Text-to-Image_Generation_via_CVPR_2025_paper.html

title:
Detect-and-Guide: Self-regulation of Diffusion Models for Safe Text-to-Image Generation via Guideline Token Optimization

abstract:
Text-to-image diffusion models have achieved state-of-the-art results in synthesis tasks; however, there is a growing concern about their potential misuse in creating harmful content. To mitigate these risks, post-hoc model intervention techniques, such as concept unlearning and safety guidance, have been developed. However, fine-tuning model weights or adapting the hidden states of the diffusion model operates in an uninterpretable way, making it unclear which part of the intermediate variables is responsible for unsafe generation. These interventions severely affect the sampling trajectory when erasing harmful concepts from complex, multi-concept prompts, thus hindering their practical use in real-world settings. In this work, we propose the safe generation framework Detect-and-Guide (DAG), leveraging the internal knowledge of diffusion models to perform self-diagnosis and fine-grained self-regulation during the sampling process. DAG first detects harmful concepts from noisy latents using refined cross-attention maps of optimized tokens, then applies safety guidance with adaptive strength and editing regions to negate unsafe generation. The optimization only requires a small annotated dataset and can provide precise detection maps with generalizability and concept specificity. Moreover, DAG does not require fine-tuning of diffusion models, and therefore introduces no loss to their generation diversity. Experiments on erasing sexual content show that DAG achieves state-of-the-art safe generation performance, balancing harmfulness mitigation and text-following performance on multi-concept real-world prompts.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Detect-and-Guide_Self-regulation_of_Diffusion_Models_for_Safe_Text-to-Image_Generation_via_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Detect-and-Guide_Self-regulation_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Feifei and Zhang, Mi and Sun, Yiming and Yang, Min},
    title     = {Detect-and-Guide: Self-regulation of Diffusion Models for Safe Text-to-Image Generation via Guideline Token Optimization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13252-13262}
}

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


title: MirrorVerse: Pushing Diffusion Models to Realistically Reflect the World
authors: Ankit Dhiman, Manan Shah, R Venkatesh Babu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11239-11249
affiliations: ["Vision and AI Lab, IISc Bangalore", "Samsung R & D Institute India - Bangalore"]
cities: ["Bangalore", "Bangalore"]
countries: ["India", "India"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dhiman_MirrorVerse_Pushing_Diffusion_Models_to_Realistically_Reflect_the_World_CVPR_2025_paper.html

abstract:
Diffusion models have become central to various image editing tasks, yet they often fail to fully adhere to physical laws, particularly with effects like shadows, reflections, and occlusions. In this work, we address the challenge of generating photorealistic mirror reflections using diffusion-based generative models. Despite extensive training data, existing diffusion models frequently overlook the nuanced details crucial to authentic mirror reflections. Recent approaches have attempted to resolve this by creating synthetic datasets and framing reflection generation as an inpainting task; however, they struggle to generalize across different object orientations and positions relative to the mirror. Our method overcomes these limitations by introducing key augmentations into the synthetic data pipeline: (1) random object positioning, (2) randomized rotations, and (3) grounding of objects, significantly enhancing generalization across poses and placements. To further address spatial relationships and occlusions in scenes with multiple objects, we implement a strategy to pair objects during dataset generation, resulting in a dataset robust enough to handle these complex scenarios. Achieving generalization to real-world scenes remains a challenge, so we introduce a three-stage training curriculum to develop the MirrorFusion 2.0 model, aimed at improving real-world performance. We provide extensive qualitative and quantitative evaluations to support our approach. The project page is available at: https://mirror-verse.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dhiman_MirrorVerse_Pushing_Diffusion_Models_to_Realistically_Reflect_the_World_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dhiman_MirrorVerse_Pushing_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dhiman_2025_CVPR,
    author    = {Dhiman, Ankit and Shah, Manan and Babu, R Venkatesh},
    title     = {MirrorVerse: Pushing Diffusion Models to Realistically Reflect the World},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11239-11249}
}

raw_affiliation:
[{"affiliation": "Vision and AI Lab, IISc Bangalore", "city": "Bangalore", "country": "India"}, {"affiliation": "Samsung R & D Institute India - Bangalore", "city": "Bangalore", "country": "India"}]


authors: Dongrui Dai, Yuxiang Xing
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16498-16507
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dai_EAP-GS_Efficient_Augmentation_of_Pointcloud_for_3D_Gaussian_Splatting_in_CVPR_2025_paper.html

title:
EAP-GS: Efficient Augmentation of Pointcloud for 3D Gaussian Splatting in Few-shot Scene Reconstruction

abstract:
3D Gaussian splatting (3DGS) has shown impressive performance in 3D scene reconstruction. However, it suffers from severe degradation when the number of training views is limited, resulting in blur and floaters. Many works have been devoted to standardize the optimization process of 3DGS through regularization techniques. However, we identify that inadequate initialization is a critical issue overlooked by current studies. To address this, we propose EAP-GS, a method to enhance initialization for fast, accurate, and stable few-shot scene reconstruction. Specifically, we introduce an Attentional Pointcloud Augmentation (APA) technique, which retains two-view tracks as an option for pointcloud generation. Additionally, the scene complexity is used to determine the required density distribution, thereby constructing a better pointcloud. We implemented APA by extending Structure-From-Motion (SFM) to focus on pointcloud generation in regions with complex structure but sparse pointcloud distribution, which significantly increases the number of valuable points and effectively harmonizes the density distribution. A better pointcloud leads to more accurate scene geometry and mitigates local overfitting during reconstruction stage. Furthermore, our APA can be framed as a modular augmentation to existing methods with minimal overhead. Experimental results from various indoor and outdoor scenes demonstrate that the proposed EAP-GS achieves outstanding scene reconstruction performance and surpasses state-of-the-art methods. Project page: https://osierddr.github.io/eapgs/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Dai_EAP-GS_Efficient_Augmentation_of_Pointcloud_for_3D_Gaussian_Splatting_in_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Dai_EAP-GS_Efficient_Augmentation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Dai_2025_CVPR,
    author    = {Dai, Dongrui and Xing, Yuxiang},
    title     = {EAP-GS: Efficient Augmentation of Pointcloud for 3D Gaussian Splatting in Few-shot Scene Reconstruction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16498-16507}
}

raw_affiliation:
[{"affiliation": "Department of Engineering Physics, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Key Laboratory of Particle and Radiation Imaging (Tsinghua University), Ministry of Education", "city": "Beijing", "country": "China"}]

affiliations:
["Department of Engineering Physics, Tsinghua University", "Key Laboratory of Particle and Radiation Imaging (Tsinghua University), Ministry of Education"]


title: Empowering Large Language Models with 3D Situation Awareness
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19435-19445
affiliations: ["FNii-Shenzhen, CUHKSZ", "SSE, CUHKSZ", "IHPC, A*STAR, Singapore", "HKU", "SYSU"]
cities: ["Shenzhen", "Shenzhen", "Singapore", "Hong Kong", "Guangzhou"]
countries: ["China", "China", "Singapore", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yuan_Empowering_Large_Language_Models_with_3D_Situation_Awareness_CVPR_2025_paper.html

authors:
Zhihao Yuan, Yibo Peng, Jinke Ren, Yinghong Liao, Yatong Han, Chun-Mei Feng, Hengshuang Zhao, Guanbin Li, Shuguang Cui, Zhen Li

abstract:
Driven by the great success of Large Language Models (LLMs) in the 2D image domain, their applications in 3D scene understanding has emerged as a new trend. A key difference between 3D and 2D is that the situation of an egocentric observer in 3D scenes can change, resulting in different descriptions (e.g., "left" or "right"). However, current LLM-based methods overlook the egocentric perspective and simply use datasets from a global viewpoint. To address this issue, we propose a novel approach to automatically generate a situation-aware dataset by leveraging the scanning trajectory during data collection and utilizing Vision-Language Models (VLMs) to produce high-quality captions and question-answer pairs. Furthermore, we introduce a situation grounding module to explicitly predict the position and orientation of observer's viewpoint, thereby enabling LLMs to ground situation description in 3D scenes. We evaluate our approach on several benchmarks, demonstrating that our method effectively enhances the 3D situational awareness of LLMs while significantly expanding existing datasets and reducing manual effort.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yuan_Empowering_Large_Language_Models_with_3D_Situation_Awareness_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yuan_Empowering_Large_Language_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yuan_2025_CVPR,
    author    = {Yuan, Zhihao and Peng, Yibo and Ren, Jinke and Liao, Yinghong and Han, Yatong and Feng, Chun-Mei and Zhao, Hengshuang and Li, Guanbin and Cui, Shuguang and Li, Zhen},
    title     = {Empowering Large Language Models with 3D Situation Awareness},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19435-19445}
}

raw_affiliation:
[{"affiliation": "FNii-Shenzhen, CUHKSZ", "city": "Shenzhen", "country": "China"}, {"affiliation": "SSE, CUHKSZ", "city": "Shenzhen", "country": "China"}, {"affiliation": "IHPC, A*STAR, Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "HKU", "city": "Hong Kong", "country": "China"}, {"affiliation": "SYSU", "city": "Guangzhou", "country": "China"}]


authors: Tai D. Nguyen, Aref Azizpour, Matthew C. Stamm
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3040-3050
raw_affiliation: [{"affiliation": "Drexel University", "city": "Philadelphia", "country": "USA"}]
affiliations: ["Drexel University"]
cities: ["Philadelphia"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nguyen_Forensic_Self-Descriptions_Are_All_You_Need_for_Zero-Shot_Detection_Open-Set_CVPR_2025_paper.html

title:
Forensic Self-Descriptions Are All You Need for Zero-Shot Detection, Open-Set Source Attribution, and Clustering of AI-generated Images

abstract:
The emergence of advanced AI-based tools to generate realistic images poses significant challenges for forensic detection and source attribution, especially as new generative techniques appear rapidly. Traditional methods often fail to generalize to unseen generators due to reliance on features specific to known sources during training. To address this problem, we propose a novel approach that explicitly models forensic microstructures--subtle, pixel-level patterns unique to the image creation process. Using only real images in a self-supervised manner, we learn a set of diverse predictive filters to extract residuals that capture different aspects of these microstructures. By jointly modeling these residuals across multiple scales, we obtain a compact model whose parameters constitute a unique forensic self-description for each image. This self-description enables us to perform zero-shot detection of synthetic images, open-set source attribution of images, and clustering based on source without prior knowledge. Extensive experiments demonstrate that our method achieves superior accuracy and adaptability compared to competing techniques, advancing the state of the art in synthetic media forensics.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nguyen_Forensic_Self-Descriptions_Are_All_You_Need_for_Zero-Shot_Detection_Open-Set_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nguyen_Forensic_Self-Descriptions_Are_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Nguyen_2025_CVPR,
    author    = {Nguyen, Tai D. and Azizpour, Aref and Stamm, Matthew C.},
    title     = {Forensic Self-Descriptions Are All You Need for Zero-Shot Detection, Open-Set Source Attribution, and Clustering of AI-generated Images},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3040-3050}
}


title: EchoTraffic: Enhancing Traffic Anomaly Understanding with Audio-Visual Insights
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19098-19108
cities: ["Hong Kong", "Guangzhou", "Tianjin", "Shanghai"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xing_EchoTraffic_Enhancing_Traffic_Anomaly_Understanding_with_Audio-Visual_Insights_CVPR_2025_paper.html

authors:
Zhenghao Xing, Hao Chen, Binzhu Xie, Jiaqi Xu, Ziyu Guo, Xuemiao Xu, Jianye Hao, Chi-Wing Fu, Xiaowei Hu, Pheng-Ann Heng

abstract:
Traffic Anomaly Understanding (TAU) is essential for improving public safety and transportation efficiency by enabling timely detection and response to incidents. Beyond existing methods, which rely largely on visual data, we propose to consider audio cues, a valuable source that offers strong hints to anomaly scenarios such as crashes and honking. Our contributions are twofold. First, we compile AV-TAU, the first large-scale audio-visual dataset for TAU, providing 29,865 traffic anomaly videos and 149,325 Q&A pairs, while supporting five essential TAU tasks. Second, we develop EchoTraffic, a multimodal LLM that integrates audio and visual data for TAU, through our audio-insight frame selector and dynamic connector to effectively extract crucial audio cues for anomaly understanding with a two-phase training framework. Experimental results on AV-TAU manifest that EchoTraffic sets a new SOTA performance in TAU, outperforming the existing multimodal LLMs. Our contributions, including AV-TAU and EchoTraffic, pave a new direction for multimodal TAU.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xing_EchoTraffic_Enhancing_Traffic_Anomaly_Understanding_with_Audio-Visual_Insights_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xing_EchoTraffic_Enhancing_Traffic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xing_2025_CVPR,
    author    = {Xing, Zhenghao and Chen, Hao and Xie, Binzhu and Xu, Jiaqi and Guo, Ziyu and Xu, Xuemiao and Hao, Jianye and Fu, Chi-Wing and Hu, Xiaowei and Heng, Pheng-Ann},
    title     = {EchoTraffic: Enhancing Traffic Anomaly Understanding with Audio-Visual Insights},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19098-19108}
}

raw_affiliation:
[{"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Tianjin University", "city": "Tianjin", "country": "China"}, {"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}]

affiliations:
["The Chinese University of Hong Kong", "South China University of Technology", "Tianjin University", "Shanghai Artificial Intelligence Laboratory"]


authors: Jingqiu Zhou, Lue Fan, Linjiang Huang, Xiaoyu Shi, Si Liu, Zhaoxiang Zhang, Hongsheng Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1549-1558
cities: ["Hong Kong", "Hong Kong", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_FlexDrive_Toward_Trajectory_Flexibility_in_Driving_Scene_Gaussian_Splatting_Reconstruction_CVPR_2025_paper.html

title:
FlexDrive: Toward Trajectory Flexibility in Driving Scene Gaussian Splatting Reconstruction and Rendering

abstract:
Driving scene reconstruction and rendering have advanced significantly using the 3D Gaussian Splatting.However, most prior research has focused on the rendering quality along a pre-recorded vehicle path and struggles to generalize to out-of-path viewpoints, which is caused by the lack of high-quality supervision in those out-of-path views. To address this issue, we introduce an Inverse View Warping technique to create compact and high-quality images as supervision for the reconstruction of the out-of-path views, enabling high-quality rendering results for those views.For accurate and robust inverse view warping, a depth bootstrap strategy is proposed to obtain on-the-fly dense depth maps during the optimization process, overcoming the sparsity and incompleteness of LiDAR depth data.Our method achieves superior in-path and out-of-path reconstruction and rendering performance on the widely used Waymo Open dataset.In addition, a simulator-based benchmark is proposed to obtain the out-of-path ground truth and quantitatively evaluate the performance of out-of-path rendering, where our method outperforms previous methods by a significant margin.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_FlexDrive_Toward_Trajectory_Flexibility_in_Driving_Scene_Gaussian_Splatting_Reconstruction_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Jingqiu and Fan, Lue and Huang, Linjiang and Shi, Xiaoyu and Liu, Si and Zhang, Zhaoxiang and Li, Hongsheng},
    title     = {FlexDrive: Toward Trajectory Flexibility in Driving Scene Gaussian Splatting Reconstruction and Rendering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1549-1558}
}

raw_affiliation:
[{"affiliation": "Multimedia Laboratory, The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Centre for Perceptual and Interactive Intelligence, Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Beihang University", "city": "Beijing", "country": "China"}]

affiliations:
["Multimedia Laboratory, The Chinese University of Hong Kong", "Centre for Perceptual and Interactive Intelligence, Hong Kong", "Institute of Automation, Chinese Academy of Sciences", "Beihang University"]


authors: Yingji Zhong, Zhihao Li, Dave Zhenyu Chen, Lanqing Hong, Dan Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6133-6143
affiliations: ["The Hong Kong University of Science and Technology", "Huawei Noah\u2019s Ark Lab"]
cities: ["Hong Kong", "Shenzhen"]
countries: ["China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhong_Taming_Video_Diffusion_Prior_with_Scene-Grounding_Guidance_for_3D_Gaussian_CVPR_2025_paper.html

title:
Taming Video Diffusion Prior with Scene-Grounding Guidance for 3D Gaussian Splatting from Sparse Inputs

abstract:
Despite recent successes in novel view synthesis using 3D Gaussian Splatting (3DGS), modeling scenes with sparse inputs remains a challenge. In this work, we address two critical yet overlooked issues in real-world sparse-input modeling: extrapolation and occlusion. To tackle these issues, we propose to use a reconstruction by generation pipeline that leverages learned priors from video diffusion models to provide plausible interpretations for regions outside the field of view or occluded. However, the generated sequences exhibit inconsistencies that do not fully benefit subsequent 3DGS modeling. To address the challenge of inconsistency, we introduce a novel scene-grounding guidance based on rendered sequences from an optimized 3DGS, which tames the diffusion model to generate consistent sequences. This guidance is training-free and does not require any fine-tuning of the diffusion model. To facilitate holistic scene modeling, we also propose a trajectory initialization method. It effectively identifies regions that are outside the field of view and occluded. We further design a scheme tailored for 3DGS optimization with generated sequences. Experiments demonstrate that our method significantly improves upon the baseline and achieves state-of-the-art performance on challenging benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhong_Taming_Video_Diffusion_Prior_with_Scene-Grounding_Guidance_for_3D_Gaussian_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhong_Taming_Video_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhong_2025_CVPR,
    author    = {Zhong, Yingji and Li, Zhihao and Chen, Dave Zhenyu and Hong, Lanqing and Xu, Dan},
    title     = {Taming Video Diffusion Prior with Scene-Grounding Guidance for 3D Gaussian Splatting from Sparse Inputs},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6133-6143}
}

raw_affiliation:
[{"affiliation": "The Hong Kong University of Science and Technology", "city": "Hong Kong", "country": "China"}, {"affiliation": "Huawei Noah\u2019s Ark Lab", "city": "Shenzhen", "country": "China"}]


title: Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20841-20851
cities: ["Chengdu", "Shanghai", "Urumqi", "Shenzhen"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cheng_Interactive_Medical_Image_Segmentation_A_Benchmark_Dataset_and_Baseline_CVPR_2025_paper.html

authors:
Junlong Cheng, Bin Fu, Jin Ye, Guoan Wang, Tianbin Li, Haoyu Wang, Ruoyu Li, He Yao, Junren Cheng, Jingwen Li, Yanzhou Su, Min Zhu, Junjun He

abstract:
Interactive Medical Image Segmentation (IMIS) has long been constrained by the limited availability of large-scale, diverse, and densely annotated datasets, which hinders model generalization and consistent evaluation across different models. In this paper, we introduce the IMed-361M benchmark dataset, a significant advancement in general IMIS research. First, we collect and standardize over 6.4 million medical images and their corresponding ground truth masks from multiple data sources. Then, leveraging the strong object recognition capabilities of a vision foundational model, we automatically generated dense interactive masks for each image and ensured their quality through rigorous quality control and granularity management. Unlike previous datasets, which are limited by specific modalities or sparse annotations, IMed-361M spans 14 modalities and 204 segmentation targets, totaling 361 million masks--an average of 56 masks per image. Finally, we developed an IMIS baseline network on this dataset that supports high-quality mask generation through interactive inputs, including clicks, bounding boxes, text prompts, and their combinations. We evaluate its performance on medical image segmentation tasks from multiple perspectives, demonstrating superior accuracy and scalability compared to existing interactive segmentation models. To facilitate research on foundational models in medical computer vision, we release the IMed-361M and model at https://github.com/uni-medical/IMIS-Bench.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cheng_Interactive_Medical_Image_Segmentation_A_Benchmark_Dataset_and_Baseline_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cheng_Interactive_Medical_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cheng_2025_CVPR,
    author    = {Cheng, Junlong and Fu, Bin and Ye, Jin and Wang, Guoan and Li, Tianbin and Wang, Haoyu and Li, Ruoyu and Yao, He and Cheng, Junren and Li, Jingwen and Su, Yanzhou and Zhu, Min and He, Junjun},
    title     = {Interactive Medical Image Segmentation: A Benchmark Dataset and Baseline},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20841-20851}
}

raw_affiliation:
[{"affiliation": "College of Computer Science, Sichuan University", "city": "Chengdu", "country": "China"}, {"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "School of Computer Science and Technology, Xinjiang University", "city": "Urumqi", "country": "China"}, {"affiliation": "Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences", "city": "Shenzhen", "country": "China"}]

affiliations:
["College of Computer Science, Sichuan University", "Shanghai AI Laboratory", "School of Computer Science and Technology, Xinjiang University", "Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences"]


title: GigaHands: A Massive Annotated Dataset of Bimanual Hand Activities
authors: Rao Fu, Dingxi Zhang, Alex Jiang, Wanjia Fu, Austin Funk, Daniel Ritchie, Srinath Sridhar
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17461-17474
affiliations: ["Brown University", "ETH Zurich"]
cities: ["Providence", "Zurich"]
countries: ["USA", "Switzerland"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fu_GigaHands_A_Massive_Annotated_Dataset_of_Bimanual_Hand_Activities_CVPR_2025_paper.html

abstract:
Understanding bimanual human hand activities is a critical problem in AI and robotics. We cannot build large models of bimanual activities because existing datasets lack the scale, coverage of diverse hand activities, and detailed annotations. We introduce GigaHands, a massive annotated dataset capturing 34 hours of bimanual hand activities from 56 subjects and 417 objects, totaling 14k motion clips derived from 183 million frames paired with 84k text annotations. Our markerless capture setup and data acquisition protocol enable fully automatic 3D hand and object estimation while minimizing the effort required for text annotation. The scale and diversity of GigaHands enable broad applications, including text-driven action synthesis, hand motion captioning, and dynamic radiance field reconstruction.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fu_GigaHands_A_Massive_Annotated_Dataset_of_Bimanual_Hand_Activities_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Fu_GigaHands_A_Massive_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Fu_2025_CVPR,
    author    = {Fu, Rao and Zhang, Dingxi and Jiang, Alex and Fu, Wanjia and Funk, Austin and Ritchie, Daniel and Sridhar, Srinath},
    title     = {GigaHands: A Massive Annotated Dataset of Bimanual Hand Activities},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17461-17474}
}

raw_affiliation:
[{"affiliation": "Brown University", "city": "Providence", "country": "USA"}, {"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}]


title: AutoSSVH: Exploring Automated Frame Sampling for Efficient Self-Supervised Video Hashing
authors: Niu Lian, Jun Li, Jinpeng Wang, Ruisheng Luo, Yaowei Wang, Shu-Tao Xia, Bin Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18881-18890
cities: ["Shenzhen", "Shenzhen", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lian_AutoSSVH_Exploring_Automated_Frame_Sampling_for_Efficient_Self-Supervised_Video_Hashing_CVPR_2025_paper.html

abstract:
Self-Supervised Video Hashing (SSVH) compresses videos into hash codes for efficient indexing and retrieval using unlabeled training videos. Existing approaches rely on random frame sampling to learn video features and treat all frames equally. This results in suboptimal hash codes, as it ignores frame-specific information density and reconstruction difficulty. To address this limitation, we propose a new framework, termed AutoSSVH, that employs adversarial frame sampling with hash-based contrastive learning. Our adversarial sampling strategy automatically identifies and selects challenging frames with richer information for reconstruction, enhancing encoding capability. Additionally, we introduce a hash component voting strategy and a point-to-set (P2Set) hash-based contrastive objective, which help capture complex inter-video semantic relationships in the Hamming space and improve the discriminability of learned hash codes. Extensive experiments demonstrate that AutoSSVH achieves superior retrieval efficacy and efficiency compared to state-of-the-art approaches. Code is available at https://github.com/EliSpectre/CVPR25-AutoSSVH.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lian_AutoSSVH_Exploring_Automated_Frame_Sampling_for_Efficient_Self-Supervised_Video_Hashing_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Lian_2025_CVPR,
    author    = {Lian, Niu and Li, Jun and Wang, Jinpeng and Luo, Ruisheng and Wang, Yaowei and Xia, Shu-Tao and Chen, Bin},
    title     = {AutoSSVH: Exploring Automated Frame Sampling for Efficient Self-Supervised Video Hashing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18881-18890}
}

raw_affiliation:
[{"affiliation": "Harbin Institute of Technology, Shenzhen", "city": "Shenzhen", "country": "China"}, {"affiliation": "Tsinghua Shenzhen International Graduate School, Tsinghua University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Research Center of Artificial Intelligence, Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}]

affiliations:
["Harbin Institute of Technology, Shenzhen", "Tsinghua Shenzhen International Graduate School, Tsinghua University", "Research Center of Artificial Intelligence, Peng Cheng Laboratory"]


authors: Federico Cocchi, Nicholas Moratelli, Marcella Cornia, Lorenzo Baraldi, Rita Cucchiara
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9199-9209
affiliations: ["University of Modena and Reggio Emilia, Italy", "University of Pisa, Italy", "IIT-CNR, Italy"]
cities: ["Modena", "Pisa", "Italy"]
countries: ["Italy", "Italy", "Italy"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cocchi_Augmenting_Multimodal_LLMs_with_Self-Reflective_Tokens_for_Knowledge-based_Visual_Question_CVPR_2025_paper.html

title:
Augmenting Multimodal LLMs with Self-Reflective Tokens for Knowledge-based Visual Question Answering

abstract:
Multimodal LLMs (MLLMs) are the natural extension of large language models to handle multimodal inputs, combining text and image data. They have recently garnered attention due to their capability to address complex tasks involving both modalities. However, their effectiveness is limited to the knowledge acquired during training, which restricts their practical utility. In this work, we introduce a novel method to enhance the adaptability of MLLMs by integrating external knowledge sources. Our proposed model, Reflective LLaVA (ReflectiVA), utilizes reflective tokens to dynamically determine the need for external knowledge and predict the relevance of information retrieved from an external database. Tokens are trained following a two-stage two-model training recipe. This ultimately enables the MLLM to manage external knowledge while preserving fluency and performance on tasks where external knowledge is not needed. Through our experiments, we demonstrate the efficacy of ReflectiVA for knowledge-based visual question answering, highlighting its superior performance compared to existing methods. Source code and trained models are publicly available at https://aimagelab.github.io/ReflectiVA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cocchi_Augmenting_Multimodal_LLMs_with_Self-Reflective_Tokens_for_Knowledge-based_Visual_Question_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cocchi_Augmenting_Multimodal_LLMs_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cocchi_2025_CVPR,
    author    = {Cocchi, Federico and Moratelli, Nicholas and Cornia, Marcella and Baraldi, Lorenzo and Cucchiara, Rita},
    title     = {Augmenting Multimodal LLMs with Self-Reflective Tokens for Knowledge-based Visual Question Answering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9199-9209}
}

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


title: DifIISR: A Diffusion Model with Gradient Guidance for Infrared Image Super-Resolution
authors: Xingyuan Li, Zirui Wang, Yang Zou, Zhixin Chen, Jun Ma, Zhiying Jiang, Long Ma, Jinyuan Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7534-7544
cities: ["Dalian", "Xi'an", "Kitakyushu", "Dalian"]
countries: ["China", "China", "Japan", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_DifIISR_A_Diffusion_Model_with_Gradient_Guidance_for_Infrared_Image_CVPR_2025_paper.html

abstract:
Infrared imaging is essential for autonomous driving and robotic operations as a supportive modality due to its reliable performance in challenging environments. Despite its popularity, the limitations of infrared cameras, such as low spatial resolution and complex degradations, consistently challenge imaging quality and subsequent visual tasks. Hence, infrared image super-resolution (IISR) has been developed to address this challenge. While recent developments in diffusion models have greatly advanced this field, current methods to solve it either ignore the unique modal characteristics of infrared imaging or overlook the machine perception requirements. To bridge these gaps, we propose DifIISR, an infrared image super-resolution diffusion model optimized for visual quality and perceptual performance. Our approach achieves task-based guidance for diffusion by injecting gradients derived from visual and perceptual priors into the noise during the reverse process. Specifically, we introduce an infrared thermal spectrum distribution regulation to preserve visual fidelity, ensuring that the reconstructed infrared images closely align with high-resolution images by matching their frequency components. Subsequently, we incorporate various visual foundational models as the perceptual guidance for downstream visual tasks, infusing generalizable perceptual features beneficial for detection and segmentation. As a result, our approach gains superior visual results while attaining State-Of-The-Art downstream task performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_DifIISR_A_Diffusion_Model_with_Gradient_Guidance_for_Infrared_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_DifIISR_A_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Xingyuan and Wang, Zirui and Zou, Yang and Chen, Zhixin and Ma, Jun and Jiang, Zhiying and Ma, Long and Liu, Jinyuan},
    title     = {DifIISR: A Diffusion Model with Gradient Guidance for Infrared Image Super-Resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7534-7544}
}

raw_affiliation:
[{"affiliation": "School of Software Technology & DUT-RU International School of ISE, Dalian University of Technology", "city": "Dalian", "country": "China"}, {"affiliation": "School of Computer Science, Northwestern Polytechnical University", "city": "Xi'an", "country": "China"}, {"affiliation": "Graduate School of Information, Production, and Systems, Waseda University", "city": "Kitakyushu", "country": "Japan"}, {"affiliation": "College of Information Science and Technology, Dalian Maritime University", "city": "Dalian", "country": "China"}]

affiliations:
["School of Software Technology & DUT-RU International School of ISE, Dalian University of Technology", "School of Computer Science, Northwestern Polytechnical University", "Graduate School of Information, Production, and Systems, Waseda University", "College of Information Science and Technology, Dalian Maritime University"]


title: Recurrent Feature Mining and Keypoint Mixup Padding for Category-Agnostic Pose Estimation
authors: Junjie Chen, Weilong Chen, Yifan Zuo, Yuming Fang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22035-22044
affiliations: ["Jiangxi University of Finance and Economics"]
cities: ["Nanchang"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Recurrent_Feature_Mining_and_Keypoint_Mixup_Padding_for_Category-Agnostic_Pose_CVPR_2025_paper.html

abstract:
Category-agnostic pose estimation aims to locate keypoints on query images according to a few annotated support images for arbitrary novel classes. Existing methods generally extract support features via heatmap pooling, and obtain interacted features from support and query via cross-attention. Hence, these works neglect to mine fine-grained and structure-aware (FGSA) features from both support and query images, which are crucial for pixel-level keypoint localization. To this end, we propose a novel yet concise framework, which recurrently mines FGSA features from both support and query images. Specifically, we design a FGSA mining module based on deformable attention mechanism. On the one hand, we mine fine-grained features by applying deformable attention head over multi-scale feature maps. On the other hand, we mine structure-aware features by offsetting the reference points of keypoints to their linked keypoints. By means of above module, we recurrently mine FGSA features from support and query images, and thus obtain better support features and query estimations. In addition, we propose to use mixup keypoints to pad various classes to a unified keypoint number, which could provide richer supervision than the zero padding used in existing works. We conduct extensive experiments and in-depth studies on large-scale MP-100 dataset, and outperform SOTA method dramatically (+3.2% PCK\@0.05).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Recurrent_Feature_Mining_and_Keypoint_Mixup_Padding_for_Category-Agnostic_Pose_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Junjie and Chen, Weilong and Zuo, Yifan and Fang, Yuming},
    title     = {Recurrent Feature Mining and Keypoint Mixup Padding for Category-Agnostic Pose Estimation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22035-22044}
}

raw_affiliation:
[{"affiliation": "Jiangxi University of Finance and Economics", "city": "Nanchang", "country": "China"}]


title: FrugalNeRF: Fast Convergence for Extreme Few-shot Novel View Synthesis without Learned Priors
authors: Chin-Yang Lin, Chung-Ho Wu, Chang-Han Yeh, Shih-Han Yen, Cheng Sun, Yu-Lun Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11227-11238
affiliations: ["National Yang Ming Chiao Tung University", "NVIDIA Research"]
cities: ["Hsinchu", "Santa Clara"]
countries: ["Taiwan", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_FrugalNeRF_Fast_Convergence_for_Extreme_Few-shot_Novel_View_Synthesis_without_CVPR_2025_paper.html

abstract:
Neural Radiance Fields (NeRF) face significant challenges in extreme few-shot scenarios, primarily due to overfitting and long training times. Existing methods, such as FreeNeRF and SparseNeRF, use frequency regularization or pre-trained priors but struggle with complex scheduling and bias. We introduce FrugalNeRF, a novel few-shot NeRF framework that leverages weight-sharing voxels across multiple scales to efficiently represent scene details. Our key contribution is a cross-scale geometric adaptation scheme that selects pseudo ground truth depth based on reprojection errors across scales. This guides training without relying on externally learned priors, enabling full utilization of the training data. It can also integrate pre-trained priors, enhancing quality without slowing convergence.  Experiments on LLFF, DTU, and RealEstate-10K show that FrugalNeRF outperforms other few-shot NeRF methods while significantly reducing training time, making it a practical solution for efficient and accurate 3D scene reconstruction.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_FrugalNeRF_Fast_Convergence_for_Extreme_Few-shot_Novel_View_Synthesis_without_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_FrugalNeRF_Fast_Convergence_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Chin-Yang and Wu, Chung-Ho and Yeh, Chang-Han and Yen, Shih-Han and Sun, Cheng and Liu, Yu-Lun},
    title     = {FrugalNeRF: Fast Convergence for Extreme Few-shot Novel View Synthesis without Learned Priors},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11227-11238}
}

raw_affiliation:
[{"affiliation": "National Yang Ming Chiao Tung University", "city": "Hsinchu", "country": "Taiwan"}, {"affiliation": "NVIDIA Research", "city": "Santa Clara", "country": "USA"}]


title: 3D-GSW: 3D Gaussian Splatting for Robust Watermarking
authors: Youngdong Jang, Hyunje Park, Feng Yang, Heeju Ko, Euijin Choo, Sangpil Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5938-5948
affiliations: ["Korea University", "Google DeepMind", "University of Alberta"]
cities: ["Seoul", "Mountain View", "Edmonton"]
countries: ["South Korea", "USA", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jang_3D-GSW_3D_Gaussian_Splatting_for_Robust_Watermarking_CVPR_2025_paper.html

abstract:
As 3D Gaussian Splatting (3D-GS) gains significant attention and its commercial usage increases, the need for watermarking technologies to prevent unauthorized use of the 3D-GS models and rendered images has become increasingly important. In this paper, we introduce a robust watermarking method for 3D-GS that secures copyright of both the model and its rendered images. Our proposed method remains robust against distortions in rendered images and model attacks while maintaining high rendering quality. To achieve these objectives, we present Frequency-Guided Densification (FGD), which removes 3D Gaussians based on their contribution to rendering quality, enhancing real-time rendering and the robustness of the message. FGD utilizes Discrete Fourier Transform to split 3D Gaussians in high-frequency areas, improving rendering quality. Furthermore, we employ a gradient mask for 3D Gaussians and design a wavelet-subband loss to enhance rendering quality. Our experiments show that our method embeds the message in the rendered images invisibly and robustly against various attacks, including model distortion. Our method achieves superior performance in both rendering quality and watermark robustness while improving real-time rendering efficiency. Project page: https://kuai-lab.github.io/cvpr20253dgsw/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jang_3D-GSW_3D_Gaussian_Splatting_for_Robust_Watermarking_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jang_3D-GSW_3D_Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jang_2025_CVPR,
    author    = {Jang, Youngdong and Park, Hyunje and Yang, Feng and Ko, Heeju and Choo, Euijin and Kim, Sangpil},
    title     = {3D-GSW: 3D Gaussian Splatting for Robust Watermarking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5938-5948}
}

raw_affiliation:
[{"affiliation": "Korea University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}, {"affiliation": "University of Alberta", "city": "Edmonton", "country": "Canada"}]


authors: Maosen Zhao, Pengtao Chen, Chong Yu, Yan Wen, Xudong Tan, Tao Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18134-18143
cities: ["Shanghai", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_Pioneering_4-Bit_FP_Quantization_for_Diffusion_Models_Mixup-Sign_Quantization_and_CVPR_2025_paper.html

title:
Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning

abstract:
Model quantization reduces the bit-width of weights and activations, improving memory efficiency and inference speed in diffusion models. However, achieving 4-bit quantization remains challenging. Existing methods, primarily based on integer quantization and post-training quantization fine-tuning, struggle with inconsistent performance. Inspired by the success of floating-point (FP) quantization in large language models, we explore low-bit FP quantization for diffusion models and identify key challenges: the failure of signed FP quantization to handle asymmetric activation distributions, the insufficient consideration of temporal complexity in the denoising process during fine-tuning, and the misalignment between fine-tuning loss and quantization error. To address these challenges, we propose the mixup-sign floating-point quantization (MSFP) framework, first introducing unsigned FP quantization in model quantization, along with timestep-aware LoRA (TALoRA) and denoising-factor loss alignment (DFA), which ensure precise and stable fine-tuning. Extensive experiments show that we are the first to achieve superior performance in 4-bit FP quantization for diffusion models, outperforming existing PTQ fine-tuning methods in 4-bit INT quantization.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_Pioneering_4-Bit_FP_Quantization_for_Diffusion_Models_Mixup-Sign_Quantization_and_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_Pioneering_4-Bit_FP_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Maosen and Chen, Pengtao and Yu, Chong and Wen, Yan and Tan, Xudong and Chen, Tao},
    title     = {Pioneering 4-Bit FP Quantization for Diffusion Models: Mixup-Sign Quantization and Timestep-Aware Fine-Tuning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18134-18143}
}

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

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


title: OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 56-66
cities: ["Shanghai", "Rochester", "Beijing", "Shanghai", "Singapore", "Hefei", "Abu Dhabi", "Shanghai"]
countries: ["China", "USA", "China", "China", "Singapore", "China", "UAE", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_OpenING_A_Comprehensive_Benchmark_for_Judging_Open-ended_Interleaved_Image-Text_Generation_CVPR_2025_paper.html

authors:
Pengfei Zhou, Xiaopeng Peng, Jiajun Song, Chuanhao Li, Zhaopan Xu, Yue Yang, Ziyao Guo, Hao Zhang, Yuqi Lin, Yefei He, Lirui Zhao, Shuo Liu, Tianhua Li, Yuxuan Xie, Xiaojun Chang, Yu Qiao, Wenqi Shao, Kaipeng Zhang

abstract:
Multimodal Large Language Models (MLLMs) have made significant strides in visual understanding and generation tasks. However, generating interleaved image-text content remains a challenge, which requires integrated multimodal understanding and generation abilities. While the progress in unified models offers new solutions, existing benchmarks are insufficient for evaluating these methods due to data size and diversity limitations. To bridge this gap, we introduce OpenING, a comprehensive benchmark comprising 5,400 high-quality human-annotated instances across 56 real-world tasks. OpenING covers diverse daily scenarios such as travel guide, design, and brainstorming, offering a robust platform for challenging interleaved generation methods. In addition, we present IntJudge, a judge model for evaluating open-ended multimodal generation methods. Trained with a novel data pipeline, our IntJudge achieves an agreement rate of 82.42% with human judgments, outperforming GPT-based evaluators by 11.34%. Extensive experiments on OpenING reveal that current interleaved generation methods still have substantial room for improvement. Key findings on interleaved image-text generation are further presented to guide the development of next-generation models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_OpenING_A_Comprehensive_Benchmark_for_Judging_Open-ended_Interleaved_Image-Text_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_OpenING_A_Comprehensive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Pengfei and Peng, Xiaopeng and Song, Jiajun and Li, Chuanhao and Xu, Zhaopan and Yang, Yue and Guo, Ziyao and Zhang, Hao and Lin, Yuqi and He, Yefei and Zhao, Lirui and Liu, Shuo and Li, Tianhua and Xie, Yuxuan and Chang, Xiaojun and Qiao, Yu and Shao, Wenqi and Zhang, Kaipeng},
    title     = {OpenING: A Comprehensive Benchmark for Judging Open-ended Interleaved Image-Text Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {56-66}
}

raw_affiliation:
[{"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "RIT", "city": "Rochester", "country": "USA"}, {"affiliation": "RUC", "city": "Beijing", "country": "China"}, {"affiliation": "SJTU", "city": "Shanghai", "country": "China"}, {"affiliation": "NUS", "city": "Singapore", "country": "Singapore"}, {"affiliation": "USTC", "city": "Hefei", "country": "China"}, {"affiliation": "MBZUAI", "city": "Abu Dhabi", "country": "UAE"}, {"affiliation": "Shanghai Innovation Institute", "city": "Shanghai", "country": "China"}]

affiliations:
["Shanghai AI Laboratory", "RIT", "RUC", "SJTU", "NUS", "USTC", "MBZUAI", "Shanghai Innovation Institute"]


title: Dual Exposure Stereo for Extended Dynamic Range 3D Imaging
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6283-6293
cities: ["Pohang", "Pohang", "Pohang", "Daejeon", "Blacksburg", "Princeton", "Princeton", "Pohang"]
countries: ["South Korea", "South Korea", "South Korea", "South Korea", "USA", "USA", "USA", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Choi_Dual_Exposure_Stereo_for_Extended_Dynamic_Range_3D_Imaging_CVPR_2025_paper.html

authors:
Juhyung Choi, Jinnyeong Kim, Seokjun Choi, Jinwoo Lee, Samuel Brucker, Mario Bijelic, Felix Heide, Seung-Hwan Baek

abstract:
Achieving robust stereo 3D imaging under diverse illumination conditions is an important however challenging task, largely due to the limited dynamic ranges (DRs) of cameras, which are significantly smaller than real world DR. As a result, the accuracy of existing stereo depth estimation methods is often compromised by under- or over-exposed images.In this work, we introduce dual-exposure stereo for extended dynamic range 3D imaging. We develop automatic dual-exposure control method that adjusts the dual exposures, diverging them when the scene DR exceeds the camera DR, thereby providing information about broader DR. From the captured dual-exposure stereo images, we estimate depth by developing a motion-aware dual-exposure stereo depth network.To validate our proposed method, we develop a robot-vision system, collect real-world stereo video datasets, and generate a synthetic dataset. Our approach outperforms traditional exposure control and depth estimation methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Choi_Dual_Exposure_Stereo_for_Extended_Dynamic_Range_3D_Imaging_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Choi_Dual_Exposure_Stereo_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Choi_2025_CVPR,
    author    = {Choi, Juhyung and Kim, Jinnyeong and Choi, Seokjun and Lee, Jinwoo and Brucker, Samuel and Bijelic, Mario and Heide, Felix and Baek, Seung-Hwan},
    title     = {Dual Exposure Stereo for Extended Dynamic Range 3D Imaging},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6283-6293}
}

raw_affiliation:
[{"affiliation": "POSTECH", "city": "Pohang", "country": "South Korea"}, {"affiliation": "POSTECH", "city": "Pohang", "country": "South Korea"}, {"affiliation": "POSTECH", "city": "Pohang", "country": "South Korea"}, {"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "Torc Robotics", "city": "Blacksburg", "country": "USA"}, {"affiliation": "Princeton University", "city": "Princeton", "country": "USA"}, {"affiliation": "Princeton University", "city": "Princeton", "country": "USA"}, {"affiliation": "POSTECH", "city": "Pohang", "country": "South Korea"}]

affiliations:
["POSTECH", "POSTECH", "POSTECH", "KAIST", "Torc Robotics", "Princeton University", "Princeton University", "POSTECH"]


title: Unbiasing through Textual Descriptions: Mitigating Representation Bias in Video Benchmarks
authors: Nina Shvetsova, Arsha Nagrani, Bernt Schiele, Hilde Kuehne, Christian Rupprecht
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29050-29059
cities: ["Frankfurt", "Tuebingen", "Saarbrucken", "Oxford", "Cambridge"]
countries: ["Germany", "Germany", "Germany", "UK", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shvetsova_Unbiasing_through_Textual_Descriptions_Mitigating_Representation_Bias_in_Video_Benchmarks_CVPR_2025_paper.html

abstract:
We propose a new "Unbiased through Textual Description (UTD)" video benchmark based on unbiased subsets of existing video classification and retrieval datasets to enable a more robust assessment of video understanding capabilities. Namely, we tackle the problem that current video benchmarks may suffer from different representation biases, e.g., object bias or single-frame bias, where mere recognition of objects or utilization of only a single frame is sufficient for correct prediction. We leverage VLMs and LLMs to analyze and debias benchmarks from such representation biases. Specifically, we generate frame-wise textual descriptions of videos, filter them for specific information (e.g. only objects) and leverage them to examine representation biases across three dimensions: 1) concept bias -- determining if a specific concept (e.g., objects) alone suffice for prediction; 2) temporal bias -- assessing if temporal information contributes to prediction; and 3) common sense vs. dataset bias -- evaluating whether zero-shot reasoning or dataset correlations contribute to prediction. We conduct a systematic analysis of 12 popular video classification and retrieval datasets and create new object-debiased test splits for these datasets. Moreover, we benchmark 30 state-of-the-art video models on original and debiased splits and analyze biases in the models. To facilitate the future development of more robust video understanding benchmarks and models, we release: "UTD-descriptions", a dataset with our rich structured descriptions for each dataset, and "UTD-splits", a dataset of object-debiased test splits.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shvetsova_Unbiasing_through_Textual_Descriptions_Mitigating_Representation_Bias_in_Video_Benchmarks_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shvetsova_Unbiasing_through_Textual_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shvetsova_2025_CVPR,
    author    = {Shvetsova, Nina and Nagrani, Arsha and Schiele, Bernt and Kuehne, Hilde and Rupprecht, Christian},
    title     = {Unbiasing through Textual Descriptions: Mitigating Representation Bias in Video Benchmarks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29050-29059}
}

raw_affiliation:
[{"affiliation": "Goethe University Frankfurt", "city": "Frankfurt", "country": "Germany"}, {"affiliation": "Tuebingen AI Center/University of Tuebingen", "city": "Tuebingen", "country": "Germany"}, {"affiliation": "MPI for Informatics, Saarland Informatics Campus", "city": "Saarbrucken", "country": "Germany"}, {"affiliation": "University of Oxford", "city": "Oxford", "country": "UK"}, {"affiliation": "MIT-IBM Watson AI Lab", "city": "Cambridge", "country": "USA"}]

affiliations:
["Goethe University Frankfurt", "Tuebingen AI Center/University of Tuebingen", "MPI for Informatics, Saarland Informatics Campus", "University of Oxford", "MIT-IBM Watson AI Lab"]


title: Embodied Scene Understanding for Vision Language Models via MetaVQA
authors: Weizhen Wang, Chenda Duan, Zhenghao Peng, Yuxin Liu, Bolei Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22453-22464
raw_affiliation: [{"affiliation": "University of California, Los Angeles", "city": "Los Angeles", "country": "USA"}]
affiliations: ["University of California, Los Angeles"]
cities: ["Los Angeles"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Embodied_Scene_Understanding_for_Vision_Language_Models_via_MetaVQA_CVPR_2025_paper.html

abstract:
Vision Language Models (VLMs) demonstrate significant potential as embodied AI agents for various mobility applications. However, a standardized, closed-loop benchmark for evaluating their spatial reasoning and sequential decision-making capabilities is lacking. To address this, we present MetaVQA: a comprehensive benchmark designed to assess and enhance VLMs' understanding of spatial relationships and scene dynamics through Visual Question Answering (VQA) and closed-loop simulations. MetaVQA leverages Set-of-Mark prompting and top-down view ground-truth annotations from nuScenes and Waymo datasets to automatically generate extensive question-answer pairs based on diverse real-world traffic scenarios, ensuring object-centric and context-rich instructions. Our experiments show that fine-tuning VLMs with the MetaVQA Dataset significantly improves their embodied scene understanding, which is evident not only in improved VQA accuracy but also in emerging safety-aware driving maneuvers. In addition, the learning exhibits strong transferability from simulation to real-world observation. The project webpage is at https://metadriverse.github.io/metavqa.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Embodied_Scene_Understanding_for_Vision_Language_Models_via_MetaVQA_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Embodied_Scene_Understanding_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Weizhen and Duan, Chenda and Peng, Zhenghao and Liu, Yuxin and Zhou, Bolei},
    title     = {Embodied Scene Understanding for Vision Language Models via MetaVQA},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22453-22464}
}


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18509-18520
cities: ["Berkeley", "Hong Kong", "San Jose", "Chapel Hill", "San Francisco"]
countries: ["USA", "Hong Kong", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ge_CompGS_Unleashing_2D_Compositionality_for_Compositional_Text-to-3D_via_Dynamically_Optimizing_CVPR_2025_paper.html

title:
CompGS: Unleashing 2D Compositionality for Compositional Text-to-3D via Dynamically Optimizing 3D Gaussians

authors:
Chongjian Ge, Chenfeng Xu, Yuanfeng Ji, Chensheng Peng, Masayoshi Tomizuka, Ping Luo, Mingyu Ding, Varun Jampani, Wei Zhan

abstract:
Recent breakthroughs in text-guided image generation have significantly advanced the field of 3D generation. While generating a single high-quality 3D object is now feasible, generating multiple objects with reasonable interactions within a 3D space, a.k.a. compositional 3D generation, presents substantial challenges. This paper introduces CompGS, a novel generative framework that employs 3D Gaussian Splatting (GS) for efficient, compositional text-to-3D content generation. To achieve this goal, two core designs are proposed: (1) 3D Gaussians Initialization with 2D compositionality: We transfer the well-established 2D compositionality to initialize the Gaussian parameters on an entity-by-entity basis, ensuring both consistent 3D priors for each entity and reasonable interactions among multiple entities; (2) Dynamic Optimization: We propose a dynamic strategy to optimize 3D Gaussians using Score Distillation Sampling (SDS) loss. CompGS first automatically decomposes 3D Gaussians into distinct entity parts, enabling optimization at both the entity and composition levels. Additionally, CompGS optimizes across objects of varying scales by dynamically adjusting the spatial parameters of each entity, enhancing the generation of fine-grained details, particularly in smaller entities. Qualitative comparisons and quantitative evaluations on T3Bench demonstrate the effectiveness of CompGS in generating compositional 3D objects with superior image quality and semantic alignment over existing methods. CompGS can also be easily extended to progressively adding 3D objects, facilitating complex scene generation. We hope CompGS will provide new insights to the compositional 3D generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ge_CompGS_Unleashing_2D_Compositionality_for_Compositional_Text-to-3D_via_Dynamically_Optimizing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ge_CompGS_Unleashing_2D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ge_2025_CVPR,
    author    = {Ge, Chongjian and Xu, Chenfeng and Ji, Yuanfeng and Peng, Chensheng and Tomizuka, Masayoshi and Luo, Ping and Ding, Mingyu and Jampani, Varun and Zhan, Wei},
    title     = {CompGS: Unleashing 2D Compositionality for Compositional Text-to-3D via Dynamically Optimizing 3D Gaussians},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18509-18520}
}

raw_affiliation:
[{"affiliation": "University of California, Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "Hong Kong"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}, {"affiliation": "UNC-Chapel Hill", "city": "Chapel Hill", "country": "USA"}, {"affiliation": "Stability AI", "city": "San Francisco", "country": "USA"}]

affiliations:
["University of California, Berkeley", "The University of Hong Kong", "Adobe Research", "UNC-Chapel Hill", "Stability AI"]


title: Learning Temporally Consistent Video Depth from Video Diffusion Priors
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22841-22852
cities: ["Hangzhou", "Bologna", "Los Angeles", "Hangzhou", "Los Altos", "Bologna"]
countries: ["China", "Italy", "USA", "China", "USA", "Italy"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shao_Learning_Temporally_Consistent_Video_Depth_from_Video_Diffusion_Priors_CVPR_2025_paper.html

authors:
Jiahao Shao, Yuanbo Yang, Hongyu Zhou, Youmin Zhang, Yujun Shen, Vitor Guizilini, Yue Wang, Matteo Poggi, Yiyi Liao

abstract:
This work addresses the challenge of streamed video depth estimation, which expects not only per-frame accuracy but, more importantly, cross-frame consistency. We argue that sharing contextual information between frames or clips is pivotal in fostering temporal consistency. Therefore, we reformulate depth prediction into a conditional generation problem to provide contextual information within a clip and across clips. Specifically, we propose a consistent context-aware training and inference strategy for arbitrarily long videos to provide cross-clip context. We sample independent noise levels for each frame within a clip during training while using a sliding window strategy and initializing overlapping frames with previously predicted frames without adding noise. Moreover, we design an effective training strategy to provide context within a clip. Extensive experimental results validate our design choices and demonstrate the superiority of our approach, dubbed ChronoDepth. Project page: https://xdimlab.github.io/ChronoDepth/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shao_Learning_Temporally_Consistent_Video_Depth_from_Video_Diffusion_Priors_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shao_Learning_Temporally_Consistent_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Shao_2025_CVPR,
    author    = {Shao, Jiahao and Yang, Yuanbo and Zhou, Hongyu and Zhang, Youmin and Shen, Yujun and Guizilini, Vitor and Wang, Yue and Poggi, Matteo and Liao, Yiyi},
    title     = {Learning Temporally Consistent Video Depth from Video Diffusion Priors},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22841-22852}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "University of Bologna", "city": "Bologna", "country": "Italy"}, {"affiliation": "University of Southern California", "city": "Los Angeles", "country": "USA"}, {"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Toyota Research Institute", "city": "Los Altos", "country": "USA"}, {"affiliation": "Rock Universe AI", "city": "Bologna", "country": "Italy"}]

affiliations:
["Zhejiang University", "University of Bologna", "University of Southern California", "Ant Group", "Toyota Research Institute", "Rock Universe AI"]


title: FIRE: Robust Detection of Diffusion-Generated Images via Frequency-Guided Reconstruction Error
authors: Beilin Chu, Xuan Xu, Xin Wang, Yufei Zhang, Weike You, Linna Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12830-12839
affiliations: ["School of CyberSpace Security, Beijing University of Posts and Telecommunications"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chu_FIRE_Robust_Detection_of_Diffusion-Generated_Images_via_Frequency-Guided_Reconstruction_Error_CVPR_2025_paper.html

abstract:
The rapid advancement of diffusion models has significantly improved high-quality image generation, making generated content increasingly challenging to distinguish from real images and raising concerns about potential misuse. In this paper, we observe that diffusion models struggle to accurately reconstruct mid-band frequency information in real images, suggesting the limitation could serve as a cue for detecting diffusion model generated images. Motivated by this observation, we propose a novel method called Frequency-guIded Reconstruction Error (FIRE), which, to the best of our knowledge, is the first to investigate the influence of frequency decomposition on reconstruction error. FIRE assesses the variation in reconstruction error before and after the frequency decomposition, offering a robust method for identifying diffusion model generated images. Extensive experiments show that FIRE generalizes effectively to unseen diffusion models and maintains robustness against diverse perturbations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chu_FIRE_Robust_Detection_of_Diffusion-Generated_Images_via_Frequency-Guided_Reconstruction_Error_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chu_FIRE_Robust_Detection_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chu_2025_CVPR,
    author    = {Chu, Beilin and Xu, Xuan and Wang, Xin and Zhang, Yufei and You, Weike and Zhou, Linna},
    title     = {FIRE: Robust Detection of Diffusion-Generated Images via Frequency-Guided Reconstruction Error},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12830-12839}
}

raw_affiliation:
[{"affiliation": "School of CyberSpace Security, Beijing University of Posts and Telecommunications", "city": "Beijing", "country": "China"}]


title: Assessing and Learning Alignment of Unimodal Vision and Language Models
authors: Le Zhang, Qian Yang, Aishwarya Agrawal
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14604-14614
affiliations: ["Mila - Quebec AI Institute", "Universit\u00b4e de Montr\u00b4eal", "Canada CIFAR AI Chair"]
cities: ["Montreal", "Montreal", "Ottawa"]
countries: ["Canada", "Canada", "Canada"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Assessing_and_Learning_Alignment_of_Unimodal_Vision_and_Language_Models_CVPR_2025_paper.html

abstract:
How well are unimodal vision and language models aligned? While prior work has explored this question, their assessment methods do not directly translate to practical vision-language tasks. In this paper, we propose a direct assessment method, inspired by linear probing, to evaluate vision-language alignment. We identify that the degree of alignment of SSL vision models depends on their SSL training objective and find that clustering quality of SSL representations impacts alignment performance more than their linear separability. We then introduce Swift Alignment of Image and Language (SAIL), an efficient transfer learning framework that aligns pretrained unimodal vision and language models for downstream tasks. SAIL requires significantly less paired image-text data ( 6%) compared to models like CLIP, which are trained from scratch. It trains with a single A100 GPU in  5 hours and supports a batch size of up to 32,768. SAIL achieves 73.4% zero-shot accuracy on ImageNet (compared to CLIP's 72.7%) and excels in zero-shot retrieval, complex reasoning, and semantic segmentation. SAIL also enhances the language-compatibility of vision encoders, improving the performance of multimodal large language models. The full codebase and model weights are open-source: https://lezhang7.github.io/sail.github.io/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Assessing_and_Learning_Alignment_of_Unimodal_Vision_and_Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Assessing_and_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Le and Yang, Qian and Agrawal, Aishwarya},
    title     = {Assessing and Learning Alignment of Unimodal Vision and Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14604-14614}
}

raw_affiliation:
[{"affiliation": "Mila - Quebec AI Institute", "city": "Montreal", "country": "Canada"}, {"affiliation": "Universit\u00b4e de Montr\u00b4eal", "city": "Montreal", "country": "Canada"}, {"affiliation": "Canada CIFAR AI Chair", "city": "Ottawa", "country": "Canada"}]


title: Samba: A Unified Mamba-based Framework for General Salient Object Detection
authors: Jiahao He, Keren Fu, Xiaohong Liu, Qijun Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25314-25324
cities: ["Chengdu", "Shanghai", "Chengdu"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/He_Samba_A_Unified_Mamba-based_Framework_for_General_Salient_Object_Detection_CVPR_2025_paper.html

abstract:
Existing salient object detection (SOD) models primarily resort to convolutional neural networks (CNNs) and Transformers. However, the limited receptive fields of CNNs and quadratic computational complexity of transformers both constrain the performance of current models on discovering attention-grabbing objects. The emerging state space model, namely Mamba, has demonstrated its potential to balance global receptive fields and computational complexity. Therefore, we propose a novel unified framework based on the pure Mamba architecture, dubbed saliency Mamba (Samba), to flexibly handle general SOD tasks, including RGB/RGB-D/RGB-T SOD, video SOD (VSOD), and RGB-D VSOD. Specifically, we rethink Mamba's scanning strategy from the perspective of SOD, and identify the importance of maintaining spatial continuity of salient patches within scanning sequences. Based on this, we propose a saliency-guided Mamba block (SGMB), incorporating a spatial neighboring scanning (SNS) algorithm to preserve spatial continuity of salient patches. Additionally, we propose a context-aware upsampling (CAU) method to promote hierarchical feature alignment and aggregations by modeling contextual dependencies. Experimental results show that our Samba outperforms existing methods across five SOD tasks on 21 datasets with lower computational cost, confirming the superiority of introducing Mamba to the SOD areas. Our code  is available at https://github.com/Jia-hao999/Samba.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/He_Samba_A_Unified_Mamba-based_Framework_for_General_Salient_Object_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/He_Samba_A_Unified_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{He_2025_CVPR,
    author    = {He, Jiahao and Fu, Keren and Liu, Xiaohong and Zhao, Qijun},
    title     = {Samba: A Unified Mamba-based Framework for General Salient Object Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25314-25324}
}

raw_affiliation:
[{"affiliation": "College of CS, Sichuan University", "city": "Chengdu", "country": "China"}, {"affiliation": "John Hopcroft Center, Shanghai Jiao T ong University", "city": "Shanghai", "country": "China"}, {"affiliation": "National Key Lab of Fundamental Science on Synthetic V ision, Sichuan University", "city": "Chengdu", "country": "China"}]

affiliations:
["College of CS, Sichuan University", "John Hopcroft Center, Shanghai Jiao T ong University", "National Key Lab of Fundamental Science on Synthetic V ision, Sichuan University"]


title: Action Detail Matters: Refining Video Recognition with Local Action Queries
authors: Mengmeng Wang, Zeyi Huang, Xiangjie Kong, Guojiang Shen, Guang Dai, Jingdong Wang, Yong Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19132-19142
cities: ["Hangzhou", "Shenzhen", "Beijing", "Beijing", "Hangzhou"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Action_Detail_Matters_Refining_Video_Recognition_with_Local_Action_Queries_CVPR_2025_paper.html

abstract:
Video action recognition involves interpreting both global context and specific details to accurately identify actions. While previous models are effective at capturing spatiotemporal features, they often lack a focused representation of key action details. To address this, we introduce \nameo, a framework designed for refining video action recognition through integrated global and local feature learning. Inspired by human visual cognition theory, our approach balances the focus on both broad contextual changes and action-specific details, minimizing the influence of irrelevant background noise. We first employ learnable action queries to selectively emphasize action-relevant regions without requiring region-specific labels. Next, these queries are learned by a local action streaming branch that enables progressive query propagation. Moreover, we introduce a parameter-free feature interaction mechanism for effective multi-scale interaction between global and local features with minimal additional overhead. Extensive experiments demonstrate that \name achieves state-of-the-art performance across multiple action recognition datasets, validating its effectiveness and robustness in handling action-relevant details.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Action_Detail_Matters_Refining_Video_Recognition_with_Local_Action_Queries_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Mengmeng and Huang, Zeyi and Kong, Xiangjie and Shen, Guojiang and Dai, Guang and Wang, Jingdong and Liu, Yong},
    title     = {Action Detail Matters: Refining Video Recognition with Local Action Queries},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19132-19142}
}

raw_affiliation:
[{"affiliation": "Zhejiang University of Technology", "city": "Hangzhou", "country": "China"}, {"affiliation": "Huawei", "city": "Shenzhen", "country": "China"}, {"affiliation": "SGIT AI Lab, State Grid Corporation of China", "city": "Beijing", "country": "China"}, {"affiliation": "Baidu", "city": "Beijing", "country": "China"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}]

affiliations:
["Zhejiang University of Technology", "Huawei", "SGIT AI Lab, State Grid Corporation of China", "Baidu", "Zhejiang University"]


title: PAVE: Patching and Adapting Video Large Language Models
authors: Zhuoming Liu, Yiquan Li, Khoi Duc Nguyen, Yiwu Zhong, Yin Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3306-3317
cities: ["unknown", "unknown", "unknown"]
countries: ["unknown", "unknown", "unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_PAVE_Patching_and_Adapting_Video_Large_Language_Models_CVPR_2025_paper.html

abstract:
We present PAVE, a framework for adapting pre-trained video large language models (Video-LLMs) to downstream tasks that incorporate side-channel signals, such as audio, camera pose, or high frame rate videos. PAVE introduces a lightweight adaptation strategy called "patching", which adds a small number of parameters and operations to the base model without modifying its architecture or pre-trained weights. We demonstrate that PAVE effectively enhances pre-trained Video-LLMs with the cost of adding <1% additional FLOPs and parameters for diverse tasks, including audio-visual understanding, 3D reasoning, and multi-view video understanding, surpassing state-of-the-art task-specific models. Moreover, when applied to high frame rate videos, PAVE further improves video understanding, enhancing the performance of strong base models. Finally, our experiments show that our framework generalizes well across different Video-LLMs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_PAVE_Patching_and_Adapting_Video_Large_Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_PAVE_Patching_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Zhuoming and Li, Yiquan and Nguyen, Khoi Duc and Zhong, Yiwu and Li, Yin},
    title     = {PAVE: Patching and Adapting Video Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3306-3317}
}

raw_affiliation:
[{"affiliation": "\u00d0\u00df\u00ca\u00db", "city": "unknown", "country": "unknown"}, {"affiliation": "\u00bd\u00b1\u00b3\u00f1\u00bc\u00ae\u00bf\u00b9\u00b1\u00b2\u00b4\u00a6\u00b3\u00f1\u00d0\u00df\u00ca\u00db", "city": "unknown", "country": "unknown"}, {"affiliation": "\u00aa\u00b7\u00bc\u00bb\u00b1\u00ac\u00bf\u00ad\u00b5\u00b1\u00ba\u00ba\u00bb\u00ae\u00ad\u00ad\u00b7\u00bc\u00bb\u00f3\u00bd\u00b8\u00bf\u00b2\u00b2\u00bb\u00b4", "city": "unknown", "country": "unknown"}]

affiliations:
["\u00d0\u00df\u00ca\u00db", "\u00bd\u00b1\u00b3\u00f1\u00bc\u00ae\u00bf\u00b9\u00b1\u00b2\u00b4\u00a6\u00b3\u00f1\u00d0\u00df\u00ca\u00db", "\u00aa\u00b7\u00bc\u00bb\u00b1\u00ac\u00bf\u00ad\u00b5\u00b1\u00ba\u00ba\u00bb\u00ae\u00ad\u00ad\u00b7\u00bc\u00bb\u00f3\u00bd\u00b8\u00bf\u00b2\u00b2\u00bb\u00b4"]


title: LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30872-30885
cities: ["Heidelberg", "Heidelberg", "Heidelberg", "Heidelberg", "Heidelberg", "Heidelberg"]
countries: ["Germany", "Germany", "Germany", "Germany", "Germany", "Germany"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Rokuss_LesionLocator_Zero-Shot_Universal_Tumor_Segmentation_and_Tracking_in_3D_Whole-Body_CVPR_2025_paper.html

authors:
Maximilian Rokuss, Yannick Kirchhoff, Seval Akbal, Balint Kovacs, Saikat Roy, Constantin Ulrich, Tassilo Wald, Lukas T. Rotkopf, Heinz-Peter Schlemmer, Klaus Maier-Hein

abstract:
In this work, we present LesionLocator, a framework for zero-shot longitudinal lesion tracking and segmentation in 3D medical imaging, establishing the first end-to-end model capable of 4D tracking with dense spatial prompts. Our model leverages an extensive dataset of 23,262 annotated medical scans, as well as synthesized longitudinal data across diverse lesion types. The diversity and scale of our dataset significantly enhances model generalizability to real-world medical imaging challenges and addresses key limitations in longitudinal data availability. LesionLocator outperforms all existing promptable models in lesion segmentation by nearly 10 dice points, reaching human-level performance, and achieves state-of-the-art results in lesion tracking, with superior lesion retrieval and segmentation accuracy. LesionLocator not only sets a new benchmark in universal promptable lesion segmentation and automated longitudinal lesion tracking but also provides the first open-access solution of its kind, releasing our synthetic 4D dataset and model to the community, empowering future advancements in medical imaging. Code is available at: www.github.com/MIC-DKFZ/LesionLocator

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Rokuss_LesionLocator_Zero-Shot_Universal_Tumor_Segmentation_and_Tracking_in_3D_Whole-Body_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Rokuss_LesionLocator_Zero-Shot_Universal_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Rokuss_2025_CVPR,
    author    = {Rokuss, Maximilian and Kirchhoff, Yannick and Akbal, Seval and Kovacs, Balint and Roy, Saikat and Ulrich, Constantin and Wald, Tassilo and Rotkopf, Lukas T. and Schlemmer, Heinz-Peter and Maier-Hein, Klaus},
    title     = {LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30872-30885}
}

raw_affiliation:
[{"affiliation": "German Cancer Research Center, Division of Medical Image Computing", "city": "Heidelberg", "country": "Germany"}, {"affiliation": "Faculty of Mathematics and Computer Science and Medical Faculty - Heidelberg University", "city": "Heidelberg", "country": "Germany"}, {"affiliation": "Helmholtz Imaging", "city": "Heidelberg", "country": "Germany"}, {"affiliation": "German Cancer Research Center, Department of Radiology", "city": "Heidelberg", "country": "Germany"}, {"affiliation": "HIDSS4Health", "city": "Heidelberg", "country": "Germany"}, {"affiliation": "Pattern Analysis and Learning Group, Heidelberg University Hospital", "city": "Heidelberg", "country": "Germany"}]

affiliations:
["German Cancer Research Center, Division of Medical Image Computing", "Faculty of Mathematics and Computer Science and Medical Faculty - Heidelberg University", "Helmholtz Imaging", "German Cancer Research Center, Department of Radiology", "HIDSS4Health", "Pattern Analysis and Learning Group, Heidelberg University Hospital"]


title: Generative Map Priors for Collaborative BEV Semantic Segmentation
authors: Jiahui Fu, Yue Gong, Luting Wang, Shifeng Zhang, Xu Zhou, Si Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11919-11928
affiliations: ["Institute of Artificial Intelligence, Beihang University", "Sangfor Technologies Inc."]
cities: ["Beijing", "Shenzhen"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Fu_Generative_Map_Priors_for_Collaborative_BEV_Semantic_Segmentation_CVPR_2025_paper.html

abstract:
Collaborative perception aims to address the constraint of single-agent perception by exchanging information among multiple agents. Previous works primarily focus on collaborative object detection, exploring compressed transmission and fusion prediction tailored to sparse object features. However, these strategies are not well-suited for dense features in collaborative BEV semantic segmentation. Therefore, we propose CoGMP, a novel Collaborative framework that leverages Generative Map Priors to achieve efficient compression and robust fusion. CoGMP introduces two key innovations: Element Format Feature Compression (EFFC) and Structure Guided Feature Fusion (SGFF). Specifically, EFFC leverages map element priors from codebook to encode BEV features as discrete element indices for transmitted information compression. Meanwhile, SGFF utilizes a diffusion model with structural priors to coherently integrate multi-agent features, thereby achieving consistent fusion predictions. Evaluations on the OPV2V dataset show that CoGMP achieves a 6.89/7.64 Road/Lane IoU improvement and a 32-fold reduction in communication volume.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Fu_Generative_Map_Priors_for_Collaborative_BEV_Semantic_Segmentation_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Fu_2025_CVPR,
    author    = {Fu, Jiahui and Gong, Yue and Wang, Luting and Zhang, Shifeng and Zhou, Xu and Liu, Si},
    title     = {Generative Map Priors for Collaborative BEV Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11919-11928}
}

raw_affiliation:
[{"affiliation": "Institute of Artificial Intelligence, Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "Sangfor Technologies Inc.", "city": "Shenzhen", "country": "China"}]


title: Coherent 3D Portrait Video Reconstruction via Triplane Fusion
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10712-10722
affiliations: ["UNC Chapel Hill", "NVIDIA"]
cities: ["Chapel Hill", "Santa Clara"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Coherent_3D_Portrait_Video_Reconstruction_via_Triplane_Fusion_CVPR_2025_paper.html

authors:
Shengze Wang, Xueting Li, Chao Liu, Matthew Chan, Michael Stengel, Henry Fuchs, Shalini De Mello, Koki Nagano

abstract:
Recent breakthroughs in single-image 3D portrait reconstruction have enabled telepresence systems to stream 3D portrait videos from a single camera in real-time, democratizing telepresence. However, per-frame 3D reconstruction exhibits temporal inconsistency and forgets the user's appearance. On the other hand, self-reenactment methods can render coherent 3D portraits by driving a 3D avatar built from a single reference image but fail to faithfully preserve the user's per-frame appearance (e.g., instantaneous facial expressions and lighting). As a result, neither of these two frameworks is an ideal solution for democratized 3D telepresence. In this work, we address this dilemma and propose a novel solution that maintains both coherent identity and dynamic per-frame appearance to enable the best possible realism. To this end, we propose a new fusion-based method that takes the best of both worlds by fusing a canonical 3D prior from a reference view with dynamic appearance from per-frame input views, producing temporally stable 3D videos with faithful reconstruction of the user's per-frame appearance. Trained only using synthetic data produced by an expression-conditioned 3D GAN, our encoder-based method achieves both state-of-the-art 3D reconstruction and temporal consistency on in-studio and in-the-wild datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Coherent_3D_Portrait_Video_Reconstruction_via_Triplane_Fusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Coherent_3D_Portrait_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Shengze and Li, Xueting and Liu, Chao and Chan, Matthew and Stengel, Michael and Fuchs, Henry and De Mello, Shalini and Nagano, Koki},
    title     = {Coherent 3D Portrait Video Reconstruction via Triplane Fusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10712-10722}
}

raw_affiliation:
[{"affiliation": "UNC Chapel Hill", "city": "Chapel Hill", "country": "USA"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}]


title: Generative Image Layer Decomposition with Visual Effects
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7643-7653
affiliations: ["UC Santa Cruz", "Adobe Research"]
cities: ["Santa Cruz", "San Jose"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Generative_Image_Layer_Decomposition_with_Visual_Effects_CVPR_2025_paper.html

authors:
Jinrui Yang, Qing Liu, Yijun Li, Soo Ye Kim, Daniil Pakhomov, Mengwei Ren, Jianming Zhang, Zhe Lin, Cihang Xie, Yuyin Zhou

abstract:
Recent advancements in large generative models, particularly diffusion-based methods, have significantly enhanced the capabilities of image editing. However, achieving precise control over image composition tasks remains a challenge. Layered representations, which allow for independent editing of image components, are essential for user-driven content creation, yet existing approaches often struggle to decompose image into plausible layers with accurately retained transparent visual effects such as shadows and reflections. We propose LayerDecomp, a generative framework for image layer decomposition which outputs photorealistic clean backgrounds and high-quality transparent foregrounds with faithfully preserved visual effects. To enable effective training, we first introduce a dataset preparation pipeline that automatically scales up simulated multi-layer data with synthesized visual effects. To further enhance real-world applicability, we supplement this simulated dataset with camera-captured images containing natural visual effects. Additionally, we propose a consistency loss which enforces the model to learn accurate representations for the transparent foreground layer when ground-truth annotations are not available. Our method achieves superior quality in layer decomposition, outperforming existing approaches in object removal and spatial editing tasks across several benchmarks and multiple user studies, unlocking various creative possibilities for layer-wise image editing.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Generative_Image_Layer_Decomposition_with_Visual_Effects_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Generative_Image_Layer_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Jinrui and Liu, Qing and Li, Yijun and Kim, Soo Ye and Pakhomov, Daniil and Ren, Mengwei and Zhang, Jianming and Lin, Zhe and Xie, Cihang and Zhou, Yuyin},
    title     = {Generative Image Layer Decomposition with Visual Effects},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7643-7653}
}

raw_affiliation:
[{"affiliation": "UC Santa Cruz", "city": "Santa Cruz", "country": "USA"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}]


title: AR-Diffusion: Asynchronous Video Generation with Auto-Regressive Diffusion
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7364-7373
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_AR-Diffusion_Asynchronous_Video_Generation_with_Auto-Regressive_Diffusion_CVPR_2025_paper.html

authors:
Mingzhen Sun, Weining Wang, Gen Li, Jiawei Liu, Jiahui Sun, Wanquan Feng, Shanshan Lao, Siyu Zhou, Qian He, Jing Liu

abstract:
The task of video generation requires synthesizing visually realistic and temporally coherent video frames. Existing methods primarily use asynchronous auto-regressive models or synchronous diffusion models to address this challenge. However, asynchronous auto-regressive models often suffer from inconsistencies between training and inference, leading to issues such as error accumulation, while synchronous diffusion models are limited by their reliance on rigid sequence length.  To address these issues, we introduce Auto-Regressive Diffusion (AR-Diffusion), a novel model that combines the strengths of auto-regressive and diffusion models for flexible, asynchronous video generation. Specifically, our approach leverages diffusion to gradually corrupt video frames in both training and inference, reducing the discrepancy between these phases. Inspired by auto-regressive generation, we incorporate a non-decreasing constraint on the corruption timesteps of individual frames, ensuring that earlier frames remain clearer than subsequent ones. This setup, together with temporal causal attention, enables flexible generation of videos with varying lengths while preserving temporal coherence. In addition, we design two specialized timestep schedulers: the FoPP scheduler for balanced timestep sampling during training, and the AD scheduler for flexible timestep differences during inference, supporting both synchronous and asynchronous generation. Extensive experiments demonstrate the superiority of our proposed method, by achieving competitive and state-of-the-art results across four challenging benchmarks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_AR-Diffusion_Asynchronous_Video_Generation_with_Auto-Regressive_Diffusion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_AR-Diffusion_Asynchronous_Video_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Mingzhen and Wang, Weining and Li, Gen and Liu, Jiawei and Sun, Jiahui and Feng, Wanquan and Lao, Shanshan and Zhou, Siyu and He, Qian and Liu, Jing},
    title     = {AR-Diffusion: Asynchronous Video Generation with Auto-Regressive Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7364-7373}
}

raw_affiliation:
[{"affiliation": "Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Bytedance Inc.", "city": "Beijing", "country": "China"}]

affiliations:
["Institute of Automation, Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "Bytedance Inc."]


title: ManiVideo: Generating Hand-Object Manipulation Video with Dexterous and Generalizable Grasping
authors: Youxin Pang, Ruizhi Shao, Jiajun Zhang, Hanzhang Tu, Yun Liu, Boyao Zhou, Hongwen Zhang, Yebin Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12209-12219
cities: ["Beijing", "Beijing", "Beijing"]
countries: ["China", "China", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pang_ManiVideo_Generating_Hand-Object_Manipulation_Video_with_Dexterous_and_Generalizable_Grasping_CVPR_2025_paper.html

abstract:
In this paper, we introduce ManiVideo, a novel method for generating consistent and temporally coherent bimanual hand-object manipulation videos from given motion sequences of hands and objects. The core idea of ManiVideo is the construction of a multi-layer occlusion (MLO) representation that learns 3D occlusion relationships from occlusion-free normal maps and occlusion confidence maps. By embedding the MLO structure into the UNet in two forms, the model enhances the 3D consistency of dexterous hand-object manipulation. To further achieve the generalizable grasping of objects, we integrate Objaverse, a large-scale 3D object dataset, to address the scarcity of video data, thereby facilitating the learning of extensive object consistency. Additionally, we propose an innovative training strategy that effectively integrates multiple datasets, supporting downstream tasks such as human-centric hand-object manipulation video generation. Through extensive experiments, we demonstrate that our approach not only achieves video generation with plausible hand-object interaction and generalizable objects, but also outperforms existing SOTA methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pang_ManiVideo_Generating_Hand-Object_Manipulation_Video_with_Dexterous_and_Generalizable_Grasping_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pang_ManiVideo_Generating_Hand-Object_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Pang_2025_CVPR,
    author    = {Pang, Youxin and Shao, Ruizhi and Zhang, Jiajun and Tu, Hanzhang and Liu, Yun and Zhou, Boyao and Zhang, Hongwen and Liu, Yebin},
    title     = {ManiVideo: Generating Hand-Object Manipulation Video with Dexterous and Generalizable Grasping},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12209-12219}
}

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

affiliations:
["Tsinghua University", "Beijing University of Posts and Telecommunications", "Beijing Normal University"]


authors: Yujie Wang, Praneeth Chakravarthula, Baoquan Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21297-21306
cities: ["Beijing", "Beijing", "Chapel Hill"]
countries: ["China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_DOF-GS_Adjustable_Depth-of-Field_3D_Gaussian_Splatting_for_Post-Capture_Refocusing_Defocus_CVPR_2025_paper.html

title:
DOF-GS: Adjustable Depth-of-Field 3D Gaussian Splatting for Post-Capture Refocusing, Defocus Rendering and Blur Removal

abstract:
3D Gaussian Splatting (3DGS) techniques have recently enabled high-quality 3D scene reconstruction and real-time novel view synthesis. These approaches, however, are limited by the pinhole camera model and lack effective modeling of defocus effects. Departing from this, we introduce DOF-- a new 3DGS-based framework with a finite-aperture camera model and explicit, differentiable defocus rendering, enabling it to function as a post-capture control tool. By training with multi-view images with moderate defocus blur, DOF-GS learns inherent camera characteristics and reconstructs sharp details of the underlying scene, particularly, enabling rendering of varying DOF effects through on-demand aperture and focal distance control, post-capture and optimization. Additionally, our framework extracts circle-of-confusion cues during optimization to identify in-focus regions in input views, enhancing the reconstructed 3D scene details. Experimental results demonstrate that DOF-GS supports post-capture refocusing, adjustable defocus and high-quality all-in-focus rendering, from multi-view images with uncalibrated defocus blur.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_DOF-GS_Adjustable_Depth-of-Field_3D_Gaussian_Splatting_for_Post-Capture_Refocusing_Defocus_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_DOF-GS_Adjustable_Depth-of-Field_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yujie and Chakravarthula, Praneeth and Chen, Baoquan},
    title     = {DOF-GS: Adjustable Depth-of-Field 3D Gaussian Splatting for Post-Capture Refocusing, Defocus Rendering and Blur Removal},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21297-21306}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of General Artificial Intelligence, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Intelligence Science and Technology, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "University of North Carolina at Chapel Hill", "city": "Chapel Hill", "country": "USA"}]

affiliations:
["State Key Laboratory of General Artificial Intelligence, Peking University", "School of Intelligence Science and Technology, Peking University", "University of North Carolina at Chapel Hill"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24807-24816
affiliations: ["University of Virginia", "Adobe"]
cities: ["Charlottesville", "San Jose"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qi_The_Photographers_Eye_Teaching_Multimodal_Large_Language_Models_to_See_CVPR_2025_paper.html

title:
The Photographer's Eye: Teaching Multimodal Large Language Models to See, and Critique Like Photographers

authors:
Daiqing Qi, Handong Zhao, Jing Shi, Simon Jenni, Yifei Fan, Franck Dernoncourt, Scott Cohen, Sheng Li

abstract:
Photographer, curator, and former director of photography at the Museum of Modern Art (MoMA), John Szarkowski remarked in *William Eggleston's Guide*, "While editing directly from life, photographers have found it too difficult to see simultaneously both the blue and the sky." Szarkowski insightfully revealed a notable gap between general and aesthetic visual understanding: while the former emphasizes identifying factual elements in an image (the sky), the latter transcends mere object identification, viewing it instead as an aesthetic component--a pure expanse of blue, valued purely as a color block in visual aesthetics. Such distinctions between general visual understanding (detection, localization, etc.) and aesthetic perception (color, lighting, composition, etc.) pose a significant challenge for existing Multimodal Large Language Models (MLLMs) in comprehending image aesthetics, which is increasingly needed in real-world applications, from image recommendation and enhancement to generation. To fundamentally advance the aesthetic understanding of MLLMs, we introduce a novel dataset, PhotoCritique, derived from extensive discussions among professional photographers and enthusiasts, distinguished by its large scale, expertise, and diversity. Additionally, we propose a new model, PhotoEye, an MLLM featuring a language-guided multi-view vision fusion mechanism for understanding image aesthetics from multiple perspectives. Finally, we introduce PhotoBench, a comprehensive and professional benchmark for aesthetic visual understanding. Our model demonstrates significant advantages over both open-source and commercial models on existing benchmarks and PhotoBench.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qi_The_Photographers_Eye_Teaching_Multimodal_Large_Language_Models_to_See_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qi_The_Photographers_Eye_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qi_2025_CVPR,
    author    = {Qi, Daiqing and Zhao, Handong and Shi, Jing and Jenni, Simon and Fan, Yifei and Dernoncourt, Franck and Cohen, Scott and Li, Sheng},
    title     = {The Photographer's Eye: Teaching Multimodal Large Language Models to See, and Critique Like Photographers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24807-24816}
}

raw_affiliation:
[{"affiliation": "University of Virginia", "city": "Charlottesville", "country": "USA"}, {"affiliation": "Adobe", "city": "San Jose", "country": "USA"}]


