# papers

title: Activating Sparse Part Concepts for 3D Class Incremental Learning
authors: Zhenya Tian, Jun Xiao, Lupeng Liu, Haiyong Jiang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30343-30353
affiliations: ["School of Artificial Intelligence, University of Chinese Academy of Sciences"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tian_Activating_Sparse_Part_Concepts_for_3D_Class_Incremental_Learning_CVPR_2025_paper.html

abstract:
This work tackles the challenge of 3D Class-Incremental Learning (CIL), where a model must learn to classify new 3D objects while retaining knowledge of previously learned classes. Existing methods often struggle with catastrophic forgetting, misclassifying old objects due to overreliance on shortcut local features. Our approach addresses this issue by learning a set of part concepts for part-aware features. Particularly, we only activate a small subset of part concepts for the feature representation of each part-aware feature. This facilitates better generalization across categories and mitigates catastrophic forgetting. We further improve the task-wise classification through a part relation-aware Transformer design. At last, we devise learnable affinities to fuse task-wise classification heads and avoid confusion among different tasks. We evaluate our method on three 3D CIL benchmarks, achieving state-of-the-art performance. Code is available at https://github.com/zhenyatian/ILPC.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tian_Activating_Sparse_Part_Concepts_for_3D_Class_Incremental_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tian_Activating_Sparse_Part_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Tian_2025_CVPR,
    author    = {Tian, Zhenya and Xiao, Jun and Liu, Lupeng and Jiang, Haiyong},
    title     = {Activating Sparse Part Concepts for 3D Class Incremental Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30343-30353}
}

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


title: ProxyTransformation: Preshaping Point Cloud Manifold With Proxy Attention For 3D Visual Grounding
authors: Qihang Peng, Henry Zheng, Gao Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24582-24592
raw_affiliation: [{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]
affiliations: ["Tsinghua University"]
cities: ["Beijing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Peng_ProxyTransformation_Preshaping_Point_Cloud_Manifold_With_Proxy_Attention_For_3D_CVPR_2025_paper.html

abstract:
Embodied intelligence requires agents to interact with 3D environments in real time based on language instructions. A foundational task in this domain is ego-centric 3D visual grounding. However, the point clouds rendered from RGB-D images retain a large amount of redundant background data and inherent noise, both of which can interfere with the manifold structure of the target regions. Existing point cloud enhancement methods often require a tedious process to improve the manifold, which is not suitable for real-time tasks. We propose Proxy Transformation suitable for multimodal task to efficiently improve the point cloud manifold. Our method first leverages Deformable Point Clustering to identify the point cloud sub-manifolds in target regions. Then, we propose a Proxy Attention module that utilizes multimodal proxies to guide point cloud transformation. Built upon Proxy Attention, we design a submanifold transformation generation module where textual information globally guides translation vectors for different submanifolds, optimizing relative spatial relationships of target regions. Simultaneously, image information guides linear transformations within each submanifold, refining the local point cloud manifold of target regions. Extensive experiments demonstrate that Proxy Transformation significantly outperforms all existing methods, achieving an impressive improvement of 7.49% on easy targets and 4.60% on hard targets, while reducing the computational overhead of attention blocks by 40.6%. These results establish a new SOTA in ego-centric 3D visual grounding, showcasing the effectiveness and robustness of our approach.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Peng_ProxyTransformation_Preshaping_Point_Cloud_Manifold_With_Proxy_Attention_For_3D_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Peng_ProxyTransformation_Preshaping_Point_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Peng_2025_CVPR,
    author    = {Peng, Qihang and Zheng, Henry and Huang, Gao},
    title     = {ProxyTransformation: Preshaping Point Cloud Manifold With Proxy Attention For 3D Visual Grounding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24582-24592}
}


title: BFANet: Revisiting 3D Semantic Segmentation with Boundary Feature Analysis
authors: Weiguang Zhao, Rui Zhang, Qiufeng Wang, Guangliang Cheng, Kaizhu Huang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29395-29405
affiliations: ["University of Liverpool", "Xi\u2019an Jiaotong-Liverpool University", "Duke Kunshan University"]
cities: ["Liverpool", "Suzhou", "Kunshan"]
countries: ["United Kingdom", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhao_BFANet_Revisiting_3D_Semantic_Segmentation_with_Boundary_Feature_Analysis_CVPR_2025_paper.html

abstract:
3D semantic segmentation plays a fundamental and crucial role to understand 3D scenes. While contemporary state-of-the-art techniques predominantly concentrate on elevating the overall performance of 3D semantic segmentation based on general metrics (e.g. mIoU, mAcc, and oAcc),  they unfortunately leave the exploration of challenging regions for segmentation mostly neglected. In this paper, we revisit 3D semantic segmentation through a more granular lens, shedding light on subtle complexities that are typically overshadowed by broader performance metrics. Concretely, we have delineated 3D semantic segmentation errors into four comprehensive categories as well as corresponding evaluation metrics tailored to each. Building upon this categorical framework, we introduce an innovative 3D semantic segmentation network called BFANet that incorporates detailed analysis of semantic boundary features. First, we design the boundary-semantic module to decouple point cloud features into semantic and boundary features, and fuse their query queue to enhance semantic features with  attention. Second, we introduce a more concise and accelerated boundary pseudo-label calculation algorithm, which is 3.9 times faster than the state-of-the-art, offering compatibility with data augmentation and enabling efficient computation in training. Extensive experiments on benchmark data indicate the superiority of our BFANet model, confirming the significance of emphasizing the four uniquely designed metrics. Code is available at https://github.com/weiguangzhao/BFANet.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhao_BFANet_Revisiting_3D_Semantic_Segmentation_with_Boundary_Feature_Analysis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhao_BFANet_Revisiting_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhao_2025_CVPR,
    author    = {Zhao, Weiguang and Zhang, Rui and Wang, Qiufeng and Cheng, Guangliang and Huang, Kaizhu},
    title     = {BFANet: Revisiting 3D Semantic Segmentation with Boundary Feature Analysis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29395-29405}
}

raw_affiliation:
[{"affiliation": "University of Liverpool", "city": "Liverpool", "country": "United Kingdom"}, {"affiliation": "Xi\u2019an Jiaotong-Liverpool University", "city": "Suzhou", "country": "China"}, {"affiliation": "Duke Kunshan University", "city": "Kunshan", "country": "China"}]


title: Stable Flow: Vital Layers for Training-Free Image Editing
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7877-7888
cities: ["Unknown", "Jerusalem", "Tel Aviv", "Herzliya"]
countries: ["Unknown", "Israel", "Israel", "Israel"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Avrahami_Stable_Flow_Vital_Layers_for_Training-Free_Image_Editing_CVPR_2025_paper.html

authors:
Omri Avrahami, Or Patashnik, Ohad Fried, Egor Nemchinov, Kfir Aberman, Dani Lischinski, Daniel Cohen-Or

abstract:
Diffusion models have revolutionized the field of content synthesis and editing. Recent models have replaced the traditional UNet architecture with the Diffusion Transformer (DiT), and employed flow-matching for improved training and sampling. However, they exhibit limited generation diversity. In this work, we leverage this limitation to perform consistent image edits via selective injection of attention features. The main challenge is that, unlike the UNet-based models, DiT lacks a coarse-to-fine synthesis structure, making it unclear in which layers to perform the injection. Therefore, we propose an automatic method to identify "vital layers" within DiT, crucial for image formation, and demonstrate how these layers facilitate a range of controlled stable edits, from non-rigid modifications to object addition, using the same mechanism. Next, to enable real-image editing, we introduce an improved image inversion method for flow models. Finally, we evaluate our approach through qualitative and quantitative comparisons, along with a user study, and demonstrate its effectiveness across multiple applications.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Avrahami_Stable_Flow_Vital_Layers_for_Training-Free_Image_Editing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Avrahami_Stable_Flow_Vital_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Avrahami_2025_CVPR,
    author    = {Avrahami, Omri and Patashnik, Or and Fried, Ohad and Nemchinov, Egor and Aberman, Kfir and Lischinski, Dani and Cohen-Or, Daniel},
    title     = {Stable Flow: Vital Layers for Training-Free Image Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7877-7888}
}

raw_affiliation:
[{"affiliation": "Snap Research", "city": "Unknown", "country": "Unknown"}, {"affiliation": "The Hebrew University of Jerusalem", "city": "Jerusalem", "country": "Israel"}, {"affiliation": "Tel Aviv University", "city": "Tel Aviv", "country": "Israel"}, {"affiliation": "Reichman University", "city": "Herzliya", "country": "Israel"}]

affiliations:
["Snap Research", "The Hebrew University of Jerusalem", "Tel Aviv University", "Reichman University"]


title: Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19691-19701
cities: ["Laurel", "Baltimore", "Baltimore", "Adelphi"]
countries: ["USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.html

authors:
Arun Reddy, Alexander Martin, Eugene Yang, Andrew Yates, Kate Sanders, Kenton Murray, Reno Kriz, Celso M. de Melo, Benjamin Van Durme, Rama Chellappa

abstract:
In this work, we tackle the problem of text-to-video retrieval (T2VR). Inspired by the success of late interaction techniques in text-document, text-image, and text-video retrieval, our approach, Video-ColBERT, introduces a simple and efficient mechanism for fine-grained similarity assessment between queries and videos. Video-ColBERT is built upon 3 main components: a fine-grained spatial and temporal token-wise interaction, query and visual expansions, and a dual sigmoid loss during training. We find that this interaction and training paradigm leads to strong individual, yet compatible, representations for encoding video content. These representations lead to increases in performance on common text-to-video retrieval benchmarks compared to other bi-encoder methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Reddy_Video-ColBERT_Contextualized_Late_Interaction_for_Text-to-Video_Retrieval_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Reddy_Video-ColBERT_Contextualized_Late_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Reddy_2025_CVPR,
    author    = {Reddy, Arun and Martin, Alexander and Yang, Eugene and Yates, Andrew and Sanders, Kate and Murray, Kenton and Kriz, Reno and de Melo, Celso M. and Van Durme, Benjamin and Chellappa, Rama},
    title     = {Video-ColBERT: Contextualized Late Interaction for Text-to-Video Retrieval},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19691-19701}
}

raw_affiliation:
[{"affiliation": "Johns Hopkins Applied Physics Laboratory", "city": "Laurel", "country": "USA"}, {"affiliation": "Johns Hopkins University", "city": "Baltimore", "country": "USA"}, {"affiliation": "Human Language Technology Center of Excellence", "city": "Baltimore", "country": "USA"}, {"affiliation": "DEVCOM Army Research Laboratory", "city": "Adelphi", "country": "USA"}]

affiliations:
["Johns Hopkins Applied Physics Laboratory", "Johns Hopkins University", "Human Language Technology Center of Excellence", "DEVCOM Army Research Laboratory"]


title: Beyond Words: Augmenting Discriminative Richness via Diffusions in Unsupervised Prompt Learning
authors: Hairui Ren, Fan Tang, He Zhao, Zixuan Wang, Dandan Guo, Yi Chang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25135-25144
cities: ["Changchun", "Beijing", "Sydney"]
countries: ["China", "China", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ren_Beyond_Words_Augmenting_Discriminative_Richness_via_Diffusions_in_Unsupervised_Prompt_CVPR_2025_paper.html

abstract:
Fine-tuning vision-language models (VLMs) with large amounts of unlabeled data has recently garnered significant interest. However, a key challenge remains the lack of high-quality pseudo-labeled data. Current pseudo-labeling strategies often struggle with mismatches between semantic and visual information, leading to sub-optimal performance of unsupervised prompt learning (UPL) methods.In this paper, we introduce a simple yet effective approach called Augmenting Discriminative Richness via Diffusions (AiR), toward learning a richer discriminating way to represent the class comprehensively and thus facilitate classification.Specifically, our approach includes a pseudo-label generation module that leverages high-fidelity synthetic samples to create an auxiliary classifier, which captures richer visual variation, bridging text-image-pair classification to a more robust image-image-pair classification. Additionally, we exploit the diversity of diffusion-based synthetic samples to enhance prompt learning, providing greater information for semantic-visual alignment.Extensive experiments on five public benchmarks, including RESISC45 and Flowers102, and across three learning paradigms-UL, SSL, and TRZSL-demonstrate that AiR achieves substantial and consistent performance improvements over state-of-the-art unsupervised prompt learning methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ren_Beyond_Words_Augmenting_Discriminative_Richness_via_Diffusions_in_Unsupervised_Prompt_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ren_Beyond_Words_Augmenting_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ren_2025_CVPR,
    author    = {Ren, Hairui and Tang, Fan and Zhao, He and Wang, Zixuan and Guo, Dandan and Chang, Yi},
    title     = {Beyond Words: Augmenting Discriminative Richness via Diffusions in Unsupervised Prompt Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25135-25144}
}

raw_affiliation:
[{"affiliation": "School of Artificial Intelligence, Jilin University", "city": "Changchun", "country": "China"}, {"affiliation": "Institute of Computing Technology, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "CSIRO\u2019s Data61", "city": "Sydney", "country": "Australia"}]

affiliations:
["School of Artificial Intelligence, Jilin University", "Institute of Computing Technology, Chinese Academy of Sciences", "CSIRO\u2019s Data61"]


title: Unlocking the Potential of Unlabeled Data in Semi-Supervised Domain Generalization
authors: Dongkwan Lee, Kyomin Hwang, Nojun Kwak
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30599-30608
raw_affiliation: [{"affiliation": "Seoul National Univeristy", "city": "Seoul", "country": "South Korea"}]
affiliations: ["Seoul National Univeristy"]
cities: ["Seoul"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lee_Unlocking_the_Potential_of_Unlabeled_Data_in_Semi-Supervised_Domain_Generalization_CVPR_2025_paper.html

abstract:
We address the problem of semi-supervised domain generalization (SSDG), where the distributions of train and test data differ, and only a small amount of labeled data along with a larger amount of unlabeled data are available during training. Existing SSDG methods that leverage only the unlabeled samples for which the model's predictions are highly confident (confident-unlabeled samples), limit the full utilization of the available unlabeled data. To the best of our knowledge, we are the first to explore a method for incorporating the unconfident-unlabeled samples that were previously disregarded in SSDG setting. To this end, we propose UPCSC to utilize these unconfident-unlabeled samples in SSDG that consists of two modules: 1) Unlabeled Proxy-based Contrastive learning (UPC) module, treating unconfident-unlabeled samples as additional negative pairs and 2) Surrogate Class learning (SC) module, generating positive pairs for unconfident-unlabeled samples using their confusing class set. These modules are plug-and-play and do not require any domain labels, which can be easily integrated into existing approaches. Experiments on four widely used SSDG benchmarks demonstrate that our approach consistently improves performance when attached to baselines and outperforms competing plug-and-play methods. We also analyze the role of our method in SSDG, showing that it enhances class-level discriminability and mitigates domain gaps.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lee_Unlocking_the_Potential_of_Unlabeled_Data_in_Semi-Supervised_Domain_Generalization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lee_Unlocking_the_Potential_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lee_2025_CVPR,
    author    = {Lee, Dongkwan and Hwang, Kyomin and Kwak, Nojun},
    title     = {Unlocking the Potential of Unlabeled Data in Semi-Supervised Domain Generalization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30599-30608}
}


title: TokenMotion: Decoupled Motion Control via Token Disentanglement for Human-centric Video Generation
authors: Ruineng Li, Daitao Xing, Huiming Sun, Yuanzhou Ha, Jinglin Shen, Chiuman Ho
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1951-1961
raw_affiliation: [{"affiliation": "OPPO US AI Center", "city": "San Francisco", "country": "USA"}]
affiliations: ["OPPO US AI Center"]
cities: ["San Francisco"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_TokenMotion_Decoupled_Motion_Control_via_Token_Disentanglement_for_Human-centric_Video_CVPR_2025_paper.html

abstract:
Human-centric motion control in video generation remains a critical challenge, particularly when jointly controlling camera movements and human poses in scenarios like the iconic Grammy Glambot moment. While recent video diffusion models have made significant progress, existing approaches struggle with limited motion representations and inadequate integration of camera and human motion controls. In this work, we present TokenMotion, the first DiT-based video diffusion framework that enables fine-grained control over camera motion, human motion, and their joint interaction. We represent camera trajectories and human poses as spatio-temporal tokens to enable local control granularity. Our approach introduces a unified modeling framework utilizing a decouple-and-fuse strategy, bridged by a human-aware dynamic mask that effectively handles the spatially varying nature of combined motion signals. Through extensive experiments, we demonstrate TokenMotion's effectiveness across both text-to-video and image-to-video paradigms, consistently outperforming current state-of-the-art methods in human-centric motion control tasks. Our work represents a significant advancement in controllable video generation, with particular relevance for creative production applications ranging from films to short-form content.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_TokenMotion_Decoupled_Motion_Control_via_Token_Disentanglement_for_Human-centric_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_TokenMotion_Decoupled_Motion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Ruineng and Xing, Daitao and Sun, Huiming and Ha, Yuanzhou and Shen, Jinglin and Ho, Chiuman},
    title     = {TokenMotion: Decoupled Motion Control via Token Disentanglement for Human-centric Video Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1951-1961}
}


title: CholecTrack20: A Multi-Perspective Tracking Dataset for Surgical Tools
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8942-8952
cities: ["Strasbourg", "Basel", "Strasbourg"]
countries: ["France", "Switzerland", "France"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Nwoye_CholecTrack20_A_Multi-Perspective_Tracking_Dataset_for_Surgical_Tools_CVPR_2025_paper.html

authors:
Chinedu Innocent Nwoye, Kareem Elgohary, Anvita Srinivas, Fauzan Zaid, Joël L. Lavanchy, Nicolas Padoy

abstract:
Tool tracking in surgical videos is essential for advancing computer-assisted interventions, such as skill assessment, safety zone estimation, and human-machine collaboration. However, the lack of context-rich datasets limits AI applications in this field. Existing datasets rely on overly generic tracking formalizations that fail to capture surgical-specific dynamics, such as tools moving out of the camera's view or exiting the body. This results in less clinically relevant trajectories and a lack of flexibility for real-world surgical applications. Methods trained on these datasets often struggle with visual challenges such as smoke, reflection, and bleeding, further exposing the limitations of current approaches. We introduce CholecTrack20, a specialized dataset for multi-class, multi-tool tracking in surgical procedures. It redefines tracking formalization with three perspectives: (1) intraoperative, (2) intracorporeal, and (3) visibility, enabling adaptable and clinically meaningful tool trajectories. The dataset comprises 20 full-length surgical videos, annotated at 1 fps, yielding over 35K frames and 65K labeled tool instances. Annotations include spatial location, category, identity, operator, phase, and visual challenges. Benchmarking state-of-the-art methods on CholecTrack20 reveals significant performance gaps, with current approaches (<45% HOTA) failing to meet the accuracy required for clinical translation. These findings motivate the need for advanced and intuitive tracking algorithms and establish CholecTrack20 as a foundation for developing robust AI-driven surgical assistance systems.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Nwoye_CholecTrack20_A_Multi-Perspective_Tracking_Dataset_for_Surgical_Tools_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Nwoye_CholecTrack20_A_Multi-Perspective_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Nwoye_2025_CVPR,
    author    = {Nwoye, Chinedu Innocent and Elgohary, Kareem and Srinivas, Anvita and Zaid, Fauzan and Lavanchy, Jo\"el L. and Padoy, Nicolas},
    title     = {CholecTrack20: A Multi-Perspective Tracking Dataset for Surgical Tools},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8942-8952}
}

raw_affiliation:
[{"affiliation": "University of Strasbourg, CNRS, INSERM, ICube, UMR7357", "city": "Strasbourg", "country": "France"}, {"affiliation": "University of Basel, University Digestive Health Care Center, Clarunis", "city": "Basel", "country": "Switzerland"}, {"affiliation": "IHU Strasbourg", "city": "Strasbourg", "country": "France"}]

affiliations:
["University of Strasbourg, CNRS, INSERM, ICube, UMR7357", "University of Basel, University Digestive Health Care Center, Clarunis", "IHU Strasbourg"]


title: Visual and Semantic Prompt Collaboration for Generalized Zero-Shot Learning
authors: Huajie Jiang, Zhengxian Li, Xiaohan Yu, Yongli Hu, Baocai Yin, Jian Yang, Yuankai Qi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20275-20285
affiliations: ["Beijing University of Technology", "Macquarie University"]
cities: ["Beijing", "Sydney"]
countries: ["China", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jiang_Visual_and_Semantic_Prompt_Collaboration_for_Generalized_Zero-Shot_Learning_CVPR_2025_paper.html

abstract:
Generalized zero-shot learning aims to recognize both seen and unseen classes with the help of semantic information that is shared among different classes. It inevitably requires consistent visual-semantic alignment. Existing approaches fine-tune the visual backbone by seen-class data to obtain semantic-related visual features, which may cause overfitting on seen classes with a limited number of training images. This paper proposes a novel visual and semantic prompt collaboration framework, which utilizes prompt tuning techniques for efficient feature adaptation. Specifically, we design a visual prompt to integrate the visual information for discriminative feature learning and a semantic prompt to integrate the semantic formation for visual-semantic alignment. To achieve effective prompt information integration, we further design a weak prompt fusion mechanism for the shallow layers and a strong prompt fusion mechanism for the deep layers in the network. Through the collaboration of visual and semantic prompts, we can obtain discriminative semantic-related features for generalized zero-shot image recognition. Extensive experiments demonstrate that our framework consistently achieves favorable performance in both conventional zero-shot learning and generalized zero-shot learning benchmarks compared to other state-of-the-art methods.

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

bibtex:
@InProceedings{Jiang_2025_CVPR,
    author    = {Jiang, Huajie and Li, Zhengxian and Yu, Xiaohan and Hu, Yongli and Yin, Baocai and Yang, Jian and Qi, Yuankai},
    title     = {Visual and Semantic Prompt Collaboration for Generalized Zero-Shot Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20275-20285}
}

raw_affiliation:
[{"affiliation": "Beijing University of Technology", "city": "Beijing", "country": "China"}, {"affiliation": "Macquarie University", "city": "Sydney", "country": "Australia"}]


title: Steering Away from Harm: An Adaptive Approach to Defending Vision Language Model Against Jailbreaks
authors: Han Wang, Gang Wang, Huan Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29947-29957
raw_affiliation: [{"affiliation": "University of Illinois Urbana-Champaign", "city": "Urbana", "country": "USA"}]
affiliations: ["University of Illinois Urbana-Champaign"]
cities: ["Urbana"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Steering_Away_from_Harm_An_Adaptive_Approach_to_Defending_Vision_CVPR_2025_paper.html

abstract:
Vision Language Models (VLMs) can produce unintended and harmful content when exposed to adversarial attacks, particularly because their vision capabilities create new vulnerabilities. Existing defenses, such as input preprocessing, adversarial training, and response evaluation-based methods, are often impractical for real-world deployment due to their high costs. To address this challenge, we propose ASTRA, an efficient and effective defense by adaptively steering models away from adversarial feature directions to resist VLM attacks. Our key procedures involve finding transferable steering vectors representing the direction of harmful response and applying adaptive activation steering to remove these directions at inference time. To create effective steering vectors, we randomly ablate the visual tokens from the adversarial images and identify those most strongly associated with jailbreaks. These tokens are then used to construct steering vectors. During inference, we perform the adaptive steering method that involves the projection between the steering vectors and calibrated activation, resulting in little performance drops on benign inputs while strongly avoiding harmful outputs under adversarial inputs. Extensive experiments across multiple models and baselines demonstrate our state-of-the-art performance and high efficiency in mitigating jailbreak risks. Additionally, ASTRA exhibits good transferability, defending against unseen attacks (i.e., structured-based attack, perturbation-based attack with project gradient descent variants, and text-only attack).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Steering_Away_from_Harm_An_Adaptive_Approach_to_Defending_Vision_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Steering_Away_from_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Han and Wang, Gang and Zhang, Huan},
    title     = {Steering Away from Harm: An Adaptive Approach to Defending Vision Language Model Against Jailbreaks},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29947-29957}
}


authors: Zexin He, Tengfei Wang, Xin Huang, Xingang Pan, Ziwei Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26514-26524
affiliations: ["The Chinese University of Hong Kong", "Shanghai AI Lab", "Nanyang Technological University"]
cities: ["Hong Kong", "Shanghai", "Singapore"]
countries: ["China", "China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/He_Neural_LightRig_Unlocking_Accurate_Object_Normal_and_Material_Estimation_with_CVPR_2025_paper.html

title:
Neural LightRig: Unlocking Accurate Object Normal and Material Estimation with Multi-Light Diffusion

abstract:
Recovering the geometry and materials of objects from a single image is challenging due to its under-constrained nature. In this paper, we present Neural LightRig, a novel framework that boosts intrinsic estimation by leveraging auxiliary multi-lighting conditions from 2D diffusion priors. Specifically, 1) we first leverage illumination priors from large-scale diffusion models to build our multi-light diffusion model on a synthetic relighting dataset with dedicated designs. This diffusion model generates multiple consistent images, each illuminated by point light sources in different directions. 2) By using these varied lighting images to reduce estimation uncertainty, we train a large G-buffer model with a U-Net backbone to accurately predict surface normals and materials. Extensive experiments validate that our approach significantly outperforms state-of-the-art methods, enabling accurate surface normal and PBR material estimation with vivid relighting effects. Code and dataset are available on our project page at https://projects.zxhezexin.com/neural-lightrig.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/He_Neural_LightRig_Unlocking_Accurate_Object_Normal_and_Material_Estimation_with_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/He_Neural_LightRig_Unlocking_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{He_2025_CVPR,
    author    = {He, Zexin and Wang, Tengfei and Huang, Xin and Pan, Xingang and Liu, Ziwei},
    title     = {Neural LightRig: Unlocking Accurate Object Normal and Material Estimation with Multi-Light Diffusion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26514-26524}
}

raw_affiliation:
[{"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Shanghai AI Lab", "city": "Shanghai", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]


title: VidMuse: A Simple Video-to-Music Generation Framework with Long-Short-Term Modeling
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18782-18793
affiliations: ["Hong Kong University of Science and Technology", "Microsoft Research Asia"]
cities: ["Hong Kong", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tian_VidMuse_A_Simple_Video-to-Music_Generation_Framework_with_Long-Short-Term_Modeling_CVPR_2025_paper.html

authors:
Zeyue Tian, Zhaoyang Liu, Ruibin Yuan, Jiahao Pan, Qifeng Liu, Xu Tan, Qifeng Chen, Wei Xue, Yike Guo

abstract:
In this work, we systematically study music generation conditioned solely on the video. First, we present a large-scale dataset by collecting 360K video-music pairs, including various genres such as movie trailers, advertisements, and documentaries. Furthermore, we propose VidMuse, a simple framework for generating music aligned with video inputs. VidMuse stands out by producing high-fidelity music that is both acoustically and semantically aligned with the video. By incorporating local and global visual cues, VidMuse enables the creation of coherent music tracks that consistently match the video content through Long-Short-Term modeling. Through extensive experiments, VidMuse outperforms existing models in terms of audio quality, diversity, and audio-visual alignment. The code and datasets are available at https://vidmuse.github.io/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tian_VidMuse_A_Simple_Video-to-Music_Generation_Framework_with_Long-Short-Term_Modeling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Tian_VidMuse_A_Simple_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Tian_2025_CVPR,
    author    = {Tian, Zeyue and Liu, Zhaoyang and Yuan, Ruibin and Pan, Jiahao and Liu, Qifeng and Tan, Xu and Chen, Qifeng and Xue, Wei and Guo, Yike},
    title     = {VidMuse: A Simple Video-to-Music Generation Framework with Long-Short-Term Modeling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18782-18793}
}

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


title: Human-centered Interactive Learning via MLLMs for Text-to-Image Person Re-identification
authors: Yang Qin, Chao Chen, Zhihang Fu, Dezhong Peng, Xi Peng, Peng Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14390-14399
cities: ["Chengdu", "Unknown", "Chengdu", "Chengdu", "Chengdu"]
countries: ["China", "Unknown", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qin_Human-centered_Interactive_Learning_via_MLLMs_for_Text-to-Image_Person_Re-identification_CVPR_2025_paper.html

abstract:
Despite remarkable advancements in text-to-image person re-identification (TIReID) facilitated by the breakthrough of cross-modal embedding models, existing methods often struggle to distinguish challenging candidate images due to intrinsic limitations, such as network architecture and data quality. To address these issues, we propose an Interactive Cross-modal Learning framework (ICL), which leverages human-centered interaction to enhance the discriminability of text queries through external multimodal knowledge. To achieve this, we propose a plug-and-play Test-time Humane-centered Interaction (TUI) module, which performs visual question answering focused on human characteristics, facilitating multi-round interactions with a multimodal large language model (MLLM) to align query intent with latent target images. Specifically, TUI refines user queries based on the MLLM responses to reduce the gap to the best-matching images, thereby boosting ranking accuracy. Additionally, to address the limitation of low-quality training texts, we introduce a novel Reorganization Data Augmentation (RDA) strategy based on information enrichment and diversity enhancement to enhance query discriminability by enriching, decomposing, and reorganizing person descriptions. Extensive experiments on four TIReID benchmarks, i.e., CUHK-PEDES, CFG-PEDES RSTPReid, RSTPReid, and UFine6926, demonstrate that our method achieves remarkable performance with substantial improvement. The code will be released publicly.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qin_Human-centered_Interactive_Learning_via_MLLMs_for_Text-to-Image_Person_Re-identification_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qin_Human-centered_Interactive_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qin_2025_CVPR,
    author    = {Qin, Yang and Chen, Chao and Fu, Zhihang and Peng, Dezhong and Peng, Xi and Hu, Peng},
    title     = {Human-centered Interactive Learning via MLLMs for Text-to-Image Person Re-identification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14390-14399}
}

raw_affiliation:
[{"affiliation": "College of Computer Science, Sichuan University", "city": "Chengdu", "country": "China"}, {"affiliation": "Independent Researcher", "city": "Unknown", "country": "Unknown"}, {"affiliation": "National Key Laboratory of Fundamental Algorithms and Models for Engineering Simulation, Sichuan University", "city": "Chengdu", "country": "China"}, {"affiliation": "Sichuan National Innovation New Vision UHD Video Technology Co., Ltd", "city": "Chengdu", "country": "China"}, {"affiliation": "Tianfu Jincheng Laboratory", "city": "Chengdu", "country": "China"}]

affiliations:
["College of Computer Science, Sichuan University", "Independent Researcher", "National Key Laboratory of Fundamental Algorithms and Models for Engineering Simulation, Sichuan University", "Sichuan National Innovation New Vision UHD Video Technology Co., Ltd", "Tianfu Jincheng Laboratory"]


title: Conditional Balance: Improving Multi-Conditioning Trade-Offs in Image Generation
authors: Nadav Z. Cohen, Oron Nir, Ariel Shamir
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2641-2650
affiliations: ["Reichman University", "Microsoft Corporation"]
cities: ["Herzliya", "Redmond"]
countries: ["Israel", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cohen_Conditional_Balance_Improving_Multi-Conditioning_Trade-Offs_in_Image_Generation_CVPR_2025_paper.html

abstract:
Balancing content fidelity and artistic style is a pivotal challenge in image generation. While traditional style transfer methods and modern Denoising Diffusion Probabilistic Models (DDPMs) strive to achieve this balance, they often struggle to do so without sacrificing either style, content, or sometimes both. This work addresses this challenge by analyzing the ability of DDPMs to maintain content and style equilibrium. We introduce a novel method to identify sensitivities within the DDPM attention layers, identifying specific layers that correspond to different stylistic aspects. By directing conditional inputs only to these sensitive layers, our approach enables fine-grained control over style and content, significantly reducing issues arising from over-constrained inputs. Our findings demonstrate that this method enhances recent stylization techniques by better aligning style and content, ultimately improving the quality of generated visual content.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cohen_Conditional_Balance_Improving_Multi-Conditioning_Trade-Offs_in_Image_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cohen_Conditional_Balance_Improving_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Cohen_2025_CVPR,
    author    = {Cohen, Nadav Z. and Nir, Oron and Shamir, Ariel},
    title     = {Conditional Balance: Improving Multi-Conditioning Trade-Offs in Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2641-2650}
}

raw_affiliation:
[{"affiliation": "Reichman University", "city": "Herzliya", "country": "Israel"}, {"affiliation": "Microsoft Corporation", "city": "Redmond", "country": "USA"}]


title: KeyFace: Expressive Audio-Driven Facial Animation for Long Sequences via KeyFrame Interpolation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5477-5488
affiliations: ["Imperial College London", "University of Wroc\u0142aw", "T echnical University of Wroclaw"]
cities: ["London", "Wroc\u0142aw", "Wroc\u0142aw"]
countries: ["United Kingdom", "Poland", "Poland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Bigata_KeyFace_Expressive_Audio-Driven_Facial_Animation_for_Long_Sequences_via_KeyFrame_CVPR_2025_paper.html

authors:
Antoni Bigata, Michał Stypułkowski, Rodrigo Mira, Stella Bounareli, Konstantinos Vougioukas, Zoe Landgraf, Nikita Drobyshev, Maciej Zieba, Stavros Petridis, Maja Pantic

abstract:
Current audio-driven facial animation methods achieve impressive results for short videos but suffer from error accumulation and identity drift when extended to longer durations. Existing methods attempt to mitigate this through external spatial control, increasing long-term consistency but compromising the naturalness of motion. We propose KeyFace, a novel two-stage diffusion-based framework, to address these issues. In the first stage, keyframes are generated at a low frame rate, conditioned on audio input and an identity frame, to capture essential facial expressions and movements over extended periods of time. In the second stage, an interpolation model fills in the gaps between keyframes, ensuring smooth transitions and temporal coherence. To further enhance realism, we incorporate continuous emotion representations and handle a wide range of non-speech vocalizations (NSVs), such as laughter and sighs. We also introduce two new evaluation metrics for assessing lip synchronization and NSV generation. Experimental results show that KeyFace outperforms state-of-the-art methods in generating natural, coherent facial animations over extended durations, successfully encompassing NSVs and continuous emotions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Bigata_KeyFace_Expressive_Audio-Driven_Facial_Animation_for_Long_Sequences_via_KeyFrame_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Bigata_KeyFace_Expressive_Audio-Driven_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Bigata_2025_CVPR,
    author    = {Bigata, Antoni and Stypu{\l}kowski, Micha{\l} and Mira, Rodrigo and Bounareli, Stella and Vougioukas, Konstantinos and Landgraf, Zoe and Drobyshev, Nikita and Zieba, Maciej and Petridis, Stavros and Pantic, Maja},
    title     = {KeyFace: Expressive Audio-Driven Facial Animation for Long Sequences via KeyFrame Interpolation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5477-5488}
}

raw_affiliation:
[{"affiliation": "Imperial College London", "city": "London", "country": "United Kingdom"}, {"affiliation": "University of Wroc\u0142aw", "city": "Wroc\u0142aw", "country": "Poland"}, {"affiliation": "T echnical University of Wroclaw", "city": "Wroc\u0142aw", "country": "Poland"}]


title: Context-Enhanced Memory-Refined Transformer for Online Action Detection
authors: Zhanzhong Pang, Fadime Sener, Angela Yao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8700-8710
affiliations: ["National University of Singapore", "Meta Reality Labs", "National University of Singapore"]
cities: ["Singapore", "Menlo Park", "Singapore"]
countries: ["Singapore", "USA", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pang_Context-Enhanced_Memory-Refined_Transformer_for_Online_Action_Detection_CVPR_2025_paper.html

abstract:
Online Action Detection (OAD) detects actions in streaming videos using past observations. State-of-the-art OAD approaches model past observations and their interactions with an anticipated future. The past is encoded using short- and long-term memories to capture immediate and long-range dependencies, while anticipation compensates for missing future context.We identify a training-inference discrepancy in existing OAD methods that hinders learning effectiveness. The training uses varying lengths of short-term memory, while inference relies on a full-length short-term memory. As a remedy, we propose a Context-enhanced Memory-Refined Transformer (CMeRT).  CMeRT introduces a context-enhanced encoder to improve frame representations using additional near-past context.  It also features a memory-refined decoder to leverage near-future generation to enhance performance. CMeRT achieves state-of-the-art in online detection and anticipation on THUMOS'14, CrossTask, and EPIC-Kitchens-100.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pang_Context-Enhanced_Memory-Refined_Transformer_for_Online_Action_Detection_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pang_Context-Enhanced_Memory-Refined_Transformer_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Pang_2025_CVPR,
    author    = {Pang, Zhanzhong and Sener, Fadime and Yao, Angela},
    title     = {Context-Enhanced Memory-Refined Transformer for Online Action Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8700-8710}
}

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


title: Towards Natural Language-Based Document Image Retrieval: New Dataset and Benchmark
authors: Hao Guo, Xugong Qin, Jun Jie Ou Yang, Peng Zhang, Gangyan Zeng, Yubo Li, Hailun Lin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29722-29732
cities: ["Beijing", "Nanjing", "Beijing", "Beijing", "Los Angeles", "Nanjing"]
countries: ["China", "China", "China", "China", "USA", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_Towards_Natural_Language-Based_Document_Image_Retrieval_New_Dataset_and_Benchmark_CVPR_2025_paper.html

abstract:
Document image retrieval (DIR) aims to retrieve document images from a gallery according to a given query. Existing DIR methods are primarily based on image queries that retrieve documents within the same coarse semantic category, e.g., newspapers or receipts. However, these methods struggle to effectively retrieve document images in real-world scenarios where textual queries with fine-grained semantics are usually provided. To bridge this gap, we introduce a new Natural Language-based Document Image Retrieval (NL-DIR) benchmark with corresponding evaluation metrics. In this work, natural language descriptions serve as semantically rich queries for the DIR task. The NL-DIR dataset contains 41K authentic document images, each paired with five high-quality, fine-grained semantic queries generated and evaluated through large language models in conjunction with manual verification. We perform zero-shot and fine-tuning evaluations of existing mainstream contrastive vision-language models and OCR-free visual document understanding (VDU) models. A two-stage retrieval method is further investigated for performance improvement while achieving both time and space efficiency. We hope the proposed NL-DIR benchmark can bring new opportunities and facilitate research for the VDU community. Datasets and codes will be publicly available at huggingface.co/datasets/nianbing/NL-DIR.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_Towards_Natural_Language-Based_Document_Image_Retrieval_New_Dataset_and_Benchmark_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Hao and Qin, Xugong and Yang, Jun Jie Ou and Zhang, Peng and Zeng, Gangyan and Li, Yubo and Lin, Hailun},
    title     = {Towards Natural Language-Based Document Image Retrieval: New Dataset and Benchmark},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29722-29732}
}

raw_affiliation:
[{"affiliation": "Institute of Information Engineering, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Cyber Science and Engineering, Nanjing University of Science and Technology", "city": "Nanjing", "country": "China"}, {"affiliation": "State Key Laboratory of Cyberspace Security Defense", "city": "Beijing", "country": "China"}, {"affiliation": "School of Cyber Security , University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "University of Southern California", "city": "Los Angeles", "country": "USA"}, {"affiliation": "Laboratory for Advanced Computing and Intelligence Engineering", "city": "Nanjing", "country": "China"}]

affiliations:
["Institute of Information Engineering, Chinese Academy of Sciences", "School of Cyber Science and Engineering, Nanjing University of Science and Technology", "State Key Laboratory of Cyberspace Security Defense", "School of Cyber Security , University of Chinese Academy of Sciences", "University of Southern California", "Laboratory for Advanced Computing and Intelligence Engineering"]


title: Mitigating Ambiguities in 3D Classification with Gaussian Splatting
authors: Ruiqi Zhang, Hao Zhu, Jingyi Zhao, Qi Zhang, Xun Cao, Zhan Ma
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27275-27284
affiliations: ["Nanjing University", "Imperial College London", "Vivo Company"]
cities: ["Nanjing", "London", "Dongguan"]
countries: ["China", "United Kingdom", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Mitigating_Ambiguities_in_3D_Classification_with_Gaussian_Splatting_CVPR_2025_paper.html

abstract:
3D classification with point cloud input is a fundamental problem in 3D vision. However, due to the discrete nature and the insufficient material description of point cloud representations, there are ambiguities in distinguishing wire-like and flat surfaces, as well as transparent or reflective objects. To address these issues, we propose Gaussian Splatting (GS) point cloud-based 3D classification. We find that the scale and rotation coefficients in the GS point cloud help characterize surface types. Specifically, wire-like surfaces consist of multiple slender Gaussian ellipsoids, while flat surfaces are composed of a few flat Gaussian ellipsoids. Additionally, the opacity in the GS point cloud represents the transparency characteristics of objects. As a result, ambiguities in point cloud-based 3D classification can be mitigated utilizing GS point cloud as input. To verify the effectiveness of GS point cloud input, we construct the first real-world GS point cloud dataset in the community, which includes 20 categories with 200 objects in each category. Experiments not only validate the superiority of GS point cloud input, especially in distinguishing ambiguous objects, but also demonstrate the generalization ability across different classification methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Mitigating_Ambiguities_in_3D_Classification_with_Gaussian_Splatting_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Mitigating_Ambiguities_in_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Ruiqi and Zhu, Hao and Zhao, Jingyi and Zhang, Qi and Cao, Xun and Ma, Zhan},
    title     = {Mitigating Ambiguities in 3D Classification with Gaussian Splatting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27275-27284}
}

raw_affiliation:
[{"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "Imperial College London", "city": "London", "country": "United Kingdom"}, {"affiliation": "Vivo Company", "city": "Dongguan", "country": "China"}]


authors: Donggoo Jung, Daehyun Kim, Guanghui Wang, Tae Hyun Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17892-17901
affiliations: ["Hanyang University1", "Toronto Metropolitan University2"]
cities: ["Seoul", "Toronto"]
countries: ["South Korea", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jung_Exposure-slot_Exposure-centric_Representations_Learning_with_Slot-in-Slot_Attention_for_Region-aware_Exposure_CVPR_2025_paper.html

title:
Exposure-slot: Exposure-centric Representations Learning with Slot-in-Slot Attention for Region-aware Exposure Correction

abstract:
Image exposure correction enhances images captured under diverse real-world conditions by addressing issues of under- and over-exposure, which can result in the loss of critical details and hinder content recognition. While significant advancements have been made, current methods often fail to achieve optimal feature learning for effective correction.To overcome these challenges, we propose Exposure-slot, a novel framework that integrates a prompt-based slot-in-slot attention mechanism to cluster exposed feature regions and learn exposure-centric features for each cluster. By extending the Slot Attention algorithm with a hierarchical structure, our approach progressively clusters features, enabling precise and region-aware correction. In particular, learnable prompts tailored to exposure characteristics of slots further enhance feature quality, adapting dynamically to varying conditions. Our method delivers superior performance on benchmark datasets, surpassing the current state-of-the-art with a PSNR improvement of over 1.85 dB on the SICE dataset and 0.4 dB on the LCDP dataset, thereby establishing a new benchmark for multi-exposure correction. The source code can be found at:https://github.com/kdhRick2222/Exposure-slot.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jung_Exposure-slot_Exposure-centric_Representations_Learning_with_Slot-in-Slot_Attention_for_Region-aware_Exposure_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jung_Exposure-slot_Exposure-centric_Representations_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jung_2025_CVPR,
    author    = {Jung, Donggoo and Kim, Daehyun and Wang, Guanghui and Kim, Tae Hyun},
    title     = {Exposure-slot: Exposure-centric Representations Learning with Slot-in-Slot Attention for Region-aware Exposure Correction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17892-17901}
}

raw_affiliation:
[{"affiliation": "Hanyang University1", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Toronto Metropolitan University2", "city": "Toronto", "country": "Canada"}]


title: Data-Free Group-Wise Fully Quantized Winograd Convolution via Learnable Scales
authors: Shuokai Pan, Gerti Tuzi, Sudarshan Sreeram, Dibakar Gope
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4091-4100
raw_affiliation: [{"affiliation": "Arm Inc.", "city": "Cambridge", "country": "UK"}]
affiliations: ["Arm Inc."]
cities: ["Cambridge"]
countries: ["UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pan_Data-Free_Group-Wise_Fully_Quantized_Winograd_Convolution_via_Learnable_Scales_CVPR_2025_paper.html

abstract:
Despite the revolutionary breakthroughs of large-scale text-to-image diffusion models for complex vision and downstream tasks, their extremely high computational and storage costs limit their usability. Quantization of diffusion models has been explored in recent works to reduce compute costs and memory bandwidth usage. To further improve inference time, fast convolution algorithms such as Winograd can be used for convolution layers, which account for a significant portion of computations in diffusion models. However, the significant quality loss of fully quantized Winograd using existing coarser-grained post-training quantization methods, combined with the complexity and cost of finetuning the Winograd transformation matrices for such large models to recover quality, makes them unsuitable for large-scale foundation models. Motivated by the presence of a large range of values in them, we investigate the impact of finer-grained group-wise quantization in quantizing diffusion models. While group-wise quantization can largely handle the fully quantized Winograd convolution, it struggles to deal with the large distribution imbalance in a sizable portion of the Winograd domain computation. To reduce range differences in the Winograd domain, we propose finetuning only the scale parameters of the Winograd transform matrices without using any domain-specific training data. Because our method does not depend on any training data, the generalization performance of quantized diffusion models is safely guaranteed. For text-to-image generation task, the 8-bit fully-quantized diffusion model with Winograd provides near-lossless quality (FID and CLIP scores) in comparison to the full-precision model. This, coupled with the development of highly optimized kernels for group-wise fully quantized Winograd, improves CPU wall-clock time by 31.3% when compared to the convolution layers of a diffusion model. For image classification, our method outperforms the state-of-the-art Winograd PTQ method by 1.62% and 2.56% in top-1 ImageNet accuracy on ResNet-18 and ResNet-34, respectively, with Winograd F(6, 3).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pan_Data-Free_Group-Wise_Fully_Quantized_Winograd_Convolution_via_Learnable_Scales_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pan_Data-Free_Group-Wise_Fully_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Pan_2025_CVPR,
    author    = {Pan, Shuokai and Tuzi, Gerti and Sreeram, Sudarshan and Gope, Dibakar},
    title     = {Data-Free Group-Wise Fully Quantized Winograd Convolution via Learnable Scales},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4091-4100}
}


title: EdgeDiff: Edge-aware Diffusion Network for Building Reconstruction from Point Clouds
authors: Yujun Liu, Ruisheng Wang, Shangfeng Huang, Guorong Cai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17008-17018
affiliations: ["Shenzhen University", "University of Calgary", "Jimei University"]
cities: ["Shenzhen", "Calgary", "Xiamen"]
countries: ["China", "Canada", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_EdgeDiff_Edge-aware_Diffusion_Network_for_Building_Reconstruction_from_Point_Clouds_CVPR_2025_paper.html

abstract:
Building reconstruction is a challenging problem at the intersection of computer vision, photogrammetry and computer graphics. 3D wireframe presents a compelling representation for building modeling through its compact structure. Existing wireframe reconstruction methods employing vertex detection and edge regression have achieved promising results. In this paper, we develop an Edge-aware Diffusion network, dubbed EdgeDiff. As a novel paradigm for wireframe reconstruction, the EdgeDiff generates wireframe models from noise using a conditional diffusion model. During the training process, the ground truth wireframes firstly are formulated as a set of parameterized edges and then diffused into a random noise distribution. EdgeDiff learns both the noise reversal process and the network structure simultaneously. During inference, EdgeDiff iteratively refines the generated edge distribution using the denoising diffusion implicit model, enabling flexible single- or multi-step denoising and dynamic adaptation to buildings of varying complexity. Additionally, given the unique structure of wireframes, we introduce an edge attention module to extract point-wise attention from point features, using it as auxiliary information to facilitate learning of edge cues and guide the network toward improved edge awareness. Extensive experiments on the real-world Building3D dataset demonstrate that our approach achieves state-of-the-art performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_EdgeDiff_Edge-aware_Diffusion_Network_for_Building_Reconstruction_from_Point_Clouds_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_EdgeDiff_Edge-aware_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yujun and Wang, Ruisheng and Huang, Shangfeng and Cai, Guorong},
    title     = {EdgeDiff: Edge-aware Diffusion Network for Building Reconstruction from Point Clouds},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17008-17018}
}

raw_affiliation:
[{"affiliation": "Shenzhen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "University of Calgary", "city": "Calgary", "country": "Canada"}, {"affiliation": "Jimei University", "city": "Xiamen", "country": "China"}]


title: GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6121-6132
affiliations: ["NVIDIA", "University of Toronto", "Vector Institute"]
cities: ["Santa Clara", "Toronto", "Toronto"]
countries: ["USA", "Canada", "Canada"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ren_GEN3C_3D-Informed_World-Consistent_Video_Generation_with_Precise_Camera_Control_CVPR_2025_paper.html

authors:
Xuanchi Ren, Tianchang Shen, Jiahui Huang, Huan Ling, Yifan Lu, Merlin Nimier-David, Thomas Müller, Alexander Keller, Sanja Fidler, Jun Gao

abstract:
We present GEN3C, a generative video model with precise Camera Control and temporal 3D Consistency. Prior video models already generate realistic videos, but they tend to leverage little 3D information, leading to inconsistencies, such as objects popping in and out of existence. Camera control, if implemented at all, is imprecise, because camera parameters are mere inputs to the neural network which must then infer how the video depends on the camera. In contrast, GEN3C is guided by a 3D cache: point clouds obtained by predicting the pixel-wise depth of seed images or previously generated frames. When generating the next frames, GEN3C is conditioned on the 2D renderings of the 3D cache with the new camera trajectory provided by the user. Crucially, this means that GEN3C neither has to remember what it previously generated nor does it have to infer the image structure from the camera pose. The model, instead, can focus all its generative power on previously unobserved regions, as well as advancing the scene state to the next frame. Our results demonstrate more precise camera control than prior work, as well as state-of-the-art results in sparse-view novel view synthesis, even in challenging settings such as driving scenes and monocular dynamic video.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ren_GEN3C_3D-Informed_World-Consistent_Video_Generation_with_Precise_Camera_Control_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ren_GEN3C_3D-Informed_World-Consistent_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ren_2025_CVPR,
    author    = {Ren, Xuanchi and Shen, Tianchang and Huang, Jiahui and Ling, Huan and Lu, Yifan and Nimier-David, Merlin and M\"uller, Thomas and Keller, Alexander and Fidler, Sanja and Gao, Jun},
    title     = {GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6121-6132}
}

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


title: A Dataset for Semantic Segmentation in the Presence of Unknowns
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1439-1448
cities: ["Prague", "Zagreb", "Espoo", "Hyderabad", "Oulu"]
countries: ["Czechia", "Croatia", "Finland", "India", "Finland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Laskar_A_Dataset_for_Semantic_Segmentation_in_the_Presence_of_Unknowns_CVPR_2025_paper.html

authors:
Zakaria Laskar, Tomas Vojir, Matej Grcic, Iaroslav Melekhov, Shankar Gangisetty, Juho Kannala, Jiri Matas, Giorgos Tolias, C.V. Jawahar

abstract:
Before deployment in the real-world deep neural networks require thorough evaluation of how they handle both knowns, inputs represented in the training data, and unknowns (anomalies). This is especially important for scene understanding tasks with safety critical applications, such as in autonomous driving. Existing datasets allow evaluation of only either knowns or unknowns - but not both, which is required to establish "in the wild" suitability of deep neural network models. To bridge this gap, we propose a novel anomaly segmentation dataset, ISSU, featuring a diverse set of anomaly inputs from cluttered real-world environments. The dataset is twice larger than existing anomaly segmentation datasets, and provides a training, validation and test set for controlled in-domain evaluation. The test set consists of a static and temporal part, with the later comprised of videos. The dataset provides annotations for both closed-set (knowns) and anomalies, enabling closed-set and open-set evaluation. The dataset covers diverse conditions, such as domain and cross-sensor shift, illumination variation and allows ablation of anomaly detection methods with respect to these variations. Evaluation results of current state-of-the-art methods confirm the need for improvements especially in domain-generalization, small and large object segmentation. The benchmarking code and the dataset will be made available.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Laskar_A_Dataset_for_Semantic_Segmentation_in_the_Presence_of_Unknowns_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Laskar_A_Dataset_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Laskar_2025_CVPR,
    author    = {Laskar, Zakaria and Vojir, Tomas and Grcic, Matej and Melekhov, Iaroslav and Gangisetty, Shankar and Kannala, Juho and Matas, Jiri and Tolias, Giorgos and Jawahar, C.V.},
    title     = {A Dataset for Semantic Segmentation in the Presence of Unknowns},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1439-1448}
}

raw_affiliation:
[{"affiliation": "VRG, FEE, Czech Technical University in Prague", "city": "Prague", "country": "Czechia"}, {"affiliation": "University of Zagreb Faculty of Electrical Engineering and Computing", "city": "Zagreb", "country": "Croatia"}, {"affiliation": "Aalto University", "city": "Espoo", "country": "Finland"}, {"affiliation": "IIIT Hyderabad", "city": "Hyderabad", "country": "India"}, {"affiliation": "University of Oulu", "city": "Oulu", "country": "Finland"}]

affiliations:
["VRG, FEE, Czech Technical University in Prague", "University of Zagreb Faculty of Electrical Engineering and Computing", "Aalto University", "IIIT Hyderabad", "University of Oulu"]


title: HierarQ: Task-Aware Hierarchical Q-Former for Enhanced Video Understanding
authors: Shehreen Azad, Vibhav Vineet, Yogesh Singh Rawat
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8545-8556
affiliations: ["Center for Research in Computer Vision, University of Central Florida", "Microsoft Research"]
cities: ["Orlando", "Redmond"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Azad_HierarQ_Task-Aware_Hierarchical_Q-Former_for_Enhanced_Video_Understanding_CVPR_2025_paper.html

abstract:
Despite advancements in multimodal large language models (MLLMs), current approaches struggle in medium-to-long video understanding due to frame and context length limitations. As a result, these models often depend on frame sampling, which risks missing key information over time and lacks task-specific relevance. To address these challenges, we introduce **HierarQ**, a task-aware hierarchical Q-Former based framework that sequentially processes frames to bypass the need for frame sampling, while avoiding LLM's context length limitations. We introduce a lightweight two-stream language-guided feature modulator to incorporate task awareness in video understanding, with the entity stream capturing frame-level object information within a short context and the scene stream identifying their broader interactions over longer period of time. Each stream is supported by dedicated memory banks which enables our proposed **Hierar**chical **Q**uerying transformer (HierarQ) to effectively capture short and long-term context. Extensive evaluations on **10** video benchmarks across video understanding, question answering, and captioning tasks demonstrate HierarQ's state-of-the-art performance across most datasets, proving its robustness and efficiency for comprehensive video analysis. All code will be made available upon acceptance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Azad_HierarQ_Task-Aware_Hierarchical_Q-Former_for_Enhanced_Video_Understanding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Azad_HierarQ_Task-Aware_Hierarchical_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Azad_2025_CVPR,
    author    = {Azad, Shehreen and Vineet, Vibhav and Rawat, Yogesh Singh},
    title     = {HierarQ: Task-Aware Hierarchical Q-Former for Enhanced Video Understanding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8545-8556}
}

raw_affiliation:
[{"affiliation": "Center for Research in Computer Vision, University of Central Florida", "city": "Orlando", "country": "USA"}, {"affiliation": "Microsoft Research", "city": "Redmond", "country": "USA"}]


title: DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15682-15692
affiliations: ["National Yang Ming Chiao Tung University", "National Taiwan University"]
cities: ["Hsinchu", "Taipei"]
countries: ["Taiwan", "Taiwan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_DeNVeR_Deformable_Neural_Vessel_Representations_for_Unsupervised_Video_Vessel_Segmentation_CVPR_2025_paper.html

authors:
Chun-Hung Wu, Shih-Hong Chen, Chih-Yao Hu, Hsin-Yu Wu, Kai-Hsin Chen, Yu-You Chen, Chih-Hai Su, Chih-Kuo Lee, Yu-Lun Liu

abstract:
This paper presents Deformable Neural Vessel Representations (DeNVeR), an unsupervised approach for vessel segmentation in X-ray angiography videos without annotated ground truth. DeNVeR utilizes optical flow and layer separation techniques, enhancing segmentation accuracy and adaptability through test-time training. Key contributions include a novel layer separation bootstrapping technique, a parallel vessel motion loss, and the integration of Eulerian motion fields for modeling complex vessel dynamics. A significant component of this research is the introduction of the XACV dataset, the first X-ray angiography coronary video dataset with high-quality, manually labeled segmentation ground truth. Extensive evaluations on both XACV and CADICA datasets demonstrate that DeNVeR outperforms current state-of-the-art methods in vessel segmentation accuracy and generalization capability while maintaining temporal coherency. Please see our project page at kirito878.github.io/DeNVeR.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_DeNVeR_Deformable_Neural_Vessel_Representations_for_Unsupervised_Video_Vessel_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_DeNVeR_Deformable_Neural_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Chun-Hung and Chen, Shih-Hong and Hu, Chih-Yao and Wu, Hsin-Yu and Chen, Kai-Hsin and Chen, Yu-You and Su, Chih-Hai and Lee, Chih-Kuo and Liu, Yu-Lun},
    title     = {DeNVeR: Deformable Neural Vessel Representations for Unsupervised Video Vessel Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15682-15692}
}

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


title: DH-Set: Improving Vision-Language Alignment with Diverse and Hybrid Set-Embeddings Learning
authors: Kun Zhang, Jingyu Li, Zhe Li, S.Kevin Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24993-25003
cities: ["Hefei", "Suzhou", "Hefei", "Hefei", "Beijing", "Hefei"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_DH-Set_Improving_Vision-Language_Alignment_with_Diverse_and_Hybrid_Set-Embeddings_Learning_CVPR_2025_paper.html

abstract:
Vision-Language (VL) alignment across image and text modalities is a challenging task due to the inherent semantic ambiguity of data with multiple possible meanings. Existing methods typically solve it by learning multiple sub-representation spaces to encode each input data as a set of embeddings, and constraining diversity between whole subspaces to capture diverse semantics for accurate VL alignment. Despite their promising outcomes, existing methods suffer two imperfections: 1) actually, specific semantics is mainly expressed by some local dimensions within the subspace. Ignoring this intrinsic property, existing diversity constraints imposed on the whole subspace may impair diverse embedding learning; 2) multiple embeddings are inevitably introduced, sacrificing computational and storage efficiency. In this paper, we propose a simple yet effective Diverse and Hybrid Set-embeddings learning framework (DH-Set), which is distinct from prior work in three aspects. DH-Set 1) devises a novel semantic importance dissecting method to focus on key local dimensions within each subspace; and thereby 2) not only imposes finer-grained diversity constraint to improve the accuracy of diverse embedding learning, 3) but also mixes key dimensions of all subspaces into the single hybrid embedding to boost inference efficiency. Extensive experiments on various benchmarks and model backbones show the superiority of DH-Set over state-of-the-art methods, achieving substantial 2.3%-14.7% rSum improvements while lowering computational and storage complexity.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_DH-Set_Improving_Vision-Language_Alignment_with_Diverse_and_Hybrid_Set-Embeddings_Learning_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Kun and Li, Jingyu and Li, Zhe and Zhou, S.Kevin},
    title     = {DH-Set: Improving Vision-Language Alignment with Diverse and Hybrid Set-Embeddings Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24993-25003}
}

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

affiliations:
["School of Biomedical Engineering, Division of Life Sciences and Medicine, USTC", "MIRACLE Center, Suzhou Institute for Advance Research, USTC", "Institute of Artificial Intelligence, Hefei Comprehensive National Science Center", "School of Cyberspace Science and Technology, USTC", "Key Laboratory of Intelligent Information Processing of CAS, ICT, CAS", "State Key Laboratory of Precision and Intelligent Chemistry, USTC"]


title: Task-Aware Clustering for Prompting Vision-Language Models
authors: Fusheng Hao, Fengxiang He, Fuxiang Wu, Tichao Wang, Chengqun Song, Jun Cheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14745-14755
cities: ["Shenzhen", "Hong Kong", "Edinburgh"]
countries: ["China", "China", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hao_Task-Aware_Clustering_for_Prompting_Vision-Language_Models_CVPR_2025_paper.html

abstract:
Prompt learning has attracted widespread attention in adapting vision-language models to downstream tasks. Existing methods largely rely on optimization strategies to ensure the task-awareness of learnable prompts. Due to the scarcity of task-specific data, overfitting is prone to occur. The resulting prompts often do not generalize well or exhibit limited task-awareness. To address this issue, we propose a novel Task-Aware Clustering (TAC) framework for prompting vision-language models, which increases the task-awareness of learnable prompts by introducing task-aware pre-context. The key ingredients are as follows: (a) generating task-aware pre-context based on task-aware clustering that can preserve the backbone structure of a downstream task with only a few clustering centers, (b) enhancing the task-awareness of learnable prompts by enabling them to interact with task-aware pre-context via the well-pretrained encoders, and (c) preventing the visual task-aware pre-context from interfering the interaction between patch embeddings by masked attention mechanism. Extensive experiments are conducted on benchmark datasets, covering the base-to-novel, domain generalization, and cross-dataset transfer settings. Ablation studies validate the effectiveness of key ingredients. Comparative results show the superiority of our TAC over competitive counterparts. The code is available at https://github.com/FushengHao/TAC.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hao_Task-Aware_Clustering_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hao_2025_CVPR,
    author    = {Hao, Fusheng and He, Fengxiang and Wu, Fuxiang and Wang, Tichao and Song, Chengqun and Cheng, Jun},
    title     = {Task-Aware Clustering for Prompting Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14745-14755}
}

raw_affiliation:
[{"affiliation": "Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences", "city": "Shenzhen", "country": "China"}, {"affiliation": "The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "University of Edinburgh", "city": "Edinburgh", "country": "UK"}]

affiliations:
["Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences", "The Chinese University of Hong Kong", "University of Edinburgh"]


title: FSboard: Over 3 Million Characters of ASL Fingerspelling Collected via Smartphones
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13897-13906
cities: ["Mountain View", "New Orleans", "Rochester", "Detroit"]
countries: ["USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Georg_FSboard_Over_3_Million_Characters_of_ASL_Fingerspelling_Collected_via_CVPR_2025_paper.html

authors:
Manfred Georg, Garrett Tanzer, Esha Uboweja, Saad Hassan, Maximus Shengelia, Sam Sepah, Sean Forbes, Thad Starner

abstract:
Progress in machine understanding of sign languages has been slow and hampered by limited data. In this paper, we present FSboard, an American Sign Language fingerspelling dataset situated in a mobile text entry use case, collected from 147 paid and consenting Deaf signers using Pixel 4A selfie cameras in a variety of environments. Fingerspelling recognition is an incomplete solution that is only one small part of sign language translation, but it could provide some immediate benefit to Deaf/Hard of Hearing signers while more broadly capable technology develops. At >3 million characters in length and >250 hours in duration, FSboard is the largest fingerspelling recognition dataset to date by a factor of >10x. As a simple baseline, we finetune 30 Hz MediaPipe Holistic landmark inputs into ByT5-Small and achieve 11.1% Character Error Rate (CER) on a test set with unique phrases and signers. This quality degrades gracefully when decreasing frame rate and excluding face/body landmarks---plausible optimizations to help with on-device performance---but falls short of human performance measured at 2.2% CER.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Georg_FSboard_Over_3_Million_Characters_of_ASL_Fingerspelling_Collected_via_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Georg_FSboard_Over_3_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Georg_2025_CVPR,
    author    = {Georg, Manfred and Tanzer, Garrett and Uboweja, Esha and Hassan, Saad and Shengelia, Maximus and Sepah, Sam and Forbes, Sean and Starner, Thad},
    title     = {FSboard: Over 3 Million Characters of ASL Fingerspelling Collected via Smartphones},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13897-13906}
}

raw_affiliation:
[{"affiliation": "Google", "city": "Mountain View", "country": "USA"}, {"affiliation": "Tulane University", "city": "New Orleans", "country": "USA"}, {"affiliation": "Rochester Institute of Technology", "city": "Rochester", "country": "USA"}, {"affiliation": "Deaf Professional Arts Network", "city": "Detroit", "country": "USA"}]

affiliations:
["Google", "Tulane University", "Rochester Institute of Technology", "Deaf Professional Arts Network"]


title: CASP: Compression of Large Multimodal Models Based on Attention Sparsity
authors: Mohsen Gholami, Mohammad Akbari, Kevin Cannons, Yong Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9372-9381
raw_affiliation: [{"affiliation": "Huawei Technologies Canada Co., Ltd.", "city": "Ottawa", "country": "Canada"}]
affiliations: ["Huawei Technologies Canada Co., Ltd."]
cities: ["Ottawa"]
countries: ["Canada"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gholami_CASP_Compression_of_Large_Multimodal_Models_Based_on_Attention_Sparsity_CVPR_2025_paper.html

abstract:
In this work, we propose an extreme compression technique for Large Multimodal Models (LMMs). While previous studies have explored quantization as an efficient post-training compression method for Large Language Models (LLMs), low-bit compression for multimodal models remains under-explored. The redundant nature of inputs in multimodal models results in a highly sparse attention matrix. We theoretically and experimentally demonstrate that the attention matrix's sparsity bounds the compression error of the Query and Key weight matrices. Based on this, we introduce CASP, a model compression technique for LMMs. Our approach performs a data-aware low-rank decomposition on the Query and Key weight matrix, followed by quantization across all layers based on an optimal bit allocation process. CASP is compatible with any quantization technique and enhances state-of-the-art 2-bit quantization methods (AQLM and QuIP#) by an average of 21% on image- and video-language benchmarks. The code is provided in the supplementary materials.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gholami_CASP_Compression_of_Large_Multimodal_Models_Based_on_Attention_Sparsity_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gholami_CASP_Compression_of_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gholami_2025_CVPR,
    author    = {Gholami, Mohsen and Akbari, Mohammad and Cannons, Kevin and Zhang, Yong},
    title     = {CASP: Compression of Large Multimodal Models Based on Attention Sparsity},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9372-9381}
}


title: UNIC-Adapter: Unified Image-instruction Adapter with Multi-modal Transformer for Image Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7963-7973
cities: ["Wuhan", "Hangzhou", "Melbourne", "Wuhan"]
countries: ["China", "China", "Australia", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Duan_UNIC-Adapter_Unified_Image-instruction_Adapter_with_Multi-modal_Transformer_for_Image_Generation_CVPR_2025_paper.html

authors:
Lunhao Duan, Shanshan Zhao, Wenjun Yan, Yinglun Li, Qing-Guo Chen, Zhao Xu, Weihua Luo, Kaifu Zhang, Mingming Gong, Gui-Song Xia

abstract:
Recently, text-to-image generation models have achieved remarkable advancements, particularly with diffusion models facilitating high-quality image synthesis from textual descriptions. However, these models often struggle with achieving precise control over pixel-level layouts, object appearances, and global styles when using text prompts alone. To mitigate this issue, previous works introduce conditional images as auxiliary inputs for image generation, enhancing control but typically necessitating specialized models tailored to different types of reference inputs. In this paper, we explore a new approach to unify controllable generation within a single framework. Specifically, we propose the unified image-instruction adapter (UNIC-Adapter) built on the Multi-Modal-Diffusion Transformer architecture, to enable flexible and controllable generation across diverse conditions without the need for multiple specialized models. Our UNIC-Adapter effectively extracts multi-modal instruction information by incorporating both conditional images and task instructions, injecting this information into the image generation process through a cross-attention mechanism enhanced by Rotary Position Embedding. Experimental results across a variety of tasks, including pixel-level spatial control, subject-driven image generation, and style-image-based image synthesis, demonstrate the effectiveness of our UNIC-Adapter in unified controllable image generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Duan_UNIC-Adapter_Unified_Image-instruction_Adapter_with_Multi-modal_Transformer_for_Image_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Duan_UNIC-Adapter_Unified_Image-instruction_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Duan_2025_CVPR,
    author    = {Duan, Lunhao and Zhao, Shanshan and Yan, Wenjun and Li, Yinglun and Chen, Qing-Guo and Xu, Zhao and Luo, Weihua and Zhang, Kaifu and Gong, Mingming and Xia, Gui-Song},
    title     = {UNIC-Adapter: Unified Image-instruction Adapter with Multi-modal Transformer for Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7963-7973}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Alibaba International Digital Commerce", "city": "Hangzhou", "country": "China"}, {"affiliation": "University of Melbourne", "city": "Melbourne", "country": "Australia"}, {"affiliation": "School of Artificial Intelligence, Wuhan University", "city": "Wuhan", "country": "China"}]

affiliations:
["School of Computer Science, Wuhan University", "Alibaba International Digital Commerce", "University of Melbourne", "School of Artificial Intelligence, Wuhan University"]


title: Towards Cost-Effective Learning: A Synergy of Semi-Supervised and Active Learning
authors: Tianxiang Yin, Ningzhong Liu, Han Sun
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10163-10172
cities: ["Nanjing", "Luoyang", "Nanjing"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yin_Towards_Cost-Effective_Learning_A_Synergy_of_Semi-Supervised_and_Active_Learning_CVPR_2025_paper.html

abstract:
Active learning (AL) and semi-supervised learning (SSL) both aim to reduce annotation costs: AL selectively annotates high-value samples from the unlabeled data, while SSL leverages abundant unlabeled data to improve model performance. Although these two appear intuitively compatible, directly combining them remains challenging due to fundamental differences in their frameworks. Current semi-supervised active learning (SSAL) methods often lack theoretical foundations and often design AL strategies tailored to a specific SSL algorithm rather than genuinely integrating the two fields.In this paper, we incorporate AL objectives into the overall risk formulation within the mainstream pseudo-label-based SSL framework, clarifying key differences between SSAL and traditional AL scenarios. To bridge these gaps, we propose a feature re-alignment module that aligns the features of unlabeled data under different augmentations by leveraging clustering and consistency constraints. Experimental results demonstrate that our module enables flexible combinations of SOTA methods from both AL and SSL, yielding more efficient algorithm performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yin_Towards_Cost-Effective_Learning_A_Synergy_of_Semi-Supervised_and_Active_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yin_Towards_Cost-Effective_Learning_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yin_2025_CVPR,
    author    = {Yin, Tianxiang and Liu, Ningzhong and Sun, Han},
    title     = {Towards Cost-Effective Learning: A Synergy of Semi-Supervised and Active Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10163-10172}
}

raw_affiliation:
[{"affiliation": "College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics", "city": "Nanjing", "country": "China"}, {"affiliation": "School of Computer Science/School of Software, Luoyang Institute of Science and Technology", "city": "Luoyang", "country": "China"}, {"affiliation": "MIIT Key Laboratory of Pattern Analysis and Machine Intelligence", "city": "Nanjing", "country": "China"}]

affiliations:
["College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics", "School of Computer Science/School of Software, Luoyang Institute of Science and Technology", "MIIT Key Laboratory of Pattern Analysis and Machine Intelligence"]


title: Unveil Inversion and Invariance in Flow Transformer for Versatile Image Editing
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28479-28489
affiliations: ["Western University", "Tencent"]
cities: ["London", "Shenzhen"]
countries: ["Canada", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_Unveil_Inversion_and_Invariance_in_Flow_Transformer_for_Versatile_Image_CVPR_2025_paper.html

authors:
Pengcheng Xu, Boyuan Jiang, Xiaobin Hu, Donghao Luo, Qingdong He, Jiangning Zhang, Chengjie Wang, Yunsheng Wu, Charles Ling, Boyu Wang

abstract:
Leveraging the large generative prior of the flow transformer for tuning-free image editing requires authentic inversion to project the image into the model's domain and a flexible invariance control mechanism to preserve non-target contents. However, the prevailing diffusion inversion performs deficiently in flow-based models, and the invariance control cannot reconcile diverse rigid and non-rigid editing tasks. To address these, we systematically analyze the inversion and invariance control based on the flow transformer. Specifically, we unveil that the Euler inversion shares a similar structure to DDIM yet is more susceptible to the approximation error. Thus, we propose a two-stage inversion to first refine the velocity estimation and then compensate for the leftover error, which pivots closely to the model prior and benefits editing. Meanwhile, we propose the invariance control that manipulates the text features within the adaptive layer normalization, connecting the changes in the text prompt to image semantics. This mechanism can simultaneously preserve the non-target contents while allowing rigid and non-rigid manipulation, enabling a wide range of editing types. Experiments on various scenarios demonstrate that our framework achieves flexible and accurate editing, unlocking the potential of the flow transformer for versatile image editing.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_Unveil_Inversion_and_Invariance_in_Flow_Transformer_for_Versatile_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_Unveil_Inversion_and_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Pengcheng and Jiang, Boyuan and Hu, Xiaobin and Luo, Donghao and He, Qingdong and Zhang, Jiangning and Wang, Chengjie and Wu, Yunsheng and Ling, Charles and Wang, Boyu},
    title     = {Unveil Inversion and Invariance in Flow Transformer for Versatile Image Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28479-28489}
}

raw_affiliation:
[{"affiliation": "Western University", "city": "London", "country": "Canada"}, {"affiliation": "Tencent", "city": "Shenzhen", "country": "China"}]


title: Light Transport-aware Diffusion Posterior Sampling for Single-View Reconstruction of 3D Volumes
authors: Ludwic Leonard, Nils Thurey, Rüdiger Westermann
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16163-16174
raw_affiliation: [{"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}]
affiliations: ["Technical University of Munich"]
cities: ["Munich"]
countries: ["Germany"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Leonard_Light_Transport-aware_Diffusion_Posterior_Sampling_for_Single-View_Reconstruction_of_3D_CVPR_2025_paper.html

abstract:
We introduce a single-view reconstruction technique of volumetric fields in which multiple light scattering effects are omnipresent, such as in clouds. We model the unknown distribution of volumetric fields using an unconditional diffusion model trained on a novel benchmark dataset comprising 1,000 synthetically simulated volumetric density fields. The neural diffusion model is trained on the latent codes of a novel, diffusion-friendly, monoplanar representation. The generative model is used to incorporate a tailored parametric diffusion posterior sampling technique into different reconstruction tasks. A physically-based differentiable volume renderer is employed to provide gradients with respect to light transport in the latent space. This stands in contrast to classic NeRF approaches and makes the reconstructions better aligned with observed data. Through various experiments, we demonstrate single-view reconstruction of volumetric clouds at a previously unattainable quality.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Leonard_Light_Transport-aware_Diffusion_Posterior_Sampling_for_Single-View_Reconstruction_of_3D_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Leonard_Light_Transport-aware_Diffusion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Leonard_2025_CVPR,
    author    = {Leonard, Ludwic and Thurey, Nils and Westermann, R\"udiger},
    title     = {Light Transport-aware Diffusion Posterior Sampling for Single-View Reconstruction of 3D Volumes},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16163-16174}
}


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30850-30860
affiliations: ["C2PS - Khalifa University of Science and Technology"]
cities: ["Abu Dhabi"]
countries: ["UAE"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Assefa_DyCON_Dynamic_Uncertainty-aware_Consistency_and_Contrastive_Learning_for_Semi-supervised_Medical_CVPR_2025_paper.html

title:
DyCON: Dynamic Uncertainty-aware Consistency and Contrastive Learning for Semi-supervised Medical Image Segmentation

authors:
Maregu Assefa, Muzammal Naseer, Iyyakutti Iyappan Ganapathi, Syed Sadaf Ali, Mohamed L Seghier, Naoufel Werghi

abstract:
Semi-supervised learning in medical image segmentation leverages unlabeled data to reduce annotation burdens through consistency learning. However, current methods struggle with class imbalance and high uncertainty from pathology variations, leading to inaccurate segmentation in 3D medical images. To address these challenges, we present DyCON, a Dynamic Uncertainty-aware Consistency and Contrastive Learning framework that enhances the generalization of consistency methods with two complementary losses: Uncertainty-aware Consistency Loss (UnCL) and Focal Entropy-aware Contrastive Loss (FeCL). UnCL enforces global consistency by dynamically weighting the contribution of each voxel to the consistency loss based on its uncertainty, preserving high-uncertainty regions instead of filtering them out. Initially, UnCL prioritizes learning from uncertain voxels with lower penalties, encouraging the model to explore challenging regions. As training progress, the penalty shift towards confident voxels to refine predictions and ensure global consistency. Meanwhile, FeCL enhances local feature discrimination in imbalanced regions by introducing dual focal mechanisms and adaptive confidence adjustments into the contrastive principle. These mechanisms jointly prioritizes hard positives and negatives while focusing on uncertain sample pairs, effectively capturing subtle lesion variations under class imbalance. Extensive evaluations on four diverse medical image segmentation datasets (ISLES'22, BraTS'19, LA, Pancreas) show DyCON's superior performance against SOTA methods(\href https://dycon25.github.io/) Project Page: https://dycon25.github.io/  .

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Assefa_DyCON_Dynamic_Uncertainty-aware_Consistency_and_Contrastive_Learning_for_Semi-supervised_Medical_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{Assefa_2025_CVPR,
    author    = {Assefa, Maregu and Naseer, Muzammal and Ganapathi, Iyyakutti Iyappan and Ali, Syed Sadaf and Seghier, Mohamed L and Werghi, Naoufel},
    title     = {DyCON: Dynamic Uncertainty-aware Consistency and Contrastive Learning for Semi-supervised Medical Image Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30850-30860}
}

raw_affiliation:
[{"affiliation": "C2PS - Khalifa University of Science and Technology", "city": "Abu Dhabi", "country": "UAE"}]


authors: Siyi Du, Xinzhe Luo, Declan P. O'Regan, Chen Qin
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15549-15559
cities: ["London", "London"]
countries: ["UK", "UK"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Du_STiL_Semi-supervised_Tabular-Image_Learning_for_Comprehensive_Task-Relevant_Information_Exploration_in_CVPR_2025_paper.html

title:
STiL: Semi-supervised Tabular-Image Learning for Comprehensive Task-Relevant Information Exploration in Multimodal Classification

abstract:
Multimodal image-tabular learning is gaining attention, yet it faces challenges due to limited labeled data. While earlier work has applied self-supervised learning (SSL) to unlabeled data, its task-agnostic nature often results in learning suboptimal features for downstream tasks. Semi-supervised learning (SemiSL), which combines labeled and unlabeled data, offers a promising solution. However, existing multimodal SemiSL methods typically focus on unimodal or modality-shared features, ignoring valuable task-relevant modality-specific information, leading to a Modality Information Gap. In this paper, we propose STiL, a novel SemiSL tabular-image framework that addresses this gap by comprehensively exploring task-relevant information. STiL features a new disentangled contrastive consistency module to learn cross-modal invariant representations of shared information while retaining modality-specific information via disentanglement. We also propose a novel consensus-guided pseudo-labeling strategy to generate reliable pseudo-labels based on classifier consensus, along with a new prototype-guided label smoothing technique to refine pseudo-label quality with prototype embeddings, thereby enhancing task-relevant information learning in unlabeled data. Experiments on natural and medical image datasets show that STiL outperforms the state-of-the-art supervised/SSL/SemiSL image/multimodal approaches. Our code is available at https://github.com/siyi-wind/STiL.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Du_STiL_Semi-supervised_Tabular-Image_Learning_for_Comprehensive_Task-Relevant_Information_Exploration_in_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Du_STiL_Semi-supervised_Tabular-Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Du_2025_CVPR,
    author    = {Du, Siyi and Luo, Xinzhe and O'Regan, Declan P. and Qin, Chen},
    title     = {STiL: Semi-supervised Tabular-Image Learning for Comprehensive Task-Relevant Information Exploration in Multimodal Classification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15549-15559}
}

raw_affiliation:
[{"affiliation": "Department of Electrical and Electronic Engineering & I-X, Imperial College London", "city": "London", "country": "UK"}, {"affiliation": "MRC Laboratory of Medical Science, Imperial College London", "city": "London", "country": "UK"}]

affiliations:
["Department of Electrical and Electronic Engineering & I-X, Imperial College London", "MRC Laboratory of Medical Science, Imperial College London"]


title: DUNE: Distilling a Universal Encoder from Heterogeneous 2D and 3D Teachers
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30084-30094
raw_affiliation: [{"affiliation": "NA VER LABS Europe", "city": "Unknown", "country": "Europe"}]
affiliations: ["NA VER LABS Europe"]
cities: ["Unknown"]
countries: ["Europe"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sariyildiz_DUNE_Distilling_a_Universal_Encoder_from_Heterogeneous_2D_and_3D_CVPR_2025_paper.html

authors:
Mert Bülent Sarıyıldız, Philippe Weinzaepfel, Thomas Lucas, Pau de Jorge, Diane Larlus, Yannis Kalantidis

abstract:
Recent multi-teacher distillation methods have unified the encoders of multiple foundation models into a single encoder, achieving competitive performance on core vision tasks like classification, segmentation, and depth estimation. This led us to ask: Could similar success be achieved when the pool of teachers also includes vision models specialized in diverse tasks across both 2D and 3D perception? In this paper, we define and investigate the problem of heterogeneous teacher distillation, or co-distillation, a challenging multi-teacher distillation scenario where teacher models vary significantly in both (a) their design objectives and (b) the data they were trained on. We explore data-sharing strategies and teacher-specific encoding, and introduce DUNE, a single encoder excelling in 2D vision, 3D understanding, and 3D human perception. Our model achieves performance comparable to that of its larger teachers, sometimes even outperforming them, on their respective tasks. Notably, DUNE surpasses MASt3R in Map-free Visual Relocalization with a much smaller encoder.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sariyildiz_DUNE_Distilling_a_Universal_Encoder_from_Heterogeneous_2D_and_3D_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sariyildiz_DUNE_Distilling_a_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sariyildiz_2025_CVPR,
    author    = {Sar{\i}y{\i}ld{\i}z, Mert B\"ulent and Weinzaepfel, Philippe and Lucas, Thomas and de Jorge, Pau and Larlus, Diane and Kalantidis, Yannis},
    title     = {DUNE: Distilling a Universal Encoder from Heterogeneous 2D and 3D Teachers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30084-30094}
}


title: Black Hole-Driven Identity Absorbing in Diffusion Models
authors: Muhammad Shaheryar, Jong Taek Lee, Soon Ki Jung
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 28544-28554
raw_affiliation: [{"affiliation": "Kyungpook National University", "city": "Daegu", "country": "Republic of Korea"}]
affiliations: ["Kyungpook National University"]
cities: ["Daegu"]
countries: ["Republic of Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shaheryar_Black_Hole-Driven_Identity_Absorbing_in_Diffusion_Models_CVPR_2025_paper.html

abstract:
Recent advances in diffusion models have positioned them as powerful generative frameworks for high-resolution image synthesis across diverse domains. The emerging h-space within these models, defined by bottleneck activations in the denoiser, offers promising pathways for semantic image editing similar to GAN latent spaces. However, as demand grows for content erasure and concept removal, privacy concerns highlight the need for identity disentanglement in the latent space of diffusion models. The high dimensional latent space poses challenges for identity removal, as traversing with random or orthogonal directions often leads to semantically unvalidated regions, resulting in unrealistic outputs.To address these issues, we propose Black Hole Driven Identity Absorption (BIA), a novel approach for identity erasure within the latent space of diffusion models. BIA uses a black hole metaphor, where the latent region representing a specified identity acts as an attractor, drawing in nearby latent points of surrounding identities to wrap the black hole. Instead of relying on random traversals for optimization, BIA employs an identity absorption mechanism by attracting and wrapping nearby validated latent points associated with other identities to achieve a vanishing effect for specified identity. Our method effectively prevents the generation of a specified identity while preserving other attributes, as validated by improved scores on identity similarity SID, FID metrics, qualitative evaluations, and user studies as compared to SOTA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shaheryar_Black_Hole-Driven_Identity_Absorbing_in_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shaheryar_Black_Hole-Driven_Identity_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shaheryar_2025_CVPR,
    author    = {Shaheryar, Muhammad and Lee, Jong Taek and Jung, Soon Ki},
    title     = {Black Hole-Driven Identity Absorbing in Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {28544-28554}
}


title: HiRes-LLaVA: Restoring Fragmentation Input in High-Resolution Large Vision-Language Models
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29814-29824
cities: ["Shenzhen", "Unknown", "Hong Kong", "Hong Kong"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_HiRes-LLaVA_Restoring_Fragmentation_Input_in_High-Resolution_Large_Vision-Language_Models_CVPR_2025_paper.html

authors:
Runhui Huang, Xinpeng Ding, Chunwei Wang, Jianhua Han, Yulong Liu, Hengshuang Zhao, Hang Xu, Lu Hou, Wei Zhang, Xiaodan Liang

abstract:
High-resolution image inputs allow Large Vision-Language Models (LVLMs) to capture finer visual details, improving comprehension. However, the increased training and computational costs associated with such inputs pose significant challenges. A common approach to mitigate these costs involves slicing the input into uniform patches using sliding windows, each aligned with the vision encoder's input size. While efficient, this method fragments the input, disrupting the continuity of context, which negatively impacts cross-patch perception tasks. To address these limitations, we propose HiRes-LLaVA, a novel framework designed to efficiently process high-resolution inputs of any size without altering the original contextual and geometric information. HiRes-LLaVA introduces two key components: (i) a SliceRestore Adapter (SRA) that reconstructs sliced patches into their original form, enabling efficient extraction of both global and local features through down-up-sampling and convolutional layers, and (ii) a Self-Mining Sampler (SMS) that compresses visual tokens based on internal relationships, preserving original context and positional information while reducing training overhead. To assess the ability of handling context fragmentation, we construct a new benchmark, EntityGrid-QA, consisting of edge-related tasks. Extensive experiments demonstrate the superiority of HiRes-LLaVA on both existing public benchmarks and EntityGrid-QA. For example, with SRA, our method achieves a performance improvement of  12% over state-of-the-art LVLMs in addressing fragmentation issues. Additionally, our SMS outperforms other visual token downsamplers, while offering high data efficiency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_HiRes-LLaVA_Restoring_Fragmentation_Input_in_High-Resolution_Large_Vision-Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_HiRes-LLaVA_Restoring_Fragmentation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Runhui and Ding, Xinpeng and Wang, Chunwei and Han, Jianhua and Liu, Yulong and Zhao, Hengshuang and Xu, Hang and Hou, Lu and Zhang, Wei and Liang, Xiaodan},
    title     = {HiRes-LLaVA: Restoring Fragmentation Input in High-Resolution Large Vision-Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29814-29824}
}

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

affiliations:
["Shenzhen campus of Sun Yat-sen University", "Huawei Noah\u2019s Ark Lab", "The Hong Kong University of Science and Technology", "The University of Hong Kong"]


title: Hallo3: Highly Dynamic and Realistic Portrait Image Animation with Video Diffusion Transformer
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21086-21095
affiliations: ["Fudan University", "Baidu Inc", "Shanghai Academy of AI for Science"]
cities: ["Shanghai", "Beijing", "Shanghai"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Cui_Hallo3_Highly_Dynamic_and_Realistic_Portrait_Image_Animation_with_Video_CVPR_2025_paper.html

authors:
Jiahao Cui, Hui Li, Yun Zhan, Hanlin Shang, Kaihui Cheng, Yuqi Ma, Shan Mu, Hang Zhou, Jingdong Wang, Siyu Zhu

abstract:
Existing methodologies for animating portrait images face significant challenges, particularly in handling non-frontal perspectives, rendering dynamic objects around the portrait, and generating immersive, realistic backgrounds. In this paper, we introduce the first application of a pretrained transformer-based video generative model that demonstrates strong generalization capabilities and generates highly dynamic, realistic videos for portrait animation, effectively addressing these challenges. The adoption of a new video backbone model makes previous U-Net-based methods for identity maintenance, audio conditioning, and video extrapolation inapplicable. To address this limitation, we design an identity reference network consisting of a causal 3D VAE combined with a stacked series of transformer layers, ensuring consistent facial identity across video sequences. Additionally, we investigate various speech audio conditioning and motion frame mechanisms to enable the generation of continuous video driven by speech audio. Our method is validated through experiments on benchmark and newly proposed wild datasets, demonstrating substantial improvements over prior methods in generating realistic portraits characterized by diverse orientations within dynamic and immersive scenes.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Cui_Hallo3_Highly_Dynamic_and_Realistic_Portrait_Image_Animation_with_Video_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Cui_Hallo3_Highly_Dynamic_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Cui_2025_CVPR,
    author    = {Cui, Jiahao and Li, Hui and Zhan, Yun and Shang, Hanlin and Cheng, Kaihui and Ma, Yuqi and Mu, Shan and Zhou, Hang and Wang, Jingdong and Zhu, Siyu},
    title     = {Hallo3: Highly Dynamic and Realistic Portrait Image Animation with Video Diffusion Transformer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21086-21095}
}

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


title: Generative Photography: Scene-Consistent Camera Control for Realistic Text-to-Image Synthesis
authors: Yu Yuan, Xijun Wang, Yichen Sheng, Prateek Chennuri, Xingguang Zhang, Stanley Chan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7920-7930
affiliations: ["School of ECE, Purdue University", "NVIDIA"]
cities: ["West Lafayette", "Santa Clara"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yuan_Generative_Photography_Scene-Consistent_Camera_Control_for_Realistic_Text-to-Image_Synthesis_CVPR_2025_paper.html

abstract:
Image generation today can produce somewhat realistic images from text prompts. However, if one asks the generator to synthesize a specific camera setting such as creating different fields of view using a 24mm lens versus a 70mm lens, the generator will not be able to interpret and generate scene-consistent images. This limitation not only hinders the adoption of generative tools in professional photography but also highlights the broader challenge of aligning data-driven models with real-world physical settings. In this paper, we introduce Generative Photography, a framework that allows controlling camera intrinsic settings during content generation. The core innovation of this work are the concepts of Dimensionality Lifting and Differential Camera Intrinsics Learning, enabling smooth and consistent transitions across different camera settings. Experimental results show that our method produces significantly more scene-consistent photorealistic images than state-of-the-art models such as Stable Diffusion 3 and FLUX. Our code and additional results are available at https://generative-photography.github.io/project.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yuan_Generative_Photography_Scene-Consistent_Camera_Control_for_Realistic_Text-to-Image_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yuan_Generative_Photography_Scene-Consistent_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yuan_2025_CVPR,
    author    = {Yuan, Yu and Wang, Xijun and Sheng, Yichen and Chennuri, Prateek and Zhang, Xingguang and Chan, Stanley},
    title     = {Generative Photography: Scene-Consistent Camera Control for Realistic Text-to-Image Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7920-7930}
}

raw_affiliation:
[{"affiliation": "School of ECE, Purdue University", "city": "West Lafayette", "country": "USA"}, {"affiliation": "NVIDIA", "city": "Santa Clara", "country": "USA"}]


title: Advancing Manga Analysis: Comprehensive Segmentation Annotations for the Manga109 Dataset
authors: Minshan Xie, Jian Lin, Hanyuan Liu, Chengze Li, Tien-Tsin Wong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8869-8878
cities: ["Hong Kong", "Hong Kong", "Melbourne"]
countries: ["China", "China", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_Advancing_Manga_Analysis_Comprehensive_Segmentation_Annotations_for_the_Manga109_Dataset_CVPR_2025_paper.html

abstract:
Manga, a popular form of multimodal artwork, has traditionally been overlooked in deep learning advancements due to the absence of a robust dataset and comprehensive annotation. Manga segmentation is the key to the digital migration of manga. There exists a significant domain gap between the manga and the natural images, that fails most existing learning-based methods. To address this gap, we introduce an augmented segmentation annotation for the Manga109 dataset, a collection of 109 manga volumes, that offers intricate artworks in a rich variety of styles. We introduce a detailed annotation that extends beyond the original simple bounding boxes to the segmentation masks with pixel-level precision. It provides object category, location, and instance information that can be used for semantic segmentation and instance segmentation. We also provide a comprehensive analysis of our annotation dataset from various aspects. We further measure the improvement of the state-of-the-art segmentation model after training it with our augmented dataset. The benefits of this augmented dataset are profound, with the potential to significantly enhance manga analysis algorithms and catalyze the novel development in digital art processing and cultural analytics. This annotation, named MangaSeg, is publicly available at https://huggingface.co/datasets/MS92/MangaSegmentation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_Advancing_Manga_Analysis_Comprehensive_Segmentation_Annotations_for_the_Manga109_Dataset_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_Advancing_Manga_Analysis_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Minshan and Lin, Jian and Liu, Hanyuan and Li, Chengze and Wong, Tien-Tsin},
    title     = {Advancing Manga Analysis: Comprehensive Segmentation Annotations for the Manga109 Dataset},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8869-8878}
}

raw_affiliation:
[{"affiliation": "Centre for Perceptual and Interactive Intelligence (CPII), Hong Kong SAR", "city": "Hong Kong", "country": "China"}, {"affiliation": "Saint Francis University, Hong Kong SAR", "city": "Hong Kong", "country": "China"}, {"affiliation": "Monash University, Australia", "city": "Melbourne", "country": "Australia"}]

affiliations:
["Centre for Perceptual and Interactive Intelligence (CPII), Hong Kong SAR", "Saint Francis University, Hong Kong SAR", "Monash University, Australia"]


title: SeqMvRL: A Sequential Fusion Framework for Multi-view Representation Learning
authors: Ren Wang, Haoliang Sun, Yuxiu Lin, Chuanhui Zuo, Yongshun Gong, Yilong Yin, Wenjia Meng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 25822-25831
cities: ["Jinan", "Beijing", "Jinan"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_SeqMvRL_A_Sequential_Fusion_Framework_for_Multi-view_Representation_Learning_CVPR_2025_paper.html

abstract:
Multi-view representation learning integrates multiple observable views of an entity into a unified representation to facilitate downstream tasks. Current methods predominantly focus on distinguishing compatible components across views, followed by a single-step parallel fusion process. However, this parallel fusion is static in essence, overlooking potential conflicts among views and compromising representation ability. To address this issue, this paper proposes a novel Sequential fusion framework for Multi-view Representation Learning, termed SeqMvRL. Specifically, we model multi-view fusion as a sequential decision-making problem and construct a pairwise integrator (PI) and a next-view selector (NVS), which represent the environment and agent in reinforcement learning, respectively. PI merges the current fused feature with the selected view, while NVS is introduced to determine which view to fuse subsequently. By adaptively selecting the next optimal view for fusion based on the current fusion state, SeqMvRL thereby effectively reduces conflicts and enhances unified representation quality. Additionally, an elaborate novel reward function encourages the model to prioritize views that enhance the discriminability of the fused features. Experimental results demonstrate that SeqMvRL outperforms parallel fusion approaches in classification and clustering tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_SeqMvRL_A_Sequential_Fusion_Framework_for_Multi-view_Representation_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_SeqMvRL_A_Sequential_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Ren and Sun, Haoliang and Lin, Yuxiu and Zuo, Chuanhui and Gong, Yongshun and Yin, Yilong and Meng, Wenjia},
    title     = {SeqMvRL: A Sequential Fusion Framework for Multi-view Representation Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {25822-25831}
}

raw_affiliation:
[{"affiliation": "School of Software, Shandong University", "city": "Jinan", "country": "China"}, {"affiliation": "Department of Computer Science and Technology, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computing and Artificial Intelligence, Shandong University of Finance and Economics", "city": "Jinan", "country": "China"}]

affiliations:
["School of Software, Shandong University", "Department of Computer Science and Technology, Tsinghua University", "School of Computing and Artificial Intelligence, Shandong University of Finance and Economics"]


title: Auto Cherry-Picker: Learning from High-quality Generative Data Driven by Language
authors: Yicheng Chen, Xiangtai Li, Yining Li, Yanhong Zeng, Jianzong Wu, Xiangyu Zhao, Kai Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19952-19962
cities: ["Shanghai", "Shanghai", "Singapore", "Beijing", "Shanghai"]
countries: ["China", "China", "Singapore", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Auto_Cherry-Picker_Learning_from_High-quality_Generative_Data_Driven_by_Language_CVPR_2025_paper.html

abstract:
Diffusion models can generate realistic and diverse images, potentially facilitating data availability for data-intensive perception tasks. However, leveraging these models to boost performance on downstream tasks with synthetic data poses several challenges, including aligning with real data distribution, scaling synthetic sample volumes, and ensuring their quality. To bridge these gaps, we present Auto Cherry-Picker (ACP), a novel framework that generates high-quality cross-modality training samples at scale to augment perception and multi-modal training. ACP first uses LLMs to sample descriptions and layouts based on object combinations from real data priors, eliminating the need for ground truth image captions or annotations. Next, we use an off-the-shelf controllable diffusion model to generate multiple images. Then, the generated data are refined using a comprehensively designed metric, Composite Layout and Image Score (CLIS), to ensure quality. Our customized synthetic high-quality samples boost performance in various scenarios, especially in addressing challenges associated with long-tailed distribution and imbalanced datasets. Experiment results on downstream tasks demonstrate that ACP can significantly improve the performance of existing models. In addition, we find a positive correlation between CLIS and performance gains in downstream tasks. This finding shows the potential for evaluation metrics as the role for various visual perception and MLLM tasks.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Auto_Cherry-Picker_Learning_from_High-quality_Generative_Data_Driven_by_Language_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Auto_Cherry-Picker_Learning_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Yicheng and Li, Xiangtai and Li, Yining and Zeng, Yanhong and Wu, Jianzong and Zhao, Xiangyu and Chen, Kai},
    title     = {Auto Cherry-Picker: Learning from High-quality Generative Data Driven by Language},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19952-19962}
}

raw_affiliation:
[{"affiliation": "Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "Fudan University", "city": "Shanghai", "country": "China"}, {"affiliation": "S-Lab, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}]

affiliations:
["Shanghai AI Laboratory", "Fudan University", "S-Lab, Nanyang Technological University", "Peking University", "Shanghai Jiao Tong University"]


title: EnvGS: Modeling View-Dependent Appearance with Environment Gaussian
authors: Tao Xie, Xi Chen, Zhen Xu, Yiman Xie, Yudong Jin, Yujun Shen, Sida Peng, Hujun Bao, Xiaowei Zhou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5742-5751
affiliations: ["Zhejiang University", "Ant Group"]
cities: ["Hangzhou", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_EnvGS_Modeling_View-Dependent_Appearance_with_Environment_Gaussian_CVPR_2025_paper.html

abstract:
Reconstructing complex reflections in real-world scenes from 2D images is essential for achieving photorealistic novel view synthesis. Existing methods that utilize environment maps to model reflections from distant lighting often struggle with high-frequency reflection details and fail to account for near-field reflections. In this work, we introduce EnvGS, a novel approach that employs a set of Gaussian primitives as an explicit 3D representation for capturing reflections of environments. These environment Gaussian primitives are incorporated with base Gaussian primitives to model the appearance of the whole scene. To efficiently render these environment Gaussian primitives, we developed a ray-tracing-based renderer that leverages the GPU's RT core for fast rendering. This allows us to jointly optimize our model for high-quality reconstruction while maintaining real-time rendering speeds. Results from multiple real-world and synthetic datasets demonstrate that our method produces significantly more detailed reflections, achieving the best rendering quality in real-time novel view synthesis. The code is available at https://zju3dv.github.io/envgs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_EnvGS_Modeling_View-Dependent_Appearance_with_Environment_Gaussian_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_EnvGS_Modeling_View-Dependent_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Tao and Chen, Xi and Xu, Zhen and Xie, Yiman and Jin, Yudong and Shen, Yujun and Peng, Sida and Bao, Hujun and Zhou, Xiaowei},
    title     = {EnvGS: Modeling View-Dependent Appearance with Environment Gaussian},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5742-5751}
}

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


title: Provoking Multi-modal Few-Shot LVLM via Exploration-Exploitation In-Context Learning
authors: Cheng Chen, Yunpeng Zhai, Yifan Zhao, Jinyang Gao, Bolin Ding, Jia Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3826-3835
affiliations: ["State Key Laboratory of Virtual Reality Technology and Systems", "Tongyi Lab, Alibaba Group"]
cities: ["Beijing", "Hangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Provoking_Multi-modal_Few-Shot_LVLM_via_Exploration-Exploitation_In-Context_Learning_CVPR_2025_paper.html

abstract:
In-context learning (ICL), a predominant trend in instruction learning, aims at enhancing the performance of large language models by providing clear task guidance and examples, improving their capability in task understanding and execution. This paper investigates ICL on Large Vision-Language Models (LVLMs) and explores the policies of multi-modal demonstration selection. Existing research efforts in ICL face significant challenges: First, they rely on pre-defined demonstrations or heuristic selecting strategies based on human intuition, which are usually inadequate for covering diverse task requirements, leading to sub-optimal solutions; Second, individually selecting each demonstration fails in modeling the interactions between them, resulting in information redundancy. Unlike these prevailing efforts, we propose a new exploration-exploitation reinforcement learning framework, which explores policies to fuse multi-modal information and adaptively select adequate demonstrations as an integrated whole. The framework allows LVLMs to optimize themselves by continually refining their demonstrations through self-exploration, enabling the ability to autonomously identify and generate the most effective selection policies for in-context learning. Experimental results verify that our approach achieves significant performance improvements on four Visual Question-Answering (VQA) datasets, demonstrating its effectiveness in enhancing the generalization capability of few-shot LVLMs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Provoking_Multi-modal_Few-Shot_LVLM_via_Exploration-Exploitation_In-Context_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Provoking_Multi-modal_Few-Shot_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Cheng and Zhai, Yunpeng and Zhao, Yifan and Gao, Jinyang and Ding, Bolin and Li, Jia},
    title     = {Provoking Multi-modal Few-Shot LVLM via Exploration-Exploitation In-Context Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3826-3835}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory of Virtual Reality Technology and Systems", "city": "Beijing", "country": "China"}, {"affiliation": "Tongyi Lab, Alibaba Group", "city": "Hangzhou", "country": "China"}]


title: BadToken: Token-level Backdoor Attacks to Multi-modal Large Language Models
authors: Zenghui Yuan, Jiawen Shi, Pan Zhou, Neil Zhenqiang Gong, Lichao Sun
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29927-29936
cities: ["Wuhan", "Durham", "Bethlehem"]
countries: ["China", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yuan_BadToken_Token-level_Backdoor_Attacks_to_Multi-modal_Large_Language_Models_CVPR_2025_paper.html

abstract:
Multi-modal large language models (MLLMs) extend large language models (LLMs) to process multi-modal information, enabling them to generate responses to image-text inputs. MLLMs have been incorporated into diverse multi-modal applications, such as autonomous driving and medical diagnosis, via plug-and-play without fine-tuning. This deployment paradigm increases the vulnerability of MLLMs to backdoor attacks. However, existing backdoor attacks against MLLMs achieve limited effectiveness and stealthiness. In this work, we propose BadToken, the first token-level backdoor attack to MLLMs. BadToken introduces two novel backdoor behaviors: Token-substitution and Token-addition, which enable flexible and stealthy attacks by making token-level modifications to the original output for backdoored inputs. We formulate a general optimization problem that considers the two backdoor behaviors to maximize the attack effectiveness. We evaluate BadToken on two open-source MLLMs and various tasks. Our results show that our attack maintains the model's utility while achieving high attack success rates and stealthiness. We also show the real-world threats of BadToken in two scenarios, i.e., autonomous driving and medical diagnosis. Furthermore, we consider defenses including fine-tuning and input purification. Our results highlight the threat of our attack.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yuan_BadToken_Token-level_Backdoor_Attacks_to_Multi-modal_Large_Language_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yuan_BadToken_Token-level_Backdoor_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yuan_2025_CVPR,
    author    = {Yuan, Zenghui and Shi, Jiawen and Zhou, Pan and Gong, Neil Zhenqiang and Sun, Lichao},
    title     = {BadToken: Token-level Backdoor Attacks to Multi-modal Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29927-29936}
}

raw_affiliation:
[{"affiliation": "Hubei Key Laboratory of Distributed System Security, Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology", "city": "Wuhan", "country": "China"}, {"affiliation": "Duke University", "city": "Durham", "country": "USA"}, {"affiliation": "Lehigh University", "city": "Bethlehem", "country": "USA"}]

affiliations:
["Hubei Key Laboratory of Distributed System Security, Hubei Engineering Research Center on Big Data Security, School of Cyber Science and Engineering, Huazhong University of Science and Technology", "Duke University", "Lehigh University"]


authors: Radu Berdan, Beril Besbinar, Christoph Reinders, Junji Otsuka, Daisuke Iso
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11833-11843
affiliations: ["Sony AI", "Leibniz University Hannover", "Sony Group Corporation"]
cities: ["Unknown", "Hannover", "Unknown"]
countries: ["Unknown", "Germany", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Berdan_ReRAW_RGB-to-RAW_Image_Reconstruction_via_Stratified_Sampling_for_Efficient_Object_CVPR_2025_paper.html

title:
ReRAW: RGB-to-RAW Image Reconstruction via Stratified Sampling for Efficient Object Detection on the Edge

abstract:
Edge-based computer vision models running on compact, resource-limited devices benefit greatly from using unprocessed, detail-rich RAW sensor data instead of processed RGB images. Training these models, however, necessitates large labeled RAW datasets, which are costly and often impractical to obtain. Thus, converting existing labeled RGB datasets into sensor-specific RAW images becomes crucial for effective model training. In this paper, we introduce ReRAW, an RGB-to-RAW conversion model that achieves state-of-the-art reconstruction performance across five diverse RAW datasets. This is accomplished through ReRAW's novel multi-head architecture predicting RAW image candidates in gamma space. The performance is further boosted by a stratified sampling-based training data selection heuristic, which helps the model better reconstruct brighter RAW pixels. We finally demonstrate that pretraining compact models on a combination of high-quality synthetic RAW datasets (such as generated by ReRAW) and ground-truth RAW images for downstream tasks like object detection, outperforms both standard RGB pipelines, and RAW fine-tuning of RGB-pretrained models for the same task.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Berdan_ReRAW_RGB-to-RAW_Image_Reconstruction_via_Stratified_Sampling_for_Efficient_Object_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Berdan_ReRAW_RGB-to-RAW_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Berdan_2025_CVPR,
    author    = {Berdan, Radu and Besbinar, Beril and Reinders, Christoph and Otsuka, Junji and Iso, Daisuke},
    title     = {ReRAW: RGB-to-RAW Image Reconstruction via Stratified Sampling for Efficient Object Detection on the Edge},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11833-11843}
}

raw_affiliation:
[{"affiliation": "Sony AI", "city": "Unknown", "country": "Unknown"}, {"affiliation": "Leibniz University Hannover", "city": "Hannover", "country": "Germany"}, {"affiliation": "Sony Group Corporation", "city": "Unknown", "country": "Unknown"}]


title: VLMs-Guided Representation Distillation for Efficient Vision-Based Reinforcement Learning
authors: Haoran Xu, Peixi Peng, Guang Tan, Yiqian Chang, Luntong Li, Yonghong Tian
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29534-29544
cities: ["Shenzhen", "Shenzhen", "Shenzhen", "Shenzhen", "Beijing"]
countries: ["China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_VLMs-Guided_Representation_Distillation_for_Efficient_Vision-Based_Reinforcement_Learning_CVPR_2025_paper.html

abstract:
Vision-based Reinforcement Learning (VRL) attempts to establish associations between visual inputs and optimal actions through interactions with the environment. Given the high-dimensional and complex nature of visual data, it becomes essential to learn policy upon high-quality state representation. To this end, existing VRL methods primarily rely on interaction-collected data, combined with self-supervised auxiliary tasks. However, two key challenges remain: limited data samples and a lack of task-relevant semantic constraints. To tackle this, we propose DGC, a method that distills guidance from Visual Language Models (VLMs) alongside self-supervised learning into a compact VRL agent. Notably, we leverage the state representation capabilities of VLMs, rather than their decision-making abilities. Within DGC, a novel prompting-reasoning pipeline is designed to convert historical observations and actions into usable supervision signals, enabling semantic understanding within the compact visual encoder. By leveraging these distilled semantic representations, the VRL agent achieves significant improvements in the sample efficiency. Extensive experiments on the Carla benchmark demonstrate our state-of-the-art performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_VLMs-Guided_Representation_Distillation_for_Efficient_Vision-Based_Reinforcement_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_VLMs-Guided_Representation_Distillation_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Haoran and Peng, Peixi and Tan, Guang and Chang, Yiqian and Li, Luntong and Tian, Yonghong},
    title     = {VLMs-Guided Representation Distillation for Efficient Vision-Based Reinforcement Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29534-29544}
}

raw_affiliation:
[{"affiliation": "School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University", "city": "Shenzhen", "country": "China"}, {"affiliation": "Peng Cheng Laboratory", "city": "Shenzhen", "country": "China"}, {"affiliation": "School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University", "city": "Shenzhen", "country": "China"}, {"affiliation": "School of Computer Science and Technology, Shenzhen Campus of Harbin Institute of Technology", "city": "Shenzhen", "country": "China"}, {"affiliation": "School of Computer Science, Peking University", "city": "Beijing", "country": "China"}]

affiliations:
["School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University", "Peng Cheng Laboratory", "School of Electronic and Computer Engineering, Shenzhen Graduate School, Peking University", "School of Computer Science and Technology, Shenzhen Campus of Harbin Institute of Technology", "School of Computer Science, Peking University"]


title: MonoDGP: Monocular 3D Object Detection with Decoupled-Query and Geometry-Error Priors
authors: Fanqi Pu, Yifan Wang, Jiru Deng, Wenming Yang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6520-6530
affiliations: ["Shenzhen International Graduate School, Tsinghua University"]
cities: ["Shenzhen"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pu_MonoDGP_Monocular_3D_Object_Detection_with_Decoupled-Query_and_Geometry-Error_Priors_CVPR_2025_paper.html

abstract:
Perspective projection has been extensively utilized in monocular 3D object detection methods. It introduces geometric priors from 2D bounding boxes and 3D object dimensions to reduce the uncertainty of depth estimation. However, due to errors originating from the object's visual surface, the bounding box height often fails to represent the actual central height, which undermines the effectiveness of geometric depth. Direct prediction for the projected height unavoidably results in a loss of 2D priors, while multi-depth prediction with complex branches does not fully leverage geometric depth. This paper presents a Transformer-based monocular 3D object detection method called MonoDGP, which adopts perspective-invariant geometry errors to modify the projection formula. We also try to systematically discuss and explain the mechanisms and efficacy behind geometry errors, which serve as a simple but effective alternative to multi-depth prediction. Additionally, MonoDGP decouples the depth-guided decoder and constructs a 2D decoder only dependent on visual features, providing 2D priors and initializing object queries without the disturbance of 3D detection. To further optimize and fine-tune input tokens of the transformer decoder, we also introduce a Region Segmentation Head (RSH) that generates enhanced features and segment embeddings. Our monocular method demonstrates state-of-the-art performance on the KITTI benchmark without extra data. Code is available at https://github.com/PuFanqi23/MonoDGP.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pu_MonoDGP_Monocular_3D_Object_Detection_with_Decoupled-Query_and_Geometry-Error_Priors_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pu_MonoDGP_Monocular_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Pu_2025_CVPR,
    author    = {Pu, Fanqi and Wang, Yifan and Deng, Jiru and Yang, Wenming},
    title     = {MonoDGP: Monocular 3D Object Detection with Decoupled-Query and Geometry-Error Priors},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6520-6530}
}

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


title: NeISF++: Neural Incident Stokes Field for Polarized Inverse Rendering of Conductors and Dielectrics
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26493-26503
affiliations: ["Sony Semiconductor Solutions Corporation", "Sony Europe B.V .", "Osaka University"]
cities: ["Tokyo", "Berlin", "Osaka"]
countries: ["Japan", "Germany", "Japan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_NeISF_Neural_Incident_Stokes_Field_for_Polarized_Inverse_Rendering_of_CVPR_2025_paper.html

authors:
Chenhao Li, Taishi Ono, Takeshi Uemori, Sho Nitta, Hajime Mihara, Alexander Gatto, Hajime Nagahara, Yusuke Moriuchi

abstract:
Recent inverse rendering methods have improved shape, material, and illumination reconstruction using polarization cues. However, they only support dielectrics, ignoring conductors, which are common in everyday life. Since conductors and dielectrics have different reflection properties, using previous dielectrics-based methods will lead to obvious errors. In addition, conductors are glossy, which may cause strong specular reflection and is hard to reconstruct. To solve the above issues, we propose NeISF++, an inverse rendering pipeline that supports conductors and dielectrics. The key ingredient for our proposal is a general pBRDF that describes both conductors and dielectrics. As for the strong specular reflection problem, we propose a novel geometry initialization method using DoLP images. This physical cue is invariant to intensities and thus robust to strong specular reflections. Experimental results on our synthetic and real datasets show that our method surpasses the existing polarized inverse rendering methods for geometry and material decomposition as well as downstream tasks like relighting.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_NeISF_Neural_Incident_Stokes_Field_for_Polarized_Inverse_Rendering_of_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_NeISF_Neural_Incident_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Chenhao and Ono, Taishi and Uemori, Takeshi and Nitta, Sho and Mihara, Hajime and Gatto, Alexander and Nagahara, Hajime and Moriuchi, Yusuke},
    title     = {NeISF++: Neural Incident Stokes Field for Polarized Inverse Rendering of Conductors and Dielectrics},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26493-26503}
}

raw_affiliation:
[{"affiliation": "Sony Semiconductor Solutions Corporation", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Sony Europe B.V .", "city": "Berlin", "country": "Germany"}, {"affiliation": "Osaka University", "city": "Osaka", "country": "Japan"}]


title: HunyuanPortrait: Implicit Condition Control for Enhanced Portrait Animation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15909-15919
affiliations: ["Tsinghua University", "Hunyuan, Tencent", "Sun Yat-Sen Univeristy", "HKUST"]
cities: ["Beijing", "Shenzhen", "Guangzhou", "Hong Kong"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xu_HunyuanPortrait_Implicit_Condition_Control_for_Enhanced_Portrait_Animation_CVPR_2025_paper.html

authors:
Zunnan Xu, Zhentao Yu, Zixiang Zhou, Jun Zhou, Xiaoyu Jin, Fa-ting Hong, Xiaozhong Ji, Junwei Zhu, Chengfei Cai, Shiyu Tang, Qin Lin, Xiu Li, Qinglin Lu

abstract:
We introduce HunyuanPortrait, a diffusion-based condition control method that employs implicit representations for highly controllable and lifelike portrait animation. Given a single portrait image as an appearance reference and video clips as driving templates, HunyuanPortrait can animate the character in the reference image by the facial expression and head pose of the driving videos. In our framework, we utilize pre-trained encoders to achieve the decoupling of portrait motion information and identity in videos. To do so, implicit representation is adopted to encode motion information and is employed as control signals in the animation phase. By leveraging the power of stable video diffusion as the main building block, we carefully design adapter layers to inject control signals into the denoising unet through attention mechanisms. These bring spatial richness of details and temporal consistency. HunyuanPortrait also exhibits strong generalization performance, which can effectively disentangle appearance and motion under different image styles. Our framework outperforms existing methods, demonstrating superior temporal consistency and controllability. Our project is available at https://kkakkkka.github.io/HunyuanPortrait.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xu_HunyuanPortrait_Implicit_Condition_Control_for_Enhanced_Portrait_Animation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xu_HunyuanPortrait_Implicit_Condition_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xu_2025_CVPR,
    author    = {Xu, Zunnan and Yu, Zhentao and Zhou, Zixiang and Zhou, Jun and Jin, Xiaoyu and Hong, Fa-ting and Ji, Xiaozhong and Zhu, Junwei and Cai, Chengfei and Tang, Shiyu and Lin, Qin and Li, Xiu and Lu, Qinglin},
    title     = {HunyuanPortrait: Implicit Condition Control for Enhanced Portrait Animation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15909-15919}
}

raw_affiliation:
[{"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Hunyuan, Tencent", "city": "Shenzhen", "country": "China"}, {"affiliation": "Sun Yat-Sen Univeristy", "city": "Guangzhou", "country": "China"}, {"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}]


title: Flexible Group Count Enables Hassle-Free Structured Pruning
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4807-4818
cities: ["Houston", "Cleveland", "San Jose", "Stanford", "Minneapolis", "Berkeley", "Raleigh"]
countries: ["USA", "USA", "USA", "USA", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_Flexible_Group_Count_Enables_Hassle-Free_Structured_Pruning_CVPR_2025_paper.html

authors:
Jiamu Zhang, Shaochen Zhong, Andrew Ye, Zirui Liu, Sebastian Zhao, Kaixiong Zhou, Li Li, Soo-Hyun Choi, Rui Chen, Xia Hu, Shuai Xu, Vipin Chaudhary

abstract:
Densely structured pruning methods -- which generate pruned models in a fully dense format, allowing immediate compression benefits without additional demands -- are evolving owing to their practical significance. Traditional techniques in this domain mainly revolve around coarser granularities, such as filter pruning, thereby limiting their performance due to restricted pruning freedom.Recent advancements in Grouped Kernel Pruning (GKP) have enabled the utilization of finer granularity while maintaining the densely structured format. We observed that existing GKP methods often introduce dynamic operations to different aspects of their procedures, where many were done so at the cost of adding complications and/or imposing limitations -- e.g., requiring an expensive mixture of clustering schemes; or having dynamic pruning rates and sizes among groups, which lead to reliance on custom architecture support for its pruned models.In this work, we argue the best practice to introduce such dynamic operation to GKP is to make `Conv2d(groups)` (a.k.a. group count) flexible under an integral optimization, leveraging its ideal alignment with the infrastructure support of Grouped Convolution. Pursuing such direction, we present a one-shot, post-train, data-agnostic GKP method that is more performant, adaptive, and efficient than its predecessors; while simultaneously being a lot more user-friendly with little-to-no hyper-parameter tuning or handcrafted criteria required.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_Flexible_Group_Count_Enables_Hassle-Free_Structured_Pruning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_Flexible_Group_Count_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Jiamu and Zhong, Shaochen and Ye, Andrew and Liu, Zirui and Zhao, Sebastian and Zhou, Kaixiong and Li, Li and Choi, Soo-Hyun and Chen, Rui and Hu, Xia and Xu, Shuai and Chaudhary, Vipin},
    title     = {Flexible Group Count Enables Hassle-Free Structured Pruning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4807-4818}
}

raw_affiliation:
[{"affiliation": "Rice University, USA", "city": "Houston", "country": "USA"}, {"affiliation": "Case Western Reserve University, USA", "city": "Cleveland", "country": "USA"}, {"affiliation": "Samsung Electronics America, USA", "city": "San Jose", "country": "USA"}, {"affiliation": "Stanford University, USA", "city": "Stanford", "country": "USA"}, {"affiliation": "University of Minnesota\u2013Twin Cities, USA", "city": "Minneapolis", "country": "USA"}, {"affiliation": "UC Berkeley, USA", "city": "Berkeley", "country": "USA"}, {"affiliation": "North Carolina State University, USA", "city": "Raleigh", "country": "USA"}]

affiliations:
["Rice University, USA", "Case Western Reserve University, USA", "Samsung Electronics America, USA", "Stanford University, USA", "University of Minnesota\u2013Twin Cities, USA", "UC Berkeley, USA", "North Carolina State University, USA"]


title: EasyCraft: A Robust and Efficient Framework for Automatic Avatar Crafting
authors: Suzhen Wang, Weijie Chen, Wei Zhang, Minda Zhao, Lincheng Li, Rongsheng Zhang, Zhipeng Hu, Xin Yu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5581-5591
affiliations: ["Netease Fuxi AI Lab", "The University of Queensland"]
cities: ["Hangzhou", "Brisbane"]
countries: ["China", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_EasyCraft_A_Robust_and_Efficient_Framework_for_Automatic_Avatar_Crafting_CVPR_2025_paper.html

abstract:
Character customization, or 'face crafting,' is a vital feature in role-playing games (RPGs), enhancing player engagement by enabling the creation of personalized avatars. Existing automated methods often struggle with generalizability across diverse game engines due to their reliance on the intermediate constraints of specific image domain and typically support only one type of input, either text or image. To overcome these challenges, we introduce EasyCraft, an innovative end-to-end feedforward framework that automates character crafting by uniquely supporting both text and image inputs. Our approach employs a translator capable of converting facial images of any style into crafting parameters. We first establish a unified feature distribution in the translator's image encoder through self-supervised learning on a large-scale dataset, enabling photos of any style to be embedded into a unified feature representation. Subsequently, we map this unified feature distribution to crafting parameters specific to a game engine, a process that can be easily adapted to most game engines and thus enhances EasyCraft's generalizability. By integrating text-to-image techniques with our translator, EasyCraft also facilitates precise, text-based character crafting. EasyCraft's ability to integrate diverse inputs significantly enhances the versatility and accuracy of avatar creation. Extensive experiments on two RPG games demonstrate the effectiveness of our method, achieving state-of-the-art results and facilitating adaptability across various avatar engines.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_EasyCraft_A_Robust_and_Efficient_Framework_for_Automatic_Avatar_Crafting_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Suzhen and Chen, Weijie and Zhang, Wei and Zhao, Minda and Li, Lincheng and Zhang, Rongsheng and Hu, Zhipeng and Yu, Xin},
    title     = {EasyCraft: A Robust and Efficient Framework for Automatic Avatar Crafting},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5581-5591}
}

raw_affiliation:
[{"affiliation": "Netease Fuxi AI Lab", "city": "Hangzhou", "country": "China"}, {"affiliation": "The University of Queensland", "city": "Brisbane", "country": "Australia"}]


title: MeshArt: Generating Articulated Meshes with Structure-Guided Transformers
authors: Daoyi Gao, Yawar Siddiqui, Lei Li, Angela Dai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 618-627
affiliations: ["Technical University of Munich", "Meta"]
cities: ["Munich", "Menlo Park"]
countries: ["Germany", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gao_MeshArt_Generating_Articulated_Meshes_with_Structure-Guided_Transformers_CVPR_2025_paper.html

abstract:
Articulated 3D object generation is fundamental for creating realistic, functional, and interactable virtual assets which are not simply static. We introduce MeshArt, a hierarchical transformer-based approach to generate articulated 3D meshes with clean, compact geometry, reminiscent of human-crafted 3D models. We approach articulated mesh generation in a part-by-part fashion across two stages.First, we generate a high-level articulation-aware object structure; then, based on this structural information, we synthesize each part's mesh faces. Key to our approach is modeling both articulation structures and part meshes as sequences of quantized triangle embeddings, leading to a unified hierarchical framework with transformers for autoregressive generation.Object part structures are first generated as their bounding primitives and articulation modes; a second transformer, guided by these articulation structures, then generates each part's mesh triangles.To ensure coherency among generated parts, we introduce structure-guided conditioning that also incorporates local  part mesh connectivity.MeshArt shows significant improvements over state of the art, with 57.1% improvement in structure coverage and a 209-point improvement in mesh generation FID.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gao_MeshArt_Generating_Articulated_Meshes_with_Structure-Guided_Transformers_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gao_MeshArt_Generating_Articulated_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gao_2025_CVPR,
    author    = {Gao, Daoyi and Siddiqui, Yawar and Li, Lei and Dai, Angela},
    title     = {MeshArt: Generating Articulated Meshes with Structure-Guided Transformers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {618-627}
}

raw_affiliation:
[{"affiliation": "Technical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Meta", "city": "Menlo Park", "country": "USA"}]


title: Non-Natural Image Understanding with Advancing Frequency-based Vision Encoders
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29756-29766
affiliations: ["Zhejiang University", "Shanghai Artificial Intelligence Laboratory"]
cities: ["Hangzhou", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_Non-Natural_Image_Understanding_with_Advancing_Frequency-based_Vision_Encoders_CVPR_2025_paper.html

authors:
Wang Lin, QingSong Wang, Yueying Feng, Shulei Wang, Tao Jin, Zhou Zhao, Fei Wu, Chang Yao, Jingyuan Chen

abstract:
Large language models (LLMs) have significantly enhanced cross-modal understanding capabilities by integrating visual encoders with textual embeddings, giving rise to multimodal large language models (MLLMs). However, these models struggle with non-natural images such as geometric and charts, particularly in fields like education and finance. Despite efforts to collect datasets and fine-tune the MLLMs, the gap with natural image understanding is still evident, and the cost of collecting large and diverse non-natural image datasets is high. To address this, we analyzed the limitations of transformer-based vision encoders(ViT) within existing MLLMs from a frequency perspective. Studies have shown that ViT models are less effective at capturing high-frequency information, impairing their ability to capture elements like points, lines, and angles in non-natural images. In response, we introduced FM-ViT, a frequency-modulated vision encoder that utilizes Fourier decomposition to extract high and low frequency components from self-attention features and re-weight them during tuning to non-natural images. In addition, we combine the features of CNN models with FM-ViT and propose EDGE, an MLLM with enhanced graphical encoders tailored for understanding non-natural images. Extensive experiments have confirmed the effectiveness of our FM-ViT and EDGE in 4 types.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_Non-Natural_Image_Understanding_with_Advancing_Frequency-based_Vision_Encoders_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_Non-Natural_Image_Understanding_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Wang and Wang, QingSong and Feng, Yueying and Wang, Shulei and Jin, Tao and Zhao, Zhou and Wu, Fei and Yao, Chang and Chen, Jingyuan},
    title     = {Non-Natural Image Understanding with Advancing Frequency-based Vision Encoders},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29756-29766}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Shanghai Artificial Intelligence Laboratory", "city": "Shanghai", "country": "China"}]


title: Generative Multimodal Pretraining with Discrete Diffusion Timestep Tokens
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26136-26146
cities: ["Hangzhou", "Singapore", "Beijing", "Singapore"]
countries: ["China", "Singapore", "China", "Singapore"]
oral: yes
award_candidate: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Pan_Generative_Multimodal_Pretraining_with_Discrete_Diffusion_Timestep_Tokens_CVPR_2025_paper.html

authors:
Kaihang Pan, Wang Lin, Zhongqi Yue, Tenglong Ao, Liyu Jia, Wei Zhao, Juncheng Li, Siliang Tang, Hanwang Zhang

abstract:
Recent endeavors in Multimodal Large Language Models (MLLMs) aim to unify visual comprehension and generation by combining LLM and diffusion models, the state-of-the-art in each task, respectively. Existing approaches rely on spatial visual tokens, where image patches are encoded and arranged according to a spatial order (e.g., raster scan). However, we show that spatial tokens lack the recursive structure inherent to languages, hence form an impossible language for LLM to master. In this paper, we build a proper visual language by leveraging diffusion timesteps to learn discrete, recursive visual tokens. Our proposed tokens recursively compensate for the progressive attribute loss in noisy images as timesteps increase, enabling the diffusion model to reconstruct the original image at any timestep. This approach allows us to effectively integrate the strengths of LLMs in autoregressive reasoning and diffusion models in precise image generation, achieving seamless multimodal comprehension and generation within a unified framework.  Extensive experiments show that we achieve a new SOTA for multimodal comprehension and generation simultaneously compared with other MLLMs. Project Page: https://DDT-LLaMA.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Pan_Generative_Multimodal_Pretraining_with_Discrete_Diffusion_Timestep_Tokens_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Pan_Generative_Multimodal_Pretraining_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Pan_2025_CVPR,
    author    = {Pan, Kaihang and Lin, Wang and Yue, Zhongqi and Ao, Tenglong and Jia, Liyu and Zhao, Wei and Li, Juncheng and Tang, Siliang and Zhang, Hanwang},
    title     = {Generative Multimodal Pretraining with Discrete Diffusion Timestep Tokens},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26136-26146}
}

raw_affiliation:
[{"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Huawei Singapore Research Center", "city": "Singapore", "country": "Singapore"}]

affiliations:
["Zhejiang University", "Nanyang Technological University", "Peking University", "Huawei Singapore Research Center"]


authors: Su Sun, Cheng Zhao, Zhuoyang Sun, Yingjie Victor Chen, Mei Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27487-27496
affiliations: ["Purdue University", "Microsoft"]
cities: ["West Lafayette", "Redmond"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_SplatFlow_Self-Supervised_Dynamic_Gaussian_Splatting_in_Neural_Motion_Flow_Field_CVPR_2025_paper.html

title:
SplatFlow: Self-Supervised Dynamic Gaussian Splatting in Neural Motion Flow Field for Autonomous Driving

abstract:
Most existing Dynamic Gaussian Splatting methods for complex dynamic urban scenarios rely on accurate object-level supervision from expensive manual labeling, limiting their scalability in real-world applications. In this paper, we introduce SplatFlow, a Self-Supervised Dynamic Gaussian Splatting within Neural Motion Flow Fields (NMFF) to learn 4D space-time representations without requiring tracked 3D bounding boxes, enabling accurate dynamic scene reconstruction and novel view RGB/depth/flow synthesis. SplatFlow designs a unified framework to seamlessly integrate time-dependent 4D Gaussian representation within NMFF, where NMFF is a set of implicit functions to model temporal motions of both LiDAR points and Gaussians as continuous motion flow fields. Leveraging NMFF, SplatFlow effectively decomposes static background and dynamic objects, representing them with 3D and 4D Gaussian primitives, respectively. NMFF also models the correspondences of each 4D Gaussian across time, which aggregates temporal features to enhance cross-view consistency of dynamic components. SplatFlow further improves dynamic object identification by distilling features from 2D foundation models into 4D space-time representation. Comprehensive evaluations conducted on the Waymo and KITTI Datasets validate SplatFlow's state-of-the-art (SOTA) performance for both image reconstruction and novel view synthesis in dynamic urban scenarios.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_SplatFlow_Self-Supervised_Dynamic_Gaussian_Splatting_in_Neural_Motion_Flow_Field_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_SplatFlow_Self-Supervised_Dynamic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Su and Zhao, Cheng and Sun, Zhuoyang and Chen, Yingjie Victor and Chen, Mei},
    title     = {SplatFlow: Self-Supervised Dynamic Gaussian Splatting in Neural Motion Flow Field for Autonomous Driving},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27487-27496}
}

raw_affiliation:
[{"affiliation": "Purdue University", "city": "West Lafayette", "country": "USA"}, {"affiliation": "Microsoft", "city": "Redmond", "country": "USA"}]


title: Zero-shot 3D Question Answering via Voxel-based Dynamic Token Compression
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19424-19434
affiliations: ["University of Washington", "Amazon"]
cities: ["Seattle", "Seattle"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Huang_Zero-shot_3D_Question_Answering_via_Voxel-based_Dynamic_Token_Compression_CVPR_2025_paper.html

authors:
Hsiang-Wei Huang, Fu-Chen Chen, Wenhao Chai, Che-Chun Su, Lu Xia, Sanghun Jung, Cheng-Yen Yang, Jenq-Neng Hwang, Min Sun, Cheng-Hao Kuo

abstract:
Recent advancements in 3D Large Multi-modal Models (3D-LMMs) have driven significant progress in 3D question answering. However, recent multi-frame Vision-Language Models (VLMs) demonstrate superior performance compared to 3D-LMMs on 3D question answering tasks, largely due to the greater scale and diversity of available 2D image data in contrast to the more limited 3D data. Multi-frame VLMs, although achieving superior performance, suffer from the difficulty of retaining all the detailed visual information in the 3D scene while limiting the number of visual tokens. Common methods such as token pooling, reduce visual token usage but often lead to information loss, impairing the model's ability to preserve visual details essential for 3D question answering tasks. To address this, we propose voxel-based Dynamic Token Compression (DTC), which combines 3D spatial priors and visual semantics to achieve over 90% reduction in visual tokens usage for current multi-frame VLMs. Our method maintains performance comparable to state-of-the-art models on 3D question answering benchmarks including OpenEQA and ScanQA, demonstrating its effectiveness.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Huang_Zero-shot_3D_Question_Answering_via_Voxel-based_Dynamic_Token_Compression_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Huang_Zero-shot_3D_Question_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Huang_2025_CVPR,
    author    = {Huang, Hsiang-Wei and Chen, Fu-Chen and Chai, Wenhao and Su, Che-Chun and Xia, Lu and Jung, Sanghun and Yang, Cheng-Yen and Hwang, Jenq-Neng and Sun, Min and Kuo, Cheng-Hao},
    title     = {Zero-shot 3D Question Answering via Voxel-based Dynamic Token Compression},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19424-19434}
}

raw_affiliation:
[{"affiliation": "University of Washington", "city": "Seattle", "country": "USA"}, {"affiliation": "Amazon", "city": "Seattle", "country": "USA"}]


authors: Sohan Patnaik, Rishabh Jain, Balaji Krishnamurthy, Mausoom Sarkar
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23701-23711
raw_affiliation: [{"affiliation": "Media and Data Science Research, Adobe", "city": "San Jose", "country": "USA"}]
affiliations: ["Media and Data Science Research, Adobe"]
cities: ["San Jose"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Patnaik_AesthetiQ_Enhancing_Graphic_Layout_Design_via_Aesthetic-Aware_Preference_Alignment_of_CVPR_2025_paper.html

title:
AesthetiQ: Enhancing Graphic Layout Design via Aesthetic-Aware Preference Alignment of Multi-modal Large Language Models

abstract:
Visual layouts are essential in graphic design fields such as advertising, posters, and web interfaces. The application of generative models for content-aware layout generation has recently gained traction. However, these models fail to understand the contextual aesthetic requirements of layout design and do not align with human-like preferences, primarily treating it as a prediction task without considering the final rendered output. To overcome these problems, we offer Aesthetic-Aware Preference Alignment (AAPA), a novel technique to train a Multi-modal Large Language Model (MLLM) for layout prediction that uses MLLM's aesthetic preferences for Direct Preference Optimization over graphic layouts. We propose a data filtering protocol utilizing our layout-quality heuristics for AAPA to ensure training happens on high-quality layouts. Additionally, we introduce a novel evaluation metric that uses another MLLM to compute the win rate of the generated layout against the ground-truth layout based on aesthetics criteria. We also demonstrate the applicability of AAPA for MLLMs of varying scales (1B to 8B parameters) and LLM families (Qwen, Phi, InternLM). By conducting thorough qualitative and quantitative analyses, we verify the efficacy of our approach on two challenging benchmarks - Crello and Webui, showcasing 17%, and 16% improvement over current State-of-The-Art methods, thereby highlighting the potential of MLLMs in aesthetic-aware layout generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Patnaik_AesthetiQ_Enhancing_Graphic_Layout_Design_via_Aesthetic-Aware_Preference_Alignment_of_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Patnaik_AesthetiQ_Enhancing_Graphic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Patnaik_2025_CVPR,
    author    = {Patnaik, Sohan and Jain, Rishabh and Krishnamurthy, Balaji and Sarkar, Mausoom},
    title     = {AesthetiQ: Enhancing Graphic Layout Design via Aesthetic-Aware Preference Alignment of Multi-modal Large Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23701-23711}
}


title: Enhanced then Progressive Fusion with View Graph for Multi-View Clustering
authors: Zhibin Dong, Meng Liu, Siwei Wang, Ke Liang, Yi Zhang, Suyuan Liu, Jiaqi Jin, Xinwang Liu, En Zhu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15518-15527
affiliations: ["National University of Defense Technology", "Intelligent Game and Decision Lab"]
cities: ["Changsha", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Dong_Enhanced_then_Progressive_Fusion_with_View_Graph_for_Multi-View_Clustering_CVPR_2025_paper.html

abstract:
Multi-view clustering aims to improve clustering accuracy by effectively integrating complementary information from multiple perspectives. However, existing methods often encounter challenges such as feature conflicts between views and insufficient enhancement of individual view features, which hinder clustering performance. To address these challenges, we propose a novel framework, EPFMVC, which integrates feature enhancement with progressive fusion to more effectively align multi-view data. Specifically, we introduce two key innovations: (1) a Feature Channel Attention Encoder (FCAencoder), which adaptively enhances the most discriminative features in each view, and (2) a View Graph-based Progressive Fusion Mechanism, which constructs a view graph using optimal transport (OT) distance to progressively fuse similar views while minimizing inter-view conflicts. By leveraging multi-head attention, the fusion process gradually integrates complementary information, ensuring more consistent and robust shared representations. These innovations enable superior representation learning and effective fusion across views. Extensive experiments demonstrate that our method significantly outperforms state-of-the-art techniques, achieving notable improvements in multi-view clustering tasks across various datasets and evaluation metrics.

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

bibtex:
@InProceedings{Dong_2025_CVPR,
    author    = {Dong, Zhibin and Liu, Meng and Wang, Siwei and Liang, Ke and Zhang, Yi and Liu, Suyuan and Jin, Jiaqi and Liu, Xinwang and Zhu, En},
    title     = {Enhanced then Progressive Fusion with View Graph for Multi-View Clustering},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15518-15527}
}

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


title: FINECAPTION: Compositional Image Captioning Focusing on Wherever You Want at Any Granularity
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24763-24773
affiliations: ["University of Rochester", "Adobe Research"]
cities: ["Rochester", "San Jose"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hua_FINECAPTION_Compositional_Image_Captioning_Focusing_on_Wherever_You_Want_at_CVPR_2025_paper.html

authors:
Hang Hua, Qing Liu, Lingzhi Zhang, Jing Shi, Soo Ye Kim, Zhifei Zhang, Yilin Wang, Jianming Zhang, Zhe Lin, Jiebo Luo

abstract:
The advent of large Vision-Language Models (VLMs) has significantly advanced multimodal tasks, enabling more sophisticated and accurate integration of visual and textual information across various applications, including image and video captioning, visual question answering, and cross-modal retrieval.Despite their superior capabilities, VLMs still struggle with fine-grained compositional image region descriptions. Specifically, they have difficulty recognizing arbitrary segmentation masks as referential inputs, interpreting compositional aspect instructions for referencing, and precisely describing the compositional aspects of a region. However, compositionality--the ability to understand and generate novel combinations of known visual and textual components--is critical for facilitating coherent reasoning and understanding across modalities in VLMs. To address this issue, we propose OpenCompositionCap, a new dataset for multi-grained region compositional image captioning that distinguishes itself from prior works by introducing the new task of compositional aspect-aware regional image captioning. To support this endeavor, we also introduce a new VLM model, FineCaption. The empirical results illustrate the effectiveness of our proposed model compared with other strong VLMs. In addition, we analyze the capabilities of current VLMs in recognizing various visual prompts for compositional region image captioning, highlighting areas for improvement in VLM design and training.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hua_FINECAPTION_Compositional_Image_Captioning_Focusing_on_Wherever_You_Want_at_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Hua_FINECAPTION_Compositional_Image_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Hua_2025_CVPR,
    author    = {Hua, Hang and Liu, Qing and Zhang, Lingzhi and Shi, Jing and Kim, Soo Ye and Zhang, Zhifei and Wang, Yilin and Zhang, Jianming and Lin, Zhe and Luo, Jiebo},
    title     = {FINECAPTION: Compositional Image Captioning Focusing on Wherever You Want at Any Granularity},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24763-24773}
}

raw_affiliation:
[{"affiliation": "University of Rochester", "city": "Rochester", "country": "USA"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}]


title: Adaptive Non-Uniform Timestep Sampling for Accelerating Diffusion Model Training
authors: Myunsoo Kim, Donghyeon Ki, Seong-Woong Shim, Byung-Jun Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2513-2522
affiliations: ["Korea University", "Gauss Labs Inc."]
cities: ["Seoul", "Seoul"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Adaptive_Non-Uniform_Timestep_Sampling_for_Accelerating_Diffusion_Model_Training_CVPR_2025_paper.html

abstract:
As a highly expressive generative model, diffusion models have demonstrated exceptional success across various domains, including image generation, natural language processing, and combinatorial optimization. However, as data distributions grow more complex, training these models to convergence becomes increasingly computationally intensive. While diffusion models are typically trained using uniform timestep sampling, our research shows that the variance in stochastic gradients varies significantly across timesteps, with high-variance timesteps becoming bottlenecks that hinder faster convergence. To address this issue, we introduce a non-uniform timestep sampling method that prioritizes these more critical timesteps. Our method tracks the impact of gradient updates on the objective for each timestep, adaptively selecting those most likely to minimize the objective effectively. Experimental results demonstrate that this approach not only accelerates the training process, but also leads to improved performance at convergence. Furthermore, our method shows robust performance across various datasets, scheduling strategies, and diffusion architectures, outperforming previously proposed timestep sampling and weighting heuristics that lack this degree of robustness.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Adaptive_Non-Uniform_Timestep_Sampling_for_Accelerating_Diffusion_Model_Training_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_Adaptive_Non-Uniform_Timestep_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Myunsoo and Ki, Donghyeon and Shim, Seong-Woong and Lee, Byung-Jun},
    title     = {Adaptive Non-Uniform Timestep Sampling for Accelerating Diffusion Model Training},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2513-2522}
}

raw_affiliation:
[{"affiliation": "Korea University", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Gauss Labs Inc.", "city": "Seoul", "country": "South Korea"}]


title: Chebyshev Attention Depth Permutation Texture Network with Latent Texture Attribute Loss
authors: Ravishankar Evani, Deepu Rajan, Shangbo Mao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23423-23432
affiliations: ["College of Computing and Data Science, Nanyang Technological University"]
cities: ["Singapore"]
countries: ["Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Evani_Chebyshev_Attention_Depth_Permutation_Texture_Network_with_Latent_Texture_Attribute_CVPR_2025_paper.html

abstract:
Despite recent advances in deep texture recognition, existing methods still lack representational diversity and struggle to capture and preserve discriminative cues across stages of representation hierarchies. Moreover, many rely on loss formulations that prioritize recognition accuracy while overlooking spatial coherence and statistical consistency in the feature space. To address these issues, we propose three key innovations: Stochastic Local Texture Masking (SLTM), a regularization strategy that randomly occludes small texture patches to promote the learning of broader spatial and contextual dependencies; the Chebyshev Attention Depth Permutation Texture Network (CAPTN), a novel architecture that learns expressive and persistent Latent Texture Attribute (LTA) representations. CAPTN integrates a Texture Frequency Attention (TFA) module that generates LTAs and enables frequency-aware interpretability, a Dual Depth Permutation (D2P) module to expose complementary channel adjacency patterns, and Learnable Chebyshev Polynomials (LCPs) to model high-order orderless LTA transformations via recursive Chebyshev basis expansion; and a Latent Texture Attribute Loss that jointly optimizes classification accuracy, statistical alignment, and spatial fidelity. CAPTN supports end-to-end training without relying on fine-tuned CNN backbones and achieves state-of-the-art performance on several texture and material recognition benchmarks. (Code: https://github.com/RavishankarEvani/CAPTN)

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Evani_Chebyshev_Attention_Depth_Permutation_Texture_Network_with_Latent_Texture_Attribute_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Evani_Chebyshev_Attention_Depth_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Evani_2025_CVPR,
    author    = {Evani, Ravishankar and Rajan, Deepu and Mao, Shangbo},
    title     = {Chebyshev Attention Depth Permutation Texture Network with Latent Texture Attribute Loss},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23423-23432}
}

raw_affiliation:
[{"affiliation": "College of Computing and Data Science, Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]


title: Explainable Saliency: Articulating Reasoning with Contextual Prioritization
authors: Nuo Chen, Ming Jiang, Qi Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9601-9610
affiliations: ["Department of Computer Science and Engineering, University of Minnesota"]
cities: ["Minneapolis"]
countries: ["USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_Explainable_Saliency_Articulating_Reasoning_with_Contextual_Prioritization_CVPR_2025_paper.html

abstract:
Deep saliency models, which predict what parts of an image capture our attention, are often like black boxes. This limits their use, especially in areas where understanding why a model makes a decision is crucial. Our research tackles this challenge by developing an explainable saliency (XSal) model that not only identifies what is important in an image, but also explains its choices in a way that makes sense to humans. We achieve this by using vision-language models to reason about images and by focusing the model's attention on the most crucial information using a contextual prioritization mechanism. Unlike prior approaches that rely on fixation descriptions or soft-attention based semantic aggregation, our method directly models the reasoning steps involved in saliency prediction, generating selectively prioritized explanations clarify why specific regions are prioritized. Comprehensive evaluations demonstrate the effectiveness of our model in generating high-quality saliency maps and coherent, contextually relevant explanations. This research is a step towards more transparent and trustworthy AI systems that can help us understand and navigate the world around us.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_Explainable_Saliency_Articulating_Reasoning_with_Contextual_Prioritization_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_Explainable_Saliency_Articulating_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Nuo and Jiang, Ming and Zhao, Qi},
    title     = {Explainable Saliency: Articulating Reasoning with Contextual Prioritization},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9601-9610}
}

raw_affiliation:
[{"affiliation": "Department of Computer Science and Engineering, University of Minnesota", "city": "Minneapolis", "country": "USA"}]


title: Decentralized Diffusion Models
authors: David McAllister, Matthew Tancik, Jiaming Song, Angjoo Kanazawa
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23323-23333
affiliations: ["University of California, Berkeley", "Luma AI"]
cities: ["Berkeley", "San Francisco"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/McAllister_Decentralized_Diffusion_Models_CVPR_2025_paper.html

abstract:
Large-scale AI model training divides work across thousands of GPUs then synchronizes gradients across them at each step. This incurs a significant network burden that only centralized, monolithic clusters can support, driving up infrastructure costs and straining power systems. We propose Decentralized Diffusion Models, a scalable framework to distribute diffusion model training across independent clusters or datacenters by eliminating the dependence on a centralized, high-bandwidth networking fabric. Our method trains a set of expert diffusion models over partitions of the dataset, each in full isolation from one another. At inference time, they ensemble through a lightweight router. We show that this ensemble collectively optimizes the same objective as a single model trained over the whole dataset. This means we can divide the training burden among a number of "compute islands," lowering infrastructure costs and improving resilience to localized GPU failures. Decentralized diffusion models empower researchers to take advantage of smaller, more cost-effective and more readily available compute like on-demand GPU nodes rather than central integrated systems. We conduct extensive experiments on ImageNet and LAION Aesthetics, showing that decentralized diffusion models FLOP-for-FLOP outperform standard diffusion models. We finally scale our approach to 24 billion parameters, demonstrating that high-quality diffusion models can now be trained with just eight individual GPU nodes in less than a week.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/McAllister_Decentralized_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/McAllister_Decentralized_Diffusion_Models_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{McAllister_2025_CVPR,
    author    = {McAllister, David and Tancik, Matthew and Song, Jiaming and Kanazawa, Angjoo},
    title     = {Decentralized Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23323-23333}
}

raw_affiliation:
[{"affiliation": "University of California, Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "Luma AI", "city": "San Francisco", "country": "USA"}]


title: AnyEdit: Mastering Unified High-Quality Image Editing for Any Idea
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26125-26135
affiliations: ["Zhejiang University", "Nanyang Technological University", "Ant Group"]
cities: ["Hangzhou", "Singapore", "Hangzhou"]
countries: ["China", "Singapore", "China"]
oral: yes
award_candidate: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_AnyEdit_Mastering_Unified_High-Quality_Image_Editing_for_Any_Idea_CVPR_2025_paper.html

authors:
Qifan Yu, Wei Chow, Zhongqi Yue, Kaihang Pan, Yang Wu, Xiaoyang Wan, Juncheng Li, Siliang Tang, Hanwang Zhang, Yueting Zhuang

abstract:
Instruction-based image editing aims to modify specific image elements with natural language instructions. However, current models in this domain often struggle to execute complex user instructions accurately, as they are trained on low-quality data with limited editing types. We present AnyEdit, a comprehensive multi-modal instruction editing dataset, comprising 2.5 million high-quality editing pairs spanning over 20 editing types and five domains. We ensure the diversity and quality of the AnyEdit collection through three aspects: initial data diversity, adaptive editing process, and automated selection of editing results. Using the dataset, we further train a novel AnyEdit Stable Diffusion with task-aware routing and learnable task embedding for unified image editing. Comprehensive experiments on three benchmark datasets show that AnyEdit consistently boosts the performance of diffusion-based editing models. This presents prospects for developing instruction-driven image editing models that support human creativity.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_AnyEdit_Mastering_Unified_High-Quality_Image_Editing_for_Any_Idea_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_AnyEdit_Mastering_Unified_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Qifan and Chow, Wei and Yue, Zhongqi and Pan, Kaihang and Wu, Yang and Wan, Xiaoyang and Li, Juncheng and Tang, Siliang and Zhang, Hanwang and Zhuang, Yueting},
    title     = {AnyEdit: Mastering Unified High-Quality Image Editing for Any Idea},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26125-26135}
}

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


title: Compass Control: Multi Object Orientation Control for Text-to-Image Generation
authors: Rishubh Parihar, Vaibhav Agrawal, Sachidanand VS, Venkatesh Babu Radhakrishnan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2791-2801
affiliations: ["IISc", "IIIT"]
cities: ["Bangalore", "Hyderabad"]
countries: ["India", "India"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Parihar_Compass_Control_Multi_Object_Orientation_Control_for_Text-to-Image_Generation_CVPR_2025_paper.html

abstract:
Existing approaches for controlling text-to-image diffusion models, while powerful, do not allow for explicit 3D object-centric control, such as precise control of object orientation. In this work, we address the problem of multi-object orientation control in text-to-image diffusion models. This enables the generation of diverse multi-object scenes with precise orientation control for each object. The key idea is to condition the diffusion model with a set of orientation-aware compass tokens, one for each object, along with text tokens. A light-weight encoder network predicts these compass tokens taking object orientation as the input. The model is trained on a synthetic dataset of procedurally generated scenes, each containing one or two 3D-assets on a plain background. However, direct training this framework results in poor orientation control as well as leads to entanglement among objects. To mitigate this, we intervene in the generation process and constrain the cross-attention maps of each compass token to its corresponding object regions. The trained model is able to achieve precise orientation control for a) complex objects not seen during training, and b) multi-object scenes with more than two objects, indicating strong generalization capabilities. Further, when combined with personalization methods, our method precisely controls the orientation of the new object in diverse contexts. Our method achieves state-of-the-art orientation control and text alignment quantified with extensive evaluations and a user study.

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

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Parihar_Compass_Control_Multi_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Parihar_2025_CVPR,
    author    = {Parihar, Rishubh and Agrawal, Vaibhav and VS, Sachidanand and Radhakrishnan, Venkatesh Babu},
    title     = {Compass Control: Multi Object Orientation Control for Text-to-Image Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2791-2801}
}

raw_affiliation:
[{"affiliation": "IISc", "city": "Bangalore", "country": "India"}, {"affiliation": "IIIT", "city": "Hyderabad", "country": "India"}]


authors: Farzad Beizaee, Gregory A. Lodygensky, Christian Desrosiers, Jose Dolz
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19088-19097
affiliations: ["\u00b4ETS Montreal", "CHU-Sainte-Justine Montreal"]
cities: ["Montreal", "Montreal"]
countries: ["Canada", "Canada"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Beizaee_Correcting_Deviations_from_Normality_A_Reformulated_Diffusion_Model_for_Multi-Class_CVPR_2025_paper.html

title:
Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detection

abstract:
Recent advances in diffusion models have spurred research into their application for Reconstruction-based unsupervised anomaly detection. However, these methods may struggle with maintaining structural integrity and recovering the anomaly-free content of abnormal regions, especially in multi-class scenarios. Furthermore, diffusion models are inherently designed to generate images from pure noise and struggle to selectively alter anomalous regions of an image while preserving normal ones. This leads to potential degradation of normal regions during reconstruction, hampering the effectiveness of anomaly detection.  This paper introduces a reformulation of the standard diffusion model geared toward selective region alteration, allowing the accurate identification of anomalies. By modeling anomalies as noise in the latent space, our proposed Deviation correction diffusion (DeCo-Diff) model preserves the normal regions and encourages transformations exclusively on anomalous areas. This selective approach enhances the reconstruction quality, facilitating effective unsupervised detection and localization of anomaly regions. Comprehensive evaluations demonstrate the superiority of our method in accurately identifying and localizing anomalies in complex images, with pixel-level AUPRC improvements of 11-14% over state-of-the-art models on well-known anomaly detection datasets. The code is available at https://github.com/farzad-bz/DeCo-Diff

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Beizaee_Correcting_Deviations_from_Normality_A_Reformulated_Diffusion_Model_for_Multi-Class_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Beizaee_Correcting_Deviations_from_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Beizaee_2025_CVPR,
    author    = {Beizaee, Farzad and Lodygensky, Gregory A. and Desrosiers, Christian and Dolz, Jose},
    title     = {Correcting Deviations from Normality: A Reformulated Diffusion Model for Multi-Class Unsupervised Anomaly Detection},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19088-19097}
}

raw_affiliation:
[{"affiliation": "\u00b4ETS Montreal", "city": "Montreal", "country": "Canada"}, {"affiliation": "CHU-Sainte-Justine Montreal", "city": "Montreal", "country": "Canada"}]


title: Continuous 3D Perception Model with Persistent State
authors: Qianqian Wang, Yifei Zhang, Aleksander Holynski, Alexei A. Efros, Angjoo Kanazawa
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10510-10522
affiliations: ["University of California, Berkeley", "Google DeepMind"]
cities: ["Berkeley", "Mountain View"]
countries: ["USA", "USA"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Continuous_3D_Perception_Model_with_Persistent_State_CVPR_2025_paper.html

abstract:
We present a unified framework capable of solving a broad range of 3D tasks. Our approach features a stateful recurrent model that continuously updates its state representation with each new observation. Given a stream of images, this evolving state can be used to generate metric-scale pointmaps (per-pixel 3D points) for each new input in an online fashion. These pointmaps reside within a common coordinate system, and can be accumulated into a coherent, dense scene reconstruction that updates as new images arrive. Our model, called CUT3R (Continuous Updating Transformer for 3D Reconstruction), captures rich priors of real-world scenes: not only can it predict accurate pointmaps from image observations, but it can also infer unseen regions of the scene by probing at virtual, unobserved views. Our method is simple yet highly flexible, naturally accepting varying length of images that may be either video streams or unordered photo collections, containing both static and dynamic content. We evaluate our method on various 3D/4D tasks and demonstrate competitive or state-of-the-art performance in each. Project page: https://cut3r.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Continuous_3D_Perception_Model_with_Persistent_State_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Continuous_3D_Perception_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Qianqian and Zhang, Yifei and Holynski, Aleksander and Efros, Alexei A. and Kanazawa, Angjoo},
    title     = {Continuous 3D Perception Model with Persistent State},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10510-10522}
}

raw_affiliation:
[{"affiliation": "University of California, Berkeley", "city": "Berkeley", "country": "USA"}, {"affiliation": "Google DeepMind", "city": "Mountain View", "country": "USA"}]


title: LP-Diff: Towards Improved Restoration of Real-World Degraded License Plate
authors: Haoyan Gong, Zhenrong Zhang, Yuzheng Feng, Anh Nguyen, Hongbin Liu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17831-17840
affiliations: ["Xi\u2019an Jiaotong-Liverpool University", "University of Liverpool"]
cities: ["Suzhou", "Liverpool"]
countries: ["China", "UK"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gong_LP-Diff_Towards_Improved_Restoration_of_Real-World_Degraded_License_Plate_CVPR_2025_paper.html

abstract:
License plate (LP) recognition is crucial in intelligent traffic management systems. However, factors such as long distances and poor camera quality often lead to severe degradation of captured LP images, posing challenges to accurate recognition. The design of License Plate Image Restoration (LPIR) methods frequently relies on synthetic degraded data, which limits their effectiveness on real-world severely degraded LP images. To address this issue, we introduce the first paired LPIR dataset collected in real-world scenarios, named MDLP, including 10,245 pairs of multi-frame severely degraded LP images and their corresponding clear images. To better restore severely degraded LP, we propose a novel Diffusion-based network, called LP-Diff, to tackle real-world LPIR tasks. Our approach incorporates (1) an Inter-frame Cross Attention Module to fuse temporal information across multiple frames, (2) a Texture Enhancement Module to restore texture information in degraded images, and (3) a Dual-Pathway Fusion Module to select effective features from both channel and spatial dimensions. Extensive experiments demonstrate the reliability of our dataset for model training and evaluation. Our proposed LP-Diff consistently outperforms other state-of-the-art image restoration methods on real-world LPIR tasks. The dataset and code are available at https://github.com/haoyGONG/LP-Diff.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gong_LP-Diff_Towards_Improved_Restoration_of_Real-World_Degraded_License_Plate_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gong_LP-Diff_Towards_Improved_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gong_2025_CVPR,
    author    = {Gong, Haoyan and Zhang, Zhenrong and Feng, Yuzheng and Nguyen, Anh and Liu, Hongbin},
    title     = {LP-Diff: Towards Improved Restoration of Real-World Degraded License Plate},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17831-17840}
}

raw_affiliation:
[{"affiliation": "Xi\u2019an Jiaotong-Liverpool University", "city": "Suzhou", "country": "China"}, {"affiliation": "University of Liverpool", "city": "Liverpool", "country": "UK"}]


authors: Yiheng Li, Yang Yang, Zichang Tan, Huan Liu, Weihua Chen, Xu Zhou, Zhen Lei
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9242-9252
cities: ["Beijing", "Beijing", "Beijing", "Macau", "Shenzhen", "Beijing", "Hangzhou"]
countries: ["China", "China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_Unleashing_the_Potential_of_Consistency_Learning_for_Detecting_and_Grounding_CVPR_2025_paper.html

title:
Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation

abstract:
To tackle the threat of fake news, the task of detecting and grounding multi-modal media manipulation (DGM4) has received increasing attention. However, most state-of-the-art methods fail to explore the fine-grained consistency within local content, usually resulting in an inadequate perception of detailed forgery and unreliable results. In this paper, we propose a novel approach named Contextual-Semantic Consistency Learning (CSCL) to enhance the fine-grained perception ability of forgery for DGM^4. Two branches for image and text modalities are established, each of which contains two cascaded decoders, i.e., Contextual Consistency Decoder (CCD) and Semantic Consistency Decoder (SCD), to capture within-modality contextual consistency and across-modality semantic consistency, respectively. Both CCD and SCD adhere to the same criteria for capturing fine-grained forgery details. To be specific, each module first constructs consistency features by leveraging additional supervision from the heterogeneous information of each token pair. Then, the forgery-aware reasoning or aggregating is adopted to deeply seek forgery cues based on the consistency features. Extensive experiments on DGM4 datasets prove that CSCL achieves new state-of-the-art performance, especially for the results of grounding manipulated content. Codes and weights are avaliable at https://github.com/liyih/CSCL.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_Unleashing_the_Potential_of_Consistency_Learning_for_Detecting_and_Grounding_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_Unleashing_the_Potential_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Yiheng and Yang, Yang and Tan, Zichang and Liu, Huan and Chen, Weihua and Zhou, Xu and Lei, Zhen},
    title     = {Unleashing the Potential of Consistency Learning for Detecting and Grounding Multi-Modal Media Manipulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9242-9252}
}

raw_affiliation:
[{"affiliation": "MAIS, Institute of Automation, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Artificial Intelligence, University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "CAIR, HKISI, Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "School of Computer Science and Engineering, the Faculty of Innovation Engineering, M.U.S.T", "city": "Macau", "country": "China"}, {"affiliation": "Sangfor Technologies Inc.", "city": "Shenzhen", "country": "China"}, {"affiliation": "Beijing Jiaotong University", "city": "Beijing", "country": "China"}, {"affiliation": "Alibaba Group", "city": "Hangzhou", "country": "China"}]

affiliations:
["MAIS, Institute of Automation, Chinese Academy of Sciences", "School of Artificial Intelligence, University of Chinese Academy of Sciences", "CAIR, HKISI, Chinese Academy of Sciences", "School of Computer Science and Engineering, the Faculty of Innovation Engineering, M.U.S.T", "Sangfor Technologies Inc.", "Beijing Jiaotong University", "Alibaba Group"]


title: DNF: Unconditional 4D Generation with Dictionary-based Neural Fields
authors: Xinyi Zhang, Naiqi Li, Angela Dai
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26047-26056
affiliations: ["T echnical University of Munich", "Tsinghua University"]
cities: ["Munich", "Beijing"]
countries: ["Germany", "China"]
oral: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhang_DNF_Unconditional_4D_Generation_with_Dictionary-based_Neural_Fields_CVPR_2025_paper.html

abstract:
While remarkable success has been achived through diffusion-based 3D generative models for shapes, 4D generative modeling remains challenging due to the complexity of object deformations over time. We propose DNF, a new 4D representation for unconditional generative modeling that efficiently models deformable shapes with disentangled shape and motion while capturing high-fidelity details in the deforming objects. To achieve this, we propose a dictionary learning approach to disentangle 4D motion from shape as neural fields.Both shape and motion are represented as learned latent spaces, where each deformable shape is represented by its shape and motion global latent codes, shape-specific coefficient vectors, and shared dictionary information. This captures both shape-specific detail and global shared information in the learned dictionary. Our dictionary-based representation well balances fidelity, contiguity and compression -- combined with a transformer-based diffusion model, our method is able to generate effective, high-fidelity 4D animations.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhang_DNF_Unconditional_4D_Generation_with_Dictionary-based_Neural_Fields_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhang_DNF_Unconditional_4D_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhang_2025_CVPR,
    author    = {Zhang, Xinyi and Li, Naiqi and Dai, Angela},
    title     = {DNF: Unconditional 4D Generation with Dictionary-based Neural Fields},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26047-26056}
}

raw_affiliation:
[{"affiliation": "T echnical University of Munich", "city": "Munich", "country": "Germany"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}]


title: ARM: Appearance Reconstruction Model for Relightable 3D Generation
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21425-21437
affiliations: ["University of Utah", "Zhejiang University", "UCLA", "Amazon"]
cities: ["Salt Lake City", "Hangzhou", "Los Angeles", "Seattle"]
countries: ["USA", "China", "USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Feng_ARM_Appearance_Reconstruction_Model_for_Relightable_3D_Generation_CVPR_2025_paper.html

authors:
Xiang Feng, Chang Yu, Zoubin Bi, Yintong Shang, Feng Gao, Hongzhi Wu, Kun Zhou, Chenfanfu Jiang, Yin Yang

abstract:
Recent image-to-3D reconstruction models have greatly advanced geometry generation, but they still struggle to faithfully generate realistic appearance. To address this, we introduce ARM, a novel method that reconstructs high-quality 3D meshes and realistic appearance from sparse-view images. The core of ARM lies in decoupling geometry from appearance, processing appearance within the UV texture space. Unlike previous methods, ARM improves texture quality by explicitly back-projecting measurements onto the texture map and processing them in a UV space module with a global receptive field. To resolve ambiguities between material and illumination in input images, ARM introduces a material prior that encodes semantic appearance information, enhancing the robustness of appearance decomposition. Trained on just 8 H100 GPUs, ARM outperforms existing methods both quantitatively and qualitatively. Our project page is available at https://arm-aigc.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Feng_ARM_Appearance_Reconstruction_Model_for_Relightable_3D_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Feng_ARM_Appearance_Reconstruction_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Feng_2025_CVPR,
    author    = {Feng, Xiang and Yu, Chang and Bi, Zoubin and Shang, Yintong and Gao, Feng and Wu, Hongzhi and Zhou, Kun and Jiang, Chenfanfu and Yang, Yin},
    title     = {ARM: Appearance Reconstruction Model for Relightable 3D Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21425-21437}
}

raw_affiliation:
[{"affiliation": "University of Utah", "city": "Salt Lake City", "country": "USA"}, {"affiliation": "Zhejiang University", "city": "Hangzhou", "country": "China"}, {"affiliation": "UCLA", "city": "Los Angeles", "country": "USA"}, {"affiliation": "Amazon", "city": "Seattle", "country": "USA"}]


title: VideoGEM: Training-free Action Grounding in Videos
authors: Felix Vogel, Walid Bousselham, Anna Kukleva, Nina Shvetsova, Hilde Kuehne
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3374-3383
cities: ["Frankfurt", "Tuebingen", "Saarbruecken", "Cambridge"]
countries: ["Germany", "Germany", "Germany", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Vogel_VideoGEM_Training-free_Action_Grounding_in_Videos_CVPR_2025_paper.html

abstract:
Vision-language foundation models have shown impressive capabilities across various zero-shot tasks, including training-free localization and grounding, primarily focusing on localizing objects in images. However, leveraging those capabilities to localize actions and events in videos is challenging, as actions have less physical outline and are usually described by higher-level concepts.In this work, we propose VideoGEM, the first training-free spatial action grounding method based on pretrained image- and video-language backbones. Namely, we adapt the self-self attention formulation of GEM to spatial activity grounding. We observe that high-level semantic concepts, such as actions, usually emerge in the higher layers of the image- and video-language models. We, therefore, propose a layer weighting in the self-attention path to prioritize higher layers. Additionally, we introduce a dynamic weighting method to automatically tune layer weights to capture each layer's relevance to a specific prompt. Finally, we introduce a prompt decomposition, processing action, verb, and object prompts separately, resulting in a better spatial localization of actions. We evaluate the proposed approach on three image- and video-language backbones, CLIP, OpenCLIP, and ViCLIP, and on four video grounding datasets, V-HICO, DALY, YouCook-Interactions, and GroundingYouTube, showing that the proposed training-free approach is able to outperform current trained state-of-the-art approaches for spatial video grounding.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Vogel_VideoGEM_Training-free_Action_Grounding_in_Videos_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Vogel_VideoGEM_Training-free_Action_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Vogel_2025_CVPR,
    author    = {Vogel, Felix and Bousselham, Walid and Kukleva, Anna and Shvetsova, Nina and Kuehne, Hilde},
    title     = {VideoGEM: Training-free Action Grounding in Videos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3374-3383}
}

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, SIC", "city": "Saarbruecken", "country": "Germany"}, {"affiliation": "MIT-IBM Watson AI Lab", "city": "Cambridge", "country": "USA"}]

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


title: FilmComposer: LLM-Driven Music Production for Silent Film Clips
authors: Zhifeng Xie, Qile He, Youjia Zhu, Qiwei He, Mengtian Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 13519-13528
affiliations: ["Shanghai University", "Shanghai Engineering Research Center of Motion Picture Special Effects"]
cities: ["Shanghai", "Shanghai"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Xie_FilmComposer_LLM-Driven_Music_Production_for_Silent_Film_Clips_CVPR_2025_paper.html

abstract:
In this work, we implement music production for silent film clips using LLM-driven method. Given the strong professional demands of film music production, we propose the FilmComposer, simulating the actual workflows of professional musicians. FilmComposer is the first to combine large generative models with a multi-agent approach, leveraging the advantages of both waveform music and symbolic music generation. Additionally, FilmComposer is the first to focus on the three core elements of music production for film--audio quality, musicality, and musical development--and introduces various controls, such as rhythm, semantics, and visuals, to enhance these key aspects. Specifically, FilmComposer consists of the visual processing module, rhythm-controllable MusicGen, and multi-agent assessment, arrangement and mix. In addition, our framework can seamlessly integrate into the actual music production pipeline and allows user intervention in every step, providing strong interactivity and a high degree of creative freedom. Furthermore, we propose MusicPro-7k which includes 7,418 film clips, music, description, rhythm spots and main melody, considering the lack of a professional and high-quality film music dataset. Finally, both the standard metrics and the new specialized metrics we propose demonstrate that the music generated by our model achieves state-of-the-art performance in terms of quality, consistency with video, diversity, musicality, and musical development. Project page: https://apple-jun.github.io/FilmComposer.github.io/

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Xie_FilmComposer_LLM-Driven_Music_Production_for_Silent_Film_Clips_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Xie_FilmComposer_LLM-Driven_Music_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Xie_2025_CVPR,
    author    = {Xie, Zhifeng and He, Qile and Zhu, Youjia and He, Qiwei and Li, Mengtian},
    title     = {FilmComposer: LLM-Driven Music Production for Silent Film Clips},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {13519-13528}
}

raw_affiliation:
[{"affiliation": "Shanghai University", "city": "Shanghai", "country": "China"}, {"affiliation": "Shanghai Engineering Research Center of Motion Picture Special Effects", "city": "Shanghai", "country": "China"}]


title: Ground-V: Teaching VLMs to Ground Complex Instructions in Pixels
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24635-24645
affiliations: ["University of Edinburgh", "AWS AI Labs"]
cities: ["Edinburgh", "Seattle"]
countries: ["UK", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zong_Ground-V_Teaching_VLMs_to_Ground_Complex_Instructions_in_Pixels_CVPR_2025_paper.html

authors:
Yongshuo Zong, Qin Zhang, Dongsheng An, Zhihua Li, Xiang Xu, Linghan Xu, Zhuowen Tu, Yifan Xing, Onkar Dabeer

abstract:
This work presents a simple yet effective workflow for automatically scaling instruction-following data to elicit pixel-level grounding capabilities of VLMs under complex instructions. In particular, we address five critical real-world challenges in text-instruction-based grounding: hallucinated references, multi-object scenarios, reasoning, multi-granularity, and part-level references. By leveraging knowledge distillation from a pre-trained teacher model, our approach generates high-quality instruction-response pairs linked to existing pixel-level annotations, minimizing the need for costly human annotation. The resulting dataset, Ground-V, captures rich object localization knowledge and nuanced pixel-level referring expressions. Experiment results show that models trained on Ground-V exhibit substantial improvements across diverse grounding tasks. Specifically, incorporating \dataset during training directly achieve an average accuracy boost of 4.4% for LISA and a 7.9% for PSALM across six benchmarks on the gIoU metric. It also sets new state-of-the-art results on standard benchmarks such as RefCOCO/+/g. Notably, on gRefCOCO, we achieve an N-Acc of 83.3%, exceeding the previous state-of-the-art by more than 20%.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zong_Ground-V_Teaching_VLMs_to_Ground_Complex_Instructions_in_Pixels_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zong_Ground-V_Teaching_VLMs_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zong_2025_CVPR,
    author    = {Zong, Yongshuo and Zhang, Qin and An, Dongsheng and Li, Zhihua and Xu, Xiang and Xu, Linghan and Tu, Zhuowen and Xing, Yifan and Dabeer, Onkar},
    title     = {Ground-V: Teaching VLMs to Ground Complex Instructions in Pixels},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24635-24645}
}

raw_affiliation:
[{"affiliation": "University of Edinburgh", "city": "Edinburgh", "country": "UK"}, {"affiliation": "AWS AI Labs", "city": "Seattle", "country": "USA"}]


title: Structure-from-Motion with a Non-Parametric Camera Model
authors: Yihan Wang, Linfei Pan, Marc Pollefeys, Viktor Larsson
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1040-1049
affiliations: ["EPFL", "ETH Zurich", "Microsoft Spatial AI Lab", "Lund University"]
cities: ["Lausanne", "Zurich", "Redmond", "Lund"]
countries: ["Switzerland", "Switzerland", "USA", "Sweden"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Structure-from-Motion_with_a_Non-Parametric_Camera_Model_CVPR_2025_paper.html

abstract:
In this paper, we present a new generic Structure-from-Motion pipeline, GenSfM, that uses a non-parametric camera projection model. The model is self-calibrated during the reconstruction process and can fit a wide variety of cameras, ranging from simple low-distortion pinhole cameras to more extreme optical systems such as fisheye or catadioptric cameras. The key component in our framework is an adaptive calibration procedure that can estimate partial calibrations, only modeling regions of the image where sufficient constraints are available. In experiments, we show that our method achieves comparable accuracy to traditional Structure-from-Motion pipelines in easy scenarios, and outperforms them in cases where they are unable to self-calibrate their parametric models. Code is at https://github.com/Ivonne320/GenSfM.git

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Structure-from-Motion_with_a_Non-Parametric_Camera_Model_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Structure-from-Motion_with_a_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yihan and Pan, Linfei and Pollefeys, Marc and Larsson, Viktor},
    title     = {Structure-from-Motion with a Non-Parametric Camera Model},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1040-1049}
}

raw_affiliation:
[{"affiliation": "EPFL", "city": "Lausanne", "country": "Switzerland"}, {"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}, {"affiliation": "Microsoft Spatial AI Lab", "city": "Redmond", "country": "USA"}, {"affiliation": "Lund University", "city": "Lund", "country": "Sweden"}]


title: EventPSR: Surface Normal and Reflectance Estimation from Photometric Stereo Using an Event Camera
authors: Bohan Yu, Jin Han, Boxin Shi, Imari Sato
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11427-11436
cities: ["Beijing", "Beijing", "Tokyo", "Tokyo"]
countries: ["China", "China", "Japan", "Japan"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yu_EventPSR_Surface_Normal_and_Reflectance_Estimation_from_Photometric_Stereo_Using_CVPR_2025_paper.html

abstract:
Simultaneously acquisition of the surface normal and reflectance parameters is a crucial but challenging technique in the field of computer vision and graphics. It requires capturing multiple high dynamic range (HDR) images in existing methods using frame-based cameras. In this paper, we propose EventPSR, the first work to recover surface normal and reflectance parameters (e.g., metallic and roughness) simultaneously using an event camera. Compared with the existing methods based on photometric stereo or neural radiance fields, EventPSR is a robust and efficient approach that works consistently with different materials. Thanks to the extremely high temporal resolution and high dynamic range coverage of event cameras, EventPSR can recover accurate surface normal and reflectance of objects with various materials in 10 seconds. Extensive experiments on both synthetic data and real objects show that compared with existing methods using more than 100 HDR images, EventPSR recovers comparable surface normal and reflectance parameters with only about 30% of the data rate.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yu_EventPSR_Surface_Normal_and_Reflectance_Estimation_from_Photometric_Stereo_Using_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yu_EventPSR_Surface_Normal_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Yu_2025_CVPR,
    author    = {Yu, Bohan and Han, Jin and Shi, Boxin and Sato, Imari},
    title     = {EventPSR: Surface Normal and Reflectance Estimation from Photometric Stereo Using an Event Camera},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11427-11436}
}

raw_affiliation:
[{"affiliation": "State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "National Engineering Research Center of Visual Technology, School of Computer Science, Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "The University of Tokyo", "city": "Tokyo", "country": "Japan"}, {"affiliation": "National Institute of Informatics", "city": "Tokyo", "country": "Japan"}]

affiliations:
["State Key Laboratory for Multimedia Information Processing, School of Computer Science, Peking University", "National Engineering Research Center of Visual Technology, School of Computer Science, Peking University", "The University of Tokyo", "National Institute of Informatics"]


title: LAL: Enhancing 3D Human Motion Prediction with Latency-aware Auxiliary Learning
authors: Xiaoning Sun, Dong Wei, Huaijiang Sun, Shengxiang Hu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7105-7114
affiliations: ["Nanjing University of Science and Technology"]
cities: ["Nanjing"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Sun_LAL_Enhancing_3D_Human_Motion_Prediction_with_Latency-aware_Auxiliary_Learning_CVPR_2025_paper.html

abstract:
Making accurate prediction of human motions based on the historical observation is a crucial technology for robots to collaborate with humans. Existing human motion prediction methods are all built under an ideal assumption that robots can instantaneously react, which ignores the time delay introduced during data processing & analysis and future reaction planning -- jointly known as "response latency". Consequently, the predictions made within this latency period become meaningless for practical use, as part of the time has passed and the corresponding real motions have already occurred before robot deliver its reaction. In this paper, we argue that the seemingly meaningless prediction period, however, can be leveraged to enhance prediction accuracy significantly. We propose LAL, a Latency-aware Auxiliary Learning framework, which shifts the existing "reaction instantaneous" convention into a new motion prediction paradigm with both latency compatibility and utility. The framework consists of two branches handling different tasks: the primary branch learns to directly predict the valid target (excluding the beginning latency period) based on observation; while the auxiliary branch learns the same target, but based on the reformed observation with additional latency data incorporated. A direct and effective way of auxiliary feature sharing is forced by our tailored consistency loss, to gradually integrate auxiliary latency insights into the primary prediction branch. Estimated feature statistics-based alignment method is presented as optional step for primary branch refinement. Experiments show that LAL achieves significant improvement on prediction accuracy, without additional time consumption during testing.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Sun_LAL_Enhancing_3D_Human_Motion_Prediction_with_Latency-aware_Auxiliary_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Sun_LAL_Enhancing_3D_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Sun_2025_CVPR,
    author    = {Sun, Xiaoning and Wei, Dong and Sun, Huaijiang and Hu, Shengxiang},
    title     = {LAL: Enhancing 3D Human Motion Prediction with Latency-aware Auxiliary Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7105-7114}
}

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


title: CASP: Consistency-aware Audio-induced Saliency Prediction Model for Omnidirectional Video
authors: Zhaolin Wan, Han Qin, Zhiyang Li, Xiaopeng Fan, Wangmeng Zuo, Debin Zhao
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12605-12614
affiliations: ["Harbin Institute of Technology", "Dalian Maritime University"]
cities: ["Harbin", "Dalian"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wan_CASP_Consistency-aware_Audio-induced_Saliency_Prediction_Model_for_Omnidirectional_Video_CVPR_2025_paper.html

abstract:
Omnidirectional videos (ODVs) present distinct challenges for accurate audio-visual saliency prediction due to their immersive nature, which combines spatial audio with panoramic visuals to enhance the user experience. While auditory cues are crucial for guiding visual attention across the panoramic scene, the interaction between audio and visual stimuli in ODVs remains underexplored. Existing models primarily focus on spatiotemporal visual cues and treat audio signals separately from their spatial and temporal contexts, often leading to misalignments between audio and visual content and undermining temporal consistency across frames. To bridge these gaps, we propose a novel audio-induced saliency prediction model for ODVs that holistically integrates audio and visual inputs through a multi-modal encoder, an audio-visual interaction module, and an audio-visual transformer. Unlike conventional methods that isolate audio cue locations and attributes, our model employs a query-based framework, where learnable audio queries capture comprehensive audio-visual dependencies, thus enhancing saliency prediction by dynamically aligning with audio cues. Besides, we introduce a novel consistency loss to enforce temporal coherence in saliency regions across frames. Extensive experiments demonstrate that our model outperforms state-of-the-art methods in predicting audio-visual salient regions in ODVs, establishing its robustness and superior performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wan_CASP_Consistency-aware_Audio-induced_Saliency_Prediction_Model_for_Omnidirectional_Video_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Wan_2025_CVPR,
    author    = {Wan, Zhaolin and Qin, Han and Li, Zhiyang and Fan, Xiaopeng and Zuo, Wangmeng and Zhao, Debin},
    title     = {CASP: Consistency-aware Audio-induced Saliency Prediction Model for Omnidirectional Video},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12605-12614}
}

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


title: TreeMeshGPT: Artistic Mesh Generation with Autoregressive Tree Sequencing
authors: Stefan Lionar, Jiabin Liang, Gim Hee Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26608-26617
affiliations: ["Sea AI Lab", "Garena", "National University of Singapore"]
cities: ["Singapore", "Singapore", "Singapore"]
countries: ["Singapore", "Singapore", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lionar_TreeMeshGPT_Artistic_Mesh_Generation_with_Autoregressive_Tree_Sequencing_CVPR_2025_paper.html

abstract:
We introduce TreeMeshGPT, an autoregressive Transformer designed to generate high-quality artistic meshes aligned with input point clouds. Instead of the conventional next-token prediction in autoregressive Transformer, we propose a novel Autoregressive Tree Sequencing where the next input token is retrieved from a dynamically growing tree structure that is built upon the triangle adjacency of faces within the mesh. Our sequencing enables the mesh to extend locally from the last generated triangular face at each step, and therefore reduces training difficulty and improves mesh quality. Our approach represents each triangular face with two tokens, achieving a compression rate of approximately 22% compared to the naive face tokenization. This efficient tokenization enables our model to generate highly detailed artistic meshes with strong point cloud conditioning, surpassing previous methods in both capacity and fidelity. Furthermore, our method generates mesh with strong normal orientation constraints, minimizing flipped normals commonly encountered in previous methods. Our experiments show that TreeMeshGPT enhances the mesh generation quality with refined details and normal orientation consistency.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lionar_TreeMeshGPT_Artistic_Mesh_Generation_with_Autoregressive_Tree_Sequencing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lionar_TreeMeshGPT_Artistic_Mesh_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lionar_2025_CVPR,
    author    = {Lionar, Stefan and Liang, Jiabin and Lee, Gim Hee},
    title     = {TreeMeshGPT: Artistic Mesh Generation with Autoregressive Tree Sequencing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26608-26617}
}

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


authors: Jaeguk Kim, Jaewoo Park, Keuntek Lee, Nam Ik Cho
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6447-6456
cities: ["Seoul", "Seoul"]
countries: ["Korea", "Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_RefPose_Leveraging_Reference_Geometric_Correspondences_for_Accurate_6D_Pose_Estimation_CVPR_2025_paper.html

title:
RefPose: Leveraging Reference Geometric Correspondences for Accurate 6D Pose Estimation of Unseen Objects

abstract:
Estimating the 6D pose of unseen objects from monocular RGB images remains a challenging problem, especially due to the lack of prior object-specific knowledge. To tackle this issue, we propose RefPose, an innovative approach to object pose estimation that leverages a reference image and geometric correspondence as guidance. RefPose first predicts an initial pose by using object templates to render the reference image and establish the geometric correspondence needed for the refinement stage. During the refinement stage, RefPose estimates the geometric correspondence of the query based on the generated references and iteratively refines the pose through a render-and-compare approach. To enhance this estimation, we introduce a correlation volume-guided attention mechanism that effectively captures correlations between the query and reference images. Unlike traditional methods that depend on pre-defined object models, RefPose dynamically adapts to new object shapes by leveraging a reference image and geometric correspondence. This results in robust performance across previously unseen objects. Extensive evaluation on the BOP benchmark datasets shows that RefPose achieves state-of-the-art results while maintaining a competitive runtime.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_RefPose_Leveraging_Reference_Geometric_Correspondences_for_Accurate_6D_Pose_Estimation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_RefPose_Leveraging_Reference_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Jaeguk and Park, Jaewoo and Lee, Keuntek and Cho, Nam Ik},
    title     = {RefPose: Leveraging Reference Geometric Correspondences for Accurate 6D Pose Estimation of Unseen Objects},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6447-6456}
}

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

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


title: Relation3D : Enhancing Relation Modeling for Point Cloud Instance Segmentation
authors: Jiahao Lu, Jiacheng Deng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 8889-8899
affiliations: ["University of Science and Technology of China", "University of Science and Technology of China"]
cities: ["Hefei", "Hefei"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lu_Relation3D__Enhancing_Relation_Modeling_for_Point_Cloud_Instance_Segmentation_CVPR_2025_paper.html

abstract:
3D instance segmentation aims to predict a set of object instances in a scene, representing them as binary foreground masks with corresponding semantic labels. Currently, transformer-based methods are gaining increasing attention due to their elegant pipelines and superior predictions. However, these methods primarily focus on modeling the external relationships between scene features and query features through mask attention. They lack effective modeling of the internal relationships among scene features as well as between query features.In light of these disadvantages, we propose Relation3D: Enhancing Relation Modeling for Point Cloud Instance Segmentation. Specifically, we introduce an adaptive superpoint aggregation module and a contrastive learning-guided superpoint refinement module to better represent superpoint features (scene features) and leverage contrastive learning to guide the updates of these features.Furthermore, our relation-aware self-attention mechanism enhances the capabilities of modeling relationships between queries by incorporating positional and geometric relationships into the self-attention mechanism.Extensive experiments on the ScanNetV2, ScanNet++, ScanNet200 and S3DIS datasets demonstrate the superior performance of Relation3D.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lu_Relation3D__Enhancing_Relation_Modeling_for_Point_Cloud_Instance_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lu_Relation3D__Enhancing_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lu_2025_CVPR,
    author    = {Lu, Jiahao and Deng, Jiacheng},
    title     = {Relation3D : Enhancing Relation Modeling for Point Cloud Instance Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {8889-8899}
}

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


title: Shape My Moves: Text-Driven Shape-Aware Synthesis of Human Motions
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 1917-1928
affiliations: ["University of Maryland", "Adobe Research"]
cities: ["College Park", "Unknown"]
countries: ["USA", "Unknown"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liao_Shape_My_Moves_Text-Driven_Shape-Aware_Synthesis_of_Human_Motions_CVPR_2025_paper.html

authors:
Ting-Hsuan Liao, Yi Zhou, Yu Shen, Chun-Hao Paul Huang, Saayan Mitra, Jia-Bin Huang, Uttaran Bhattacharya

abstract:
We explore how body shapes influence human motion synthesis, an aspect often overlooked in existing text-to-motion generation methods due to the ease of learning a homogenized, canonical body shape. However, this homogenization can distort the natural correlations between different body shapes and their motion dynamics. Our method addresses this gap by generating body-shape-aware human motions from natural language prompts. We utilize a finite scalar quantization-based variational autoencoder (FSQ-VAE) to quantize motion into discrete tokens and then leverage continuous body shape information to de-quantize these tokens back into continuous, detailed motion. Additionally, we harness the capabilities of a pretrained language model to predict both continuous shape parameters and motion tokens, facilitating the synthesis of text-aligned motions and decoding them into shape-aware motions. We evaluate our method quantitatively and qualitatively, and also conduct a comprehensive perceptual study to demonstrate its efficacy in generating shape-aware motions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liao_Shape_My_Moves_Text-Driven_Shape-Aware_Synthesis_of_Human_Motions_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liao_Shape_My_Moves_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liao_2025_CVPR,
    author    = {Liao, Ting-Hsuan and Zhou, Yi and Shen, Yu and Huang, Chun-Hao Paul and Mitra, Saayan and Huang, Jia-Bin and Bhattacharya, Uttaran},
    title     = {Shape My Moves: Text-Driven Shape-Aware Synthesis of Human Motions},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {1917-1928}
}

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


title: Generating 3D-Consistent Videos from Unposed Internet Photos
authors: Gene Chou, Kai Zhang, Sai Bi, Hao Tan, Zexiang Xu, Fujun Luan, Bharath Hariharan, Noah Snavely
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 27934-27945
affiliations: ["Cornell University", "Adobe Research"]
cities: ["Ithaca", "San Jose"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chou_Generating_3D-Consistent_Videos_from_Unposed_Internet_Photos_CVPR_2025_paper.html

abstract:
We address the problem of generating videos from unposed internet photos. A handful of input images serve as keyframes, and our model interpolates between them to simulate a path moving between the cameras. Given random images, a model's ability to capture underlying geometry, recognize scene identity, and relate frames in terms of camera position and orientation reflects a fundamental understanding of 3D structure and scene layout. However, existing video models such as Luma Dream Machine fail at this task. We design a self-supervised method that takes advantage of the consistency of videos and variability of multiview internet photos to train a scalable, 3D-aware video model without any 3D annotations such as camera parameters. We validate that our method outperforms commercial models in terms of geometric and appearance consistency. We also show our model benefits applications that enable camera control, such as 3D Gaussian Splatting. Our results suggest that we can scale up scene-level 3D learning using only 2D data such as videos and multiview internet photos.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chou_Generating_3D-Consistent_Videos_from_Unposed_Internet_Photos_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Chou_2025_CVPR,
    author    = {Chou, Gene and Zhang, Kai and Bi, Sai and Tan, Hao and Xu, Zexiang and Luan, Fujun and Hariharan, Bharath and Snavely, Noah},
    title     = {Generating 3D-Consistent Videos from Unposed Internet Photos},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {27934-27945}
}

raw_affiliation:
[{"affiliation": "Cornell University", "city": "Ithaca", "country": "USA"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}]


authors: Bo Wang, Dingwei Tan, Yen-Ling Kuo, Zhaowei Sun, Jeremy M. Wolfe, Tat-Jen Cham, Mengmi Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 14810-14823
cities: ["Singapore", "Singapore", "Harbin", "Beijing", "Charlottesville", "Boston", "Boston"]
countries: ["Singapore", "Singapore", "China", "China", "USA", "USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_Gazing_at_Rewards_Eye_Movements_as_a_Lens_into_Human_CVPR_2025_paper.html

title:
Gazing at Rewards: Eye Movements as a Lens into Human and AI Decision-Making in Hybrid Visual Foraging

abstract:
Imagine searching a collection of coins for quarters (0.25), dimes (0.10), nickels (0.05), and pennies (0.01)--a hybrid foraging task where observers search for multiple instances of multiple target types. In such tasks, how do target values and their prevalence influence foraging and eye movement behaviors (e.g., should you prioritize rare quarters or common nickels)? To explore this, we conducted human psychophysics experiments, revealing that humans are proficient reward foragers. Their eye fixations are drawn to regions with higher average rewards, fixation durations are longer on more valuable targets, and their cumulative rewards exceed chance, approaching the upper bound of optimal foragers. To probe these decision making process of humans, we developed a transformer-based Visual Forager (VF) model trained via reinforcement learning. Our VF model takes a series of targets, their corresponding values, and the search image as inputs, processes the images using foveated vision, and produces a sequence of eye movements along with decisions on whether to click on each fixated item. Our model outperforms all baselines, achieves cumulative rewards comparable to those of humans, and closely mirrors human foraging behavior in eye movements and click biases. Furthermore, stress tests on out-of-distribution tasks with novel targets, unseen values, and varying set sizes demonstrate the VF model's effective generalization. Our work offers valuable insights into the relationship between eye movements and decision-making, with our model serving as a powerful tool for further exploration of this connection. All data, code, and models are available at https://github.com/ZhangLab-DeepNeuroCogLab/visual-forager.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_Gazing_at_Rewards_Eye_Movements_as_a_Lens_into_Human_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_Gazing_at_Rewards_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Bo and Tan, Dingwei and Kuo, Yen-Ling and Sun, Zhaowei and Wolfe, Jeremy M. and Cham, Tat-Jen and Zhang, Mengmi},
    title     = {Gazing at Rewards: Eye Movements as a Lens into Human and AI Decision-Making in Hybrid Visual Foraging},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {14810-14823}
}

raw_affiliation:
[{"affiliation": "College of Computing and Data Science, Nanyang Technological University, Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Deep NeuroCognition Lab, I2R and CFAR, Agency for Science, Technology and Research, Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Harbin Institute of Technology, Harbin, China", "city": "Harbin", "country": "China"}, {"affiliation": "Beijing Institute of Technology, Beijing, China", "city": "Beijing", "country": "China"}, {"affiliation": "University of Virginia, USA", "city": "Charlottesville", "country": "USA"}, {"affiliation": "Brigham and Women\u2019s Hospital, USA", "city": "Boston", "country": "USA"}, {"affiliation": "Harvard Medical School, USA", "city": "Boston", "country": "USA"}]

affiliations:
["College of Computing and Data Science, Nanyang Technological University, Singapore", "Deep NeuroCognition Lab, I2R and CFAR, Agency for Science, Technology and Research, Singapore", "Harbin Institute of Technology, Harbin, China", "Beijing Institute of Technology, Beijing, China", "University of Virginia, USA", "Brigham and Women\u2019s Hospital, USA", "Harvard Medical School, USA"]


title: FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image Classification
authors: Zhengrui Guo, Conghao Xiong, Jiabo Ma, Qichen Sun, Lishuang Feng, Jinzhuo Wang, Hao Chen
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15590-15600
affiliations: ["HKUST", "BICI", "CUHK", "Peking University"]
cities: ["Hong Kong", "Beijing", "Hong Kong", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_FOCUS_Knowledge-enhanced_Adaptive_Visual_Compression_for_Few-shot_Whole_Slide_Image_CVPR_2025_paper.html

abstract:
Few-shot learning presents a critical solution for cancer diagnosis in computational pathology (CPath), addressing fundamental limitations in data availability, particularly the scarcity of expert annotations and patient privacy constraints. A key challenge in this paradigm stems from the inherent disparity between the limited training set of whole slide images (WSIs) and the enormous number of contained patches, where a significant portion of these patches lacks diagnostically relevant information, potentially diluting the model's ability to learn and focus on critical diagnostic features. While recent works attempt to address this by incorporating additional knowledge, several crucial gaps hinder further progress: (1) despite the emergence of powerful pathology foundation models (FMs), their potential remains largely untapped, with most approaches limiting their use to basic feature extraction; (2) current language guidance mechanisms attempt to align text prompts with vast numbers of WSI patches all at once, struggling to leverage rich pathological semantic information. To this end, we introduce the knowledge-enhanced adaptive visual compression framework, dubbed FOCUS, which uniquely combines pathology FMs with language prior knowledge to enable a focused analysis of diagnostically relevant regions by prioritizing discriminative WSI patches. Our approach implements a progressive three-stage compression strategy: we first leverage FMs for global visual redundancy elimination, and integrate compressed features with language prompts for semantic relevance assessment, then perform neighbor-aware visual token filtering while preserving spatial coherence. Extensive experiments on pathological datasets spanning breast, lung, and ovarian cancers demonstrate its superior performance in few-shot pathology diagnosis. Codes are available at https://github.com/dddavid4real/FOCUS.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_FOCUS_Knowledge-enhanced_Adaptive_Visual_Compression_for_Few-shot_Whole_Slide_Image_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Guo_FOCUS_Knowledge-enhanced_Adaptive_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Zhengrui and Xiong, Conghao and Ma, Jiabo and Sun, Qichen and Feng, Lishuang and Wang, Jinzhuo and Chen, Hao},
    title     = {FOCUS: Knowledge-enhanced Adaptive Visual Compression for Few-shot Whole Slide Image Classification},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15590-15600}
}

raw_affiliation:
[{"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}, {"affiliation": "BICI", "city": "Beijing", "country": "China"}, {"affiliation": "CUHK", "city": "Hong Kong", "country": "China"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}]


title: Beyond Human Perception: Understanding Multi-Object World from Monocular View
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3751-3760
cities: ["Xi'an", "Xi'an", "Melbourne", "Perth"]
countries: ["China", "China", "Australia", "Australia"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Guo_Beyond_Human_Perception_Understanding_Multi-Object_World_from_Monocular_View_CVPR_2025_paper.html

authors:
Keyu Guo, Yongle Huang, Shijie Sun, Xiangyu Song, Mingtao Feng, Zedong Liu, Huansheng Song, Tiantian Wang, Jianxin Li, Naveed Akhtar, Ajmal Saeed Mian

abstract:
Language and binocular vision play a crucial role in human understanding of the world. Advancements in artificial intelligence have also made it possible for machines to develop 3D perception capabilities essential for high-level scene understanding. However, only monocular cameras are often available in practice due to cost and space constraints. Enabling machines to achieve accurate 3D understanding from a monocular view is practical but presents significant challenges. We introduce MonoMulti-3DVG, a novel task aimed at achieving multi-object 3D Visual Grounding (3DVG) based on monocular RGB images, allowing machines to better understand and interact with the 3D world. To this end, we construct a large-scale benchmark dataset, MonoMulti3D-ROPE, and propose a model, CyclopsNet that integrates a State-Prompt Visual Encoder (SPVE) module with a Denoising Alignment Fusion (DAF) module to achieve robust multi-modal semantic alignment and fusion. This leads to more stable and robust multi-modal joint representations for downstream tasks. Experimental results show that our method significantly outperforms existing techniques on the MonoMulti3D-ROPE dataset. Our dataset and code are available at https://github.com/JasonHuang516/MonoMulti-3DVG

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Guo_Beyond_Human_Perception_Understanding_Multi-Object_World_from_Monocular_View_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Guo_2025_CVPR,
    author    = {Guo, Keyu and Huang, Yongle and Sun, Shijie and Song, Xiangyu and Feng, Mingtao and Liu, Zedong and Song, Huansheng and Wang, Tiantian and Li, Jianxin and Akhtar, Naveed and Mian, Ajmal Saeed},
    title     = {Beyond Human Perception: Understanding Multi-Object World from Monocular View},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3751-3760}
}

raw_affiliation:
[{"affiliation": "Chang\u2019an University", "city": "Xi'an", "country": "China"}, {"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "The University of Melbourne", "city": "Melbourne", "country": "Australia"}, {"affiliation": "University of Western Australia", "city": "Perth", "country": "Australia"}]

affiliations:
["Chang\u2019an University", "Xidian University", "The University of Melbourne", "University of Western Australia"]


title: GRAE-3DMOT: Geometry Relation-Aware Encoder for Online 3D Multi-Object Tracking
authors: Hyunseop Kim, Hyo-Jun Lee, Yonguk Lee, Jinu Lee, Hanul Kim, Yeong Jun Koh
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11697-11706
cities: ["Daejeon", "Chuncheon", "Seoul", "Seoul"]
countries: ["South Korea", "South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_GRAE-3DMOT_Geometry_Relation-Aware_Encoder_for_Online_3D_Multi-Object_Tracking_CVPR_2025_paper.html

abstract:
Recently, 3D multi-object tracking (MOT) has widely adopted the standard tracking-by-detection paradigm, which solves the association problem between detections and tracks. Many tracking-by-detection approaches establish constrained relationships between detections and tracks using a distance threshold to reduce confusion during association. However, this approach does not effectively and comprehensively utilize the information regarding objects due to the constraints of the distance threshold. In this paper, we propose GRAE-3DMOT, Geometry Relation-Aware Encoder 3D Multi-Object Tracking, which contains a geometric relation-aware encoder to produce informative features for association. The geometric relation-aware encoder consists of three components: a spatial relation-aware encoder, a spatiotemporal relation-aware encoder, and a distance-aware feature fusion layer. The spatial relation-aware encoder effectively aggregates detection features by comprehensively exploiting as many detections as possible. The spatiotemporal relation-aware encoder provides spatiotemporal relation-aware features by combing spatial and temporal relation features, where the spatiotemporal relation-aware features are transformed into association scores for MOT. The distance-aware feature fusion layer is integrated into both encoders to enhance the relation features of physically proximate objects. Experimental results demonstrate that the proposed GRAE-3DMOT outperforms the state-of-the-art on the nuScenes. Our approach achieves 73.7% and 70.2% AMOTA on the nuScenes validation and test sets using CenterPoint detections.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_GRAE-3DMOT_Geometry_Relation-Aware_Encoder_for_Online_3D_Multi-Object_Tracking_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_GRAE-3DMOT_Geometry_Relation-Aware_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Hyunseop and Lee, Hyo-Jun and Lee, Yonguk and Lee, Jinu and Kim, Hanul and Koh, Yeong Jun},
    title     = {GRAE-3DMOT: Geometry Relation-Aware Encoder for Online 3D Multi-Object Tracking},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11697-11706}
}

raw_affiliation:
[{"affiliation": "Chungnam National University", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "Kangwon National University", "city": "Chuncheon", "country": "South Korea"}, {"affiliation": "42dot Inc.", "city": "Seoul", "country": "South Korea"}, {"affiliation": "Seoul National University of Science and Technology", "city": "Seoul", "country": "South Korea"}]

affiliations:
["Chungnam National University", "Kangwon National University", "42dot Inc.", "Seoul National University of Science and Technology"]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15234-15244
affiliations: ["Microsoft1", "Lehigh University2"]
cities: ["Redmond", "Bethlehem"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Qu_Automatic_Joint_Structured_Pruning_and_Quantization_for_Efficient_Neural_Network_CVPR_2025_paper.html

title:
Automatic Joint Structured Pruning and Quantization for Efficient Neural Network Training and Compression

authors:
Xiaoyi Qu, David Aponte, Colby Banbury, Daniel P. Robinson, Tianyu Ding, Kazuhito Koishida, Ilya Zharkov, Tianyi Chen

abstract:
Structured pruning and quantization are fundamental techniques used to reduce the size of deep neural networks (DNNs) and typically are applied independently. Applying these techniques jointly via co-optimization has the potential to produce smaller, high-quality models. However, existing joint schemes are not widely used because of (1) engineering difficulties (complicated multi-stage processes), (2) black-box optimization (extensive hyperparameter tuning to control the overall compression), and (3) insufficient architecture generalization. To address these limitations, we present the framework GETA, which automatically and efficiently performs joint structured pruning and quantization-aware training on any DNN. GETA introduces three key innovations: (I) a quantization-aware dependency graph (QADG) that constructs a pruning search space for generic quantization-aware DNN, (II) a partially projected stochastic gradient method that guarantees layer-wise bit constraints are satisfied, and (III) a new joint learning strategy that incorporates interpretable relationships between pruning and quantization. We present numerical experiments on both convolutional neural networks and transformer architectures that show that our approach achieves competitive (often superior) performance compared to existing joint pruning and quantization methods. The source code is available at https://github.com/microsoft/GETA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Qu_Automatic_Joint_Structured_Pruning_and_Quantization_for_Efficient_Neural_Network_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Qu_Automatic_Joint_Structured_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Qu_2025_CVPR,
    author    = {Qu, Xiaoyi and Aponte, David and Banbury, Colby and Robinson, Daniel P. and Ding, Tianyu and Koishida, Kazuhito and Zharkov, Ilya and Chen, Tianyi},
    title     = {Automatic Joint Structured Pruning and Quantization for Efficient Neural Network Training and Compression},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15234-15244}
}

raw_affiliation:
[{"affiliation": "Microsoft1", "city": "Redmond", "country": "USA"}, {"affiliation": "Lehigh University2", "city": "Bethlehem", "country": "USA"}]


title: Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation
authors: Seokil Ham, Hee-Seon Kim, Sangmin Woo, Changick Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30106-30115
affiliations: ["Korea Advanced Institute of Science and Technology (KAIST)"]
cities: ["Daejeon"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Ham_Parameter_Efficient_Mamba_Tuning_via_Projector-targeted_Diagonal-centric_Linear_Transformation_CVPR_2025_paper.html

abstract:
Despite the growing interest in Mamba architecture as a potential replacement for Transformer architecture, parameter-efficient fine-tuning (PEFT) approaches for Mamba remain largely unexplored. In our study, we introduce two key insights-driven strategies for PEFT in Mamba architecture: (1) While state-space models (SSMs) have been regarded as the cornerstone of Mamba architecture, then expected to play a primary role in transfer learning, our findings reveal that Projectors---not SSMs---are the predominant contributors to transfer learning, and (2) Based on our observation, we propose a novel PEFT method specialized to Mamba architecture: Projector-targeted Diagonal-centric Linear Transformation (ProDiaL). ProDiaL focuses on optimizing only diagonal-centric linear transformation matrices, without directly fine-tuning the pretrained Projector weights. This targeted approach allows efficient task adaptation, utilizing less than 1% of the total parameters, and exhibits strong performance across both vision and language Mamba models, highlighting its versatility and effectiveness.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Ham_Parameter_Efficient_Mamba_Tuning_via_Projector-targeted_Diagonal-centric_Linear_Transformation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Ham_Parameter_Efficient_Mamba_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Ham_2025_CVPR,
    author    = {Ham, Seokil and Kim, Hee-Seon and Woo, Sangmin and Kim, Changick},
    title     = {Parameter Efficient Mamba Tuning via Projector-targeted Diagonal-centric Linear Transformation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30106-30115}
}

raw_affiliation:
[{"affiliation": "Korea Advanced Institute of Science and Technology (KAIST)", "city": "Daejeon", "country": "South Korea"}]


title: ViUniT: Visual Unit Tests for More Robust Visual Programming
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24646-24656
affiliations: ["Salesforce AI Research", "University of Pennsylvania"]
cities: ["Palo Alto", "Philadelphia"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Panagopoulou_ViUniT_Visual_Unit_Tests_for_More_Robust_Visual_Programming_CVPR_2025_paper.html

authors:
Artemis Panagopoulou, Honglu Zhou, Silvio Savarese, Caiming Xiong, Chris Callison-Burch, Mark Yatskar, Juan Carlos Niebles

abstract:
Programming based approaches to reasoning tasks have substantially expanded the types of questions models can answer about visual scenes.Yet on benchmark visual reasoning data, when answering correctly, such models produce incorrect programs 33% of the time. These models are often right for the wrong reasons and risk unexpected failures on new data. Unit tests play a foundational role in ensuring code correctness and could be used to repair such failures. We propose Visual Unit Testing (ViUniT), a framework to improve the reliability of visual programs by automatically generating unit tests. In our framework, a unit test is represented as a novel image and answer meant to verify logical correctness of a program produced for a given query. Our method leverages a language model to create unit tests in the form of image descriptions and expected answers and image synthesis to produce corresponding images. We conduct a comprehensive analysis of what constitutes an effective visual unit test suite, exploring unit test generation, sampling strategies, image generation methods, and varying the number of programs and unit tests.  Additionally, we introduce four applications of visual unit tests: best program selection, answer refusal, re-prompting, and unsupervised reward formulations for reinforcement learning.  Experiments with two models across three datasets in visual question answering and image-text matching demonstrate that ViUniT improves model performance by 11.4%. Notably, it enables 7B open-source models to outperform gpt-4o-mini by an average of 7.7% and reduces the occurrence of programs that are correct for the wrong reasons by 40%.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Panagopoulou_ViUniT_Visual_Unit_Tests_for_More_Robust_Visual_Programming_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Panagopoulou_ViUniT_Visual_Unit_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Panagopoulou_2025_CVPR,
    author    = {Panagopoulou, Artemis and Zhou, Honglu and Savarese, Silvio and Xiong, Caiming and Callison-Burch, Chris and Yatskar, Mark and Niebles, Juan Carlos},
    title     = {ViUniT: Visual Unit Tests for More Robust Visual Programming},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24646-24656}
}

raw_affiliation:
[{"affiliation": "Salesforce AI Research", "city": "Palo Alto", "country": "USA"}, {"affiliation": "University of Pennsylvania", "city": "Philadelphia", "country": "USA"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 505-517
affiliations: ["Meta Reality Labs", "University of Maryland, College Park"]
cities: ["Menlo Park", "College Park"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_LIRM_Large_Inverse_Rendering_Model_for_Progressive_Reconstruction_of_Shape_CVPR_2025_paper.html

title:
LIRM: Large Inverse Rendering Model for Progressive Reconstruction of Shape, Materials and View-dependent Radiance Fields

authors:
Zhengqin Li, Dilin Wang, Ka Chen, Zhaoyang Lv, Thu Nguyen-Phuoc, Milim Lee, Jia-Bin Huang, Lei Xiao, Yufeng Zhu, Carl S. Marshall, Yuheng Ren, Richard Newcombe, Zhao Dong

abstract:
We present Large Inverse Rendering Model (LIRM), a transformer architecture that jointly reconstructs high-quality shape, materials, and radiance fields with view-dependent effects in less than a second. Our model builds upon the recent Large Reconstruction Models (LRMs) that achieve state-of-the-art sparse-view reconstruction quality. However, existing LRMs struggle to reconstruct unseen parts accurately and cannot recover glossy appearance or generate relightable 3D contents that can be consumed by standard Graphics engines. To address these limitations, we make three key technical contributions to build a more practical multi-view 3D reconstruction framework. First, we introduce an update model that allows us to progressively add more input views to improve our reconstruction. Second, we propose a hexa-plane neural SDF representation to better recover detailed textures, geometry and material parameters. Third, we develop a novel neural directional-embedding mechanism to handle view-dependent effects. Trained on a large-scale shape and material dataset with a tailored coarse-to-fine training scheme, our model achieves compelling results. It compared favorably to optimization-based dense-view inverse rendering methods in terms of geometry and relighting accuracy, while requiring only a fraction of the inference time.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_LIRM_Large_Inverse_Rendering_Model_for_Progressive_Reconstruction_of_Shape_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_LIRM_Large_Inverse_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Zhengqin and Wang, Dilin and Chen, Ka and Lv, Zhaoyang and Nguyen-Phuoc, Thu and Lee, Milim and Huang, Jia-Bin and Xiao, Lei and Zhu, Yufeng and Marshall, Carl S. and Ren, Yuheng and Newcombe, Richard and Dong, Zhao},
    title     = {LIRM: Large Inverse Rendering Model for Progressive Reconstruction of Shape, Materials and View-dependent Radiance Fields},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {505-517}
}

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


title: DualTalk: Dual-Speaker Interaction for 3D Talking Head Conversations
authors: Ziqiao Peng, Yanbo Fan, Haoyu Wu, Xuan Wang, Hongyan Liu, Jun He, Zhaoxin Fan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 21055-21064
cities: ["Beijing", "Hangzhou", "Beijing", "Beijing"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Peng_DualTalk_Dual-Speaker_Interaction_for_3D_Talking_Head_Conversations_CVPR_2025_paper.html

abstract:
In face-to-face conversations, individuals need to switch between speaking and listening roles seamlessly. Existing 3D talking head generation models focus solely on speaking or listening, neglecting the natural dynamics of interactive conversation, which leads to unnatural interactions and awkward transitions. To address this issue, we propose a new task--multi-round dual-speaker interaction for 3D talking head generation--which requires models to handle and generate both speaking and listening behaviors in continuous conversation. To solve this task, we introduce DualTalk, a novel unified framework that integrates the dynamic behaviors of speakers and listeners to simulate realistic and coherent dialogue interactions. This framework not only synthesizes lifelike talking heads when speaking but also generates continuous and vivid non-verbal feedback when listening, effectively capturing the interplay between the roles. We also create a new dataset featuring 50 hours of multi-round conversations with over 1,000 characters, where participants continuously switch between speaking and listening roles. Extensive experiments demonstrate that our method significantly enhances the naturalness and expressiveness of 3D talking heads in dual-speaker conversations. We recommend watching the supplementary video: https://ziqiaopeng.github.io/dualtalk

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Peng_DualTalk_Dual-Speaker_Interaction_for_3D_Talking_Head_Conversations_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Peng_DualTalk_Dual-Speaker_Interaction_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Peng_2025_CVPR,
    author    = {Peng, Ziqiao and Fan, Yanbo and Wu, Haoyu and Wang, Xuan and Liu, Hongyan and He, Jun and Fan, Zhaoxin},
    title     = {DualTalk: Dual-Speaker Interaction for 3D Talking Head Conversations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {21055-21064}
}

raw_affiliation:
[{"affiliation": "Renmin University of China", "city": "Beijing", "country": "China"}, {"affiliation": "Ant Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing", "city": "Beijing", "country": "China"}]

affiliations:
["Renmin University of China", "Ant Group", "Tsinghua University", "Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing"]


title: MAR-3D: Progressive Masked Auto-regressor for High-Resolution 3D Generation
authors: Jinnan Chen, Lingting Zhu, Zeyu Hu, Shengju Qian, Yugang Chen, Xin Wang, Gim Hee Lee
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 11083-11092
affiliations: ["National University of Singapore", "The University of Hong Kong", "LIGHTSPEED"]
cities: ["Singapore", "Hong Kong", "Shenzhen"]
countries: ["Singapore", "Hong Kong", "China"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_MAR-3D_Progressive_Masked_Auto-regressor_for_High-Resolution_3D_Generation_CVPR_2025_paper.html

abstract:
Recent advances in auto-regressive transformers have revolutionized generative modeling across different domains, from language processing to visual generation, demonstrating remarkable capabilities. However, applying these advances to 3D generation presents three key challenges: the unordered nature of 3D data conflicts with sequential next-token prediction paradigm, conventional vector quantization approaches incur substantial compression loss when applied to 3D meshes, and the lack of efficient scaling strategies for higher resolution latent prediction. To address these challenges, we introduce MAR-3D, which integrates a pyramid variational autoencoder with a cascaded masked auto-regressive transformer (Cascaded MAR) for progressive latent upscaling in the continuous space. Our architecture employs random masking during training and auto-regressive denoising in random order during inference, naturally accommodating the unordered property of 3D latent tokens. Additionally, we propose a cascaded training strategy with condition augmentation that enables efficiently up-scale the latent token resolution with fast convergence. Extensive experiments demonstrate that MAR-3D not only achieves superior performance and generalization capabilities compared to existing methods but also exhibits enhanced scaling capabilities compared to joint distribution modeling approaches (e.g., diffusion transformers).

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_MAR-3D_Progressive_Masked_Auto-regressor_for_High-Resolution_3D_Generation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_MAR-3D_Progressive_Masked_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Jinnan and Zhu, Lingting and Hu, Zeyu and Qian, Shengju and Chen, Yugang and Wang, Xin and Lee, Gim Hee},
    title     = {MAR-3D: Progressive Masked Auto-regressor for High-Resolution 3D Generation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {11083-11092}
}

raw_affiliation:
[{"affiliation": "National University of Singapore", "city": "Singapore", "country": "Singapore"}, {"affiliation": "The University of Hong Kong", "city": "Hong Kong", "country": "Hong Kong"}, {"affiliation": "LIGHTSPEED", "city": "Shenzhen", "country": "China"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30839-30849
cities: ["Xi'an", "Beijing", "Xi'an", "Xi'an", "Wuhan", "Nanning"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Hu_beta-FFT_Nonlinear_Interpolation_and_Differentiated_Training_Strategies_for_Semi-Supervised_Medical_CVPR_2025_paper.html

title:
beta-FFT: Nonlinear Interpolation and Differentiated Training Strategies for Semi-Supervised Medical Image Segmentation

authors:
Ming Hu, Jianfu Yin, Zhuangzhuang Ma, Jianheng Ma, Feiyu Zhu, Bingbing Wu, Ya Wen, Meng Wu, Cong Hu, Bingliang Hu, Quan Wang

abstract:
Co-training has achieved significant success in the field of semi-supervised learning; however, the *homogenization phenomenon*, which arises from multiple models tending towards similar decision boundaries, remains inadequately addressed. To tackle this issue, we propose a novel algorithm called **&beta;-FFT** from the perspectives of data diversity and training structure.First, from the perspective of data diversity, we introduce a nonlinear interpolation method based on the **Fast Fourier Transform (FFT)**. This method generates more diverse training samples by swapping low-frequency components between pairs of images, thereby enhancing the model's generalization capability. Second, from the structural perspective, we propose a differentiated training strategy to alleviate the homogenization issue in co-training. In this strategy, we apply additional training with labeled data to one model in the co-training framework, while employing linear interpolation based on the **Beta (&beta;)** distribution for the unlabeled data as a regularization term for the additional training. This approach enables us to effectively utilize the limited labeled data while simultaneously improving the model's performance on unlabeled data, ultimately enhancing the overall performance of the system.Experimental results demonstrate that **&beta;-FFT** outperforms current state-of-the-art (SOTA) methods on three public medical image datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Hu_beta-FFT_Nonlinear_Interpolation_and_Differentiated_Training_Strategies_for_Semi-Supervised_Medical_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Hu_2025_CVPR,
    author    = {Hu, Ming and Yin, Jianfu and Ma, Zhuangzhuang and Ma, Jianheng and Zhu, Feiyu and Wu, Bingbing and Wen, Ya and Wu, Meng and Hu, Cong and Hu, Bingliang and Wang, Quan},
    title     = {beta-FFT: Nonlinear Interpolation and Differentiated Training Strategies for Semi-Supervised Medical Image Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30839-30849}
}

raw_affiliation:
[{"affiliation": "Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences", "city": "Xi'an", "country": "China"}, {"affiliation": "University of Chinese Academy of Sciences", "city": "Beijing", "country": "China"}, {"affiliation": "Xidian University", "city": "Xi'an", "country": "China"}, {"affiliation": "Xi\u2019an University of Technology", "city": "Xi'an", "country": "China"}, {"affiliation": "Zhongnan Hospital of Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "The First Affiliated Hospital of Guangxi Medical University", "city": "Nanning", "country": "China"}]

affiliations:
["Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences", "University of Chinese Academy of Sciences", "Xidian University", "Xi\u2019an University of Technology", "Zhongnan Hospital of Wuhan University", "The First Affiliated Hospital of Guangxi Medical University"]


title: Dynamic Group Normalization: Spatio-Temporal Adaptation to Evolving Data Statistics
authors: Yair Smadar, Assaf Hoogi
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30167-30177
raw_affiliation: [{"affiliation": "Ariel University", "city": "Ariel", "country": "Israel"}]
affiliations: ["Ariel University"]
cities: ["Ariel"]
countries: ["Israel"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Smadar_Dynamic_Group_Normalization_Spatio-Temporal_Adaptation_to_Evolving_Data_Statistics_CVPR_2025_paper.html

abstract:
Deep neural networks remain vulnerable to statistical variations in data, even with advances in normalization techniques. Existing methods use fixed-size normalization sets, restricting their adaptability to evolving and diverse data characteristics. We introduce Dynamic Group Normalization (DGN), a framework that dynamically adjusts channel grouping based on statistical awareness, ensuring a flexible and optimized formation of channel groups. By leveraging an efficient spatio-temporal mechanism, DGN continuously evaluates inter-channel relationships within layers and across training epochs, ensuring robust and responsive adaptation to the updated data statistics. Extensive evaluations of 34 architectures and 11 computer vision benchmarks show the consistent superiority of DGN over traditional normalization methods. It achieves significant accuracy gains in classification, detection, and segmentation tasks while maintaining computational efficiency. Moreover, it outperforms traditional methods in challenging scenarios, including out-of-distribution generalization, imbalanced long-tailed distributions, and corrupted data.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Smadar_Dynamic_Group_Normalization_Spatio-Temporal_Adaptation_to_Evolving_Data_Statistics_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Smadar_Dynamic_Group_Normalization_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Smadar_2025_CVPR,
    author    = {Smadar, Yair and Hoogi, Assaf},
    title     = {Dynamic Group Normalization: Spatio-Temporal Adaptation to Evolving Data Statistics},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30167-30177}
}


title: Latent Space Super-Resolution for Higher-Resolution Image Generation with Diffusion Models
authors: Jinho Jeong, Sangmin Han, Jinwoo Kim, Seon Joo Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 2355-2365
raw_affiliation: [{"affiliation": "Yonsei University", "city": "Seoul", "country": "South Korea"}]
affiliations: ["Yonsei University"]
cities: ["Seoul"]
countries: ["South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Jeong_Latent_Space_Super-Resolution_for_Higher-Resolution_Image_Generation_with_Diffusion_Models_CVPR_2025_paper.html

abstract:
In this paper, we propose LSRNA, a novel framework for higher-resolution (exceeding 1K) image generation using diffusion models by leveraging super-resolution directly in the latent space. Existing diffusion models struggle with scaling beyond their training resolutions, often leading to structural distortions or content repetition. Reference-based methods address the issues by upsampling a low-resolution reference to guide higher-resolution generation. However, they face significant challenges: upsampling in latent space often causes manifold deviation, which degrades output quality. On the other hand, upsampling in RGB space tends to produce overly smoothed outputs. To overcome these limitations, LSRNA combines Latent space Super-Resolution (LSR) for manifold alignment and Region-wise Noise Addition (RNA) to enhance high-frequency details. Our extensive experiments demonstrate that integrating LSRNA outperforms state-of-the-art reference-based methods across various resolutions and metrics, while showing the critical role of latent space upsampling in preserving detail and sharpness. The code will be available at https://github.com/3587jjh/LSRNA  https://github.com/3587jjh/LSRNA.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Jeong_Latent_Space_Super-Resolution_for_Higher-Resolution_Image_Generation_with_Diffusion_Models_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Jeong_Latent_Space_Super-Resolution_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Jeong_2025_CVPR,
    author    = {Jeong, Jinho and Han, Sangmin and Kim, Jinwoo and Kim, Seon Joo},
    title     = {Latent Space Super-Resolution for Higher-Resolution Image Generation with Diffusion Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {2355-2365}
}


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 29767-29779
cities: ["Shanghai", "Hong Kong", "Nanjing", "Beijing", "Shanghai", "Unknown"]
countries: ["China", "China", "China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Li_SynerGen-VL_Towards_Synergistic_Image_Understanding_and_Generation_with_Vision_Experts_CVPR_2025_paper.html

title:
SynerGen-VL: Towards Synergistic Image Understanding and Generation with Vision Experts and Token Folding

authors:
Hao Li, Changyao Tian, Jie Shao, Xizhou Zhu, Zhaokai Wang, Jinguo Zhu, Wenhan Dou, Xiaogang Wang, Hongsheng Li, Lewei Lu, Jifeng Dai

abstract:
The remarkable success of Large Language Models (LLMs) has extended to the multimodal domain, achieving outstanding performance in image understanding and generation. Recent efforts to develop unified Multimodal Large Language Models (MLLMs) that integrate these capabilities have shown promising results. However, existing approaches often involve complex designs in model architecture or training pipeline, increasing the difficulty of model training and scaling. In this paper, we propose SynerGen-VL, a simple yet powerful encoder-free MLLM capable of both image understanding and generation. To address challenges identified in existing encoder-free unified MLLMs, we introduce the token folding mechanism and the vision-expert-based progressive alignment pretraining strategy, effectively supporting high-resolution image understanding while reducing training complexity. After being trained on large-scale mixed image-text data with a unified next-token prediction objective, SynerGen-VL achieves or surpasses the performance of existing encoder-free unified MLLMs with comparable or smaller parameter sizes, and narrows the gap with task-specific state-of-the-art models, highlighting a promising path toward future unified MLLMs. Our code and models shall be released.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Li_SynerGen-VL_Towards_Synergistic_Image_Understanding_and_Generation_with_Vision_Experts_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Li_SynerGen-VL_Towards_Synergistic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Li_2025_CVPR,
    author    = {Li, Hao and Tian, Changyao and Shao, Jie and Zhu, Xizhou and Wang, Zhaokai and Zhu, Jinguo and Dou, Wenhan and Wang, Xiaogang and Li, Hongsheng and Lu, Lewei and Dai, Jifeng},
    title     = {SynerGen-VL: Towards Synergistic Image Understanding and Generation with Vision Experts and Token Folding},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {29767-29779}
}

raw_affiliation:
[{"affiliation": "OpenGVLab, Shanghai AI Laboratory", "city": "Shanghai", "country": "China"}, {"affiliation": "MMLab, The Chinese University of Hong Kong", "city": "Hong Kong", "country": "China"}, {"affiliation": "Nanjing University", "city": "Nanjing", "country": "China"}, {"affiliation": "Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "SenseTime Research", "city": "Unknown", "country": "China"}]

affiliations:
["OpenGVLab, Shanghai AI Laboratory", "MMLab, The Chinese University of Hong Kong", "Nanjing University", "Tsinghua University", "Shanghai Jiao Tong University", "SenseTime Research"]


title: Synthetic Prior for Few-Shot Drivable Head Avatar Inversion
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10735-10746
affiliations: ["Max Planck Institute for Intelligent Systems", "Technical University of Darmstadt", "Google"]
cities: ["T\u00fcbingen", "Darmstadt", "Mountain View"]
countries: ["Germany", "Germany", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zielonka_Synthetic_Prior_for_Few-Shot_Drivable_Head_Avatar_Inversion_CVPR_2025_paper.html

authors:
Wojciech Zielonka, Stephan J. Garbin, Alexandros Lattas, George Kopanas, Paulo Gotardo, Thabo Beeler, Justus Thies, Timo Bolkart

abstract:
We present SynShot, a novel method for the few-shot inversion of a drivable head avatar based on a synthetic prior. We tackle three major challenges. First, training a controllable 3D generative network requires a large number of diverse sequences, for which pairs of images and high-quality tracked meshes are not always available. Second, the use of real data is strictly regulated (e.g., under the General Data Protection Regulation, which mandates frequent deletion of models and data to accommodate a situation when participant's consent is withdrawn). Synthetic data, free from these constraints, is an appealing alternative. Third, state-of-the-art monocular avatar models struggle to generalize to new views and expressions, lacking a strong prior and often overfitting to a specific viewpoint distribution. Inspired by machine learning models trained solely on synthetic data, we propose a method that learns a prior model from a large dataset of synthetic heads with diverse identities, expressions, and viewpoints. With few input images, SynShot fine-tunes the pretrained synthetic prior to bridge the domain gap, modeling a photorealistic head avatar that generalizes to novel expressions and viewpoints. We model the head avatar using 3D Gaussian splatting and a convolutional encoder-decoder that outputs Gaussian parameters in UV texture space. To account for the different modeling complexities over parts of the head (e.g., skin vs hair), we embed the prior with explicit control for upsampling the number of per-part primitives. Compared to SOTA monocular and GAN-based methods, SynShot significantly improves novel view and expression synthesis.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zielonka_Synthetic_Prior_for_Few-Shot_Drivable_Head_Avatar_Inversion_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zielonka_Synthetic_Prior_for_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zielonka_2025_CVPR,
    author    = {Zielonka, Wojciech and Garbin, Stephan J. and Lattas, Alexandros and Kopanas, George and Gotardo, Paulo and Beeler, Thabo and Thies, Justus and Bolkart, Timo},
    title     = {Synthetic Prior for Few-Shot Drivable Head Avatar Inversion},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10735-10746}
}

raw_affiliation:
[{"affiliation": "Max Planck Institute for Intelligent Systems", "city": "T\u00fcbingen", "country": "Germany"}, {"affiliation": "Technical University of Darmstadt", "city": "Darmstadt", "country": "Germany"}, {"affiliation": "Google", "city": "Mountain View", "country": "USA"}]


title: Reasoning in Visual Navigation of End-to-end Trained Agents: A Dynamical Systems Approach
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 12111-12121
affiliations: ["1", "2", "3"]
cities: ["Grenoble", "Genova", "Genova"]
countries: ["France", "Italy", "Italy"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Janny_Reasoning_in_Visual_Navigation_of_End-to-end_Trained_Agents_A_Dynamical_CVPR_2025_paper.html

authors:
Steeven Janny, Hervé Poirier, Leonid Antsfeld, Guillaume Bono, Gianluca Monaci, Boris Chidlovskii, Francesco Giuliari, Alessio Del Bue, Christian Wolf

abstract:
Progress in Embodied AI has made it possible for end-to-end-trained agents to navigate in photo-realistic environments with high-level reasoning and zero-shot or language-conditioned behavior, but evaluations and benchmarks are still dominated by simulation. In this work, we focus on the fine-grained behavior of fast-moving real robots and present a large-scale experimental study involving \numepisodes   navigation episodes in a real environment with a physical robot, where we analyze the type of reasoning emerging from end-to-end training. In particular, we study the presence of realistic dynamics which the agent learned for open-loop forecasting, and their interplay with sensing. We analyze the way the agent uses latent memory to hold elements of the scene structure and information gathered during exploration. We probe the planning capabilities of the agent, and find in its memory evidence for somewhat precise plans over a limited horizon. Furthermore, we show in a post-hoc analysis that the value function learned by the agent relates to long-term planning. Put together, our experiments paint a new picture on how using tools from computer vision and sequential decision making have led to new capabilities in robotics and control. An interactive tool is available at https://visual-navigation-reasoning.github.io

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Janny_Reasoning_in_Visual_Navigation_of_End-to-end_Trained_Agents_A_Dynamical_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Janny_Reasoning_in_Visual_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Janny_2025_CVPR,
    author    = {Janny, Steeven and Poirier, Herv\'e and Antsfeld, Leonid and Bono, Guillaume and Monaci, Gianluca and Chidlovskii, Boris and Giuliari, Francesco and Del Bue, Alessio and Wolf, Christian},
    title     = {Reasoning in Visual Navigation of End-to-end Trained Agents: A Dynamical Systems Approach},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {12111-12121}
}

raw_affiliation:
[{"affiliation": "1", "city": "Grenoble", "country": "France"}, {"affiliation": "2", "city": "Genova", "country": "Italy"}, {"affiliation": "3", "city": "Genova", "country": "Italy"}]


title: Rethinking Noisy Video-Text Retrieval via Relation-aware Alignment
authors: Huakai Lai, Guoxin Xiong, Huayu Mai, Xiang Liu, Tianzhu Zhang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9231-9241
cities: ["Hefei", "Hefei", "Dongguan"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lai_Rethinking_Noisy_Video-Text_Retrieval_via_Relation-aware_Alignment_CVPR_2025_paper.html

abstract:
Video-Text Retrieval (VTR) is a core task in multi-modal understanding, drawing growing attention from both academia and industry in recent years. While numerous VTR methods have achieved success, most of them assume accurate visual-text correspondences during training, which is difficult to ensure in practice due to ubiquitous noise, known as noisy correspondences (NC). In this paper, we rethink how to mitigate the NC from the perspective of representative reference features (termed agents), and propose a novel relation-aware purified consistency (RPC) network to amend direct pairwise correlation, including representative agents construction and relation-aware ranking distribution alignment. The proposed RPC enjoys several merits. First, to learn the agents well without any correspondence supervision, we customize the agents construction according to the three characteristics of reliability, representativeness, and resilience. Second, the ranking distribution-based alignment process leverages the structural information inherent in inter-pair relationships, making it more robust compared to individual comparisons. Extensive experiments on five datasets under different settings demonstrate the efficacy and robustness of our method.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lai_Rethinking_Noisy_Video-Text_Retrieval_via_Relation-aware_Alignment_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lai_Rethinking_Noisy_Video-Text_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lai_2025_CVPR,
    author    = {Lai, Huakai and Xiong, Guoxin and Mai, Huayu and Liu, Xiang and Zhang, Tianzhu},
    title     = {Rethinking Noisy Video-Text Retrieval via Relation-aware Alignment},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9231-9241}
}

raw_affiliation:
[{"affiliation": "University of Science and Technology of China", "city": "Hefei", "country": "China"}, {"affiliation": "National Key Laboratory of Deep Space Exploration, Deep Space Exploration Laboratory", "city": "Hefei", "country": "China"}, {"affiliation": "Dongguan University of Technology", "city": "Dongguan", "country": "China"}]

affiliations:
["University of Science and Technology of China", "National Key Laboratory of Deep Space Exploration, Deep Space Exploration Laboratory", "Dongguan University of Technology"]


title: DFormerv2: Geometry Self-Attention for RGBD Semantic Segmentation
authors: Bo-Wen Yin, Jiao-Long Cao, Ming-Ming Cheng, Qibin Hou
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19345-19355
raw_affiliation: [{"affiliation": "Nankai University", "city": "Tianjin", "country": "China"}]
affiliations: ["Nankai University"]
cities: ["Tianjin"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yin_DFormerv2_Geometry_Self-Attention_for_RGBD_Semantic_Segmentation_CVPR_2025_paper.html

abstract:
Recent advances in scene understanding benefit a lot from depth maps because of the 3D geometry information, especially in complex conditions (e.g., low light and overexposed). Existing approaches encode depth maps along with RGB images and perform feature fusion between them to enable more robust predictions. Taking into account that depth can be regarded as a geometry supplement for RGB images, a straightforward question arises: Do we really need to explicitly encode depth information with neural networks as done for RGB images? Based on this insight, in this paper, we investigate a new way to learn RGBD feature representations and present DFormerv2, a strong RGBD encoder that explicitly uses depth maps as geometry priors rather than encoding depth information with neural networks. Our goal is to extract the geometry clues from the depth and spatial distances among all the image patch tokens, which will then be used as geometry priors to allocate attention weights in self-attention. Extensive experiments demonstrate that DFormerv2 exhibits exceptional performance in various RGBD semantic segmentation benchmarks. Code is available at: https://github.com/VCIP-RGBD/DFormer.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yin_DFormerv2_Geometry_Self-Attention_for_RGBD_Semantic_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yin_DFormerv2_Geometry_Self-Attention_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yin_2025_CVPR,
    author    = {Yin, Bo-Wen and Cao, Jiao-Long and Cheng, Ming-Ming and Hou, Qibin},
    title     = {DFormerv2: Geometry Self-Attention for RGBD Semantic Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19345-19355}
}


title: Scaling Vision Pre-Training to 4K Resolution
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 9631-9640
affiliations: ["UC Berkeley", "NVIDIA"]
cities: ["Berkeley", "Santa Clara"]
countries: ["USA", "USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shi_Scaling_Vision_Pre-Training_to_4K_Resolution_CVPR_2025_paper.html

authors:
Baifeng Shi, Boyi Li, Han Cai, Yao Lu, Sifei Liu, Marco Pavone, Jan Kautz, Song Han, Trevor Darrell, Pavlo Molchanov, Hongxu Yin

abstract:
High-resolution perception of visual details is crucial for daily tasks. Current vision pre-training, however, is still limited to low resolutions (e.g., 378 x 378 pixels) due to the quadratic cost of processing larger images. We introduce PS3 that scales CLIP-style vision pre-training to 4K resolution with a near-constant cost. Instead of contrastive learning on global image representation, PS3 is pre-trained by selectively processing local regions and contrasting them with local detailed captions, enabling high-resolution representation learning with greatly reduced computational overhead. The pre-trained PS3 is able to both encode the global image at low resolution and selectively process local high-resolution regions based on their saliency or relevance to a text prompt. When applying PS3 to multi-modal LLM (MLLM), the resulting model, named VILA-HD, significantly improves high-resolution visual perception compared to baselines without high-resolution vision pre-training such as AnyRes and S^2 while using up to 4.3x fewer tokens. PS3 also unlocks appealing scaling properties of VILA-HD, including scaling up resolution for free and scaling up test-time compute for better performance. Compared to state of the arts, VILA-HD outperforms previous MLLMs such as NVILA and Qwen2-VL across multiple benchmarks and achieves better efficiency than latest token pruning approaches. Finally, we find current benchmarks do not require 4K-resolution perception, which motivates us to propose 4KPro, a new benchmark of image QA at 4K resolution, on which VILA-HD outperforms all previous MLLMs, including a 14.5% improvement over GPT-4o, and a 3.2% improvement and 2.96x speedup over Qwen2-VL.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shi_Scaling_Vision_Pre-Training_to_4K_Resolution_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Shi_Scaling_Vision_Pre-Training_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Shi_2025_CVPR,
    author    = {Shi, Baifeng and Li, Boyi and Cai, Han and Lu, Yao and Liu, Sifei and Pavone, Marco and Kautz, Jan and Han, Song and Darrell, Trevor and Molchanov, Pavlo and Yin, Hongxu},
    title     = {Scaling Vision Pre-Training to 4K Resolution},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {9631-9640}
}

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


authors: Ruihai Wu, Ziyu Zhu, Yuran Wang, Yue Chen, Jiarui Wang, Hao Dong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 6950-6959
affiliations: ["CFCS, School of Computer Science, PKU", "School of EECS, PKU"]
cities: ["Beijing", "Beijing"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wu_GarmentPile_Point-Level_Visual_Affordance_Guided_Retrieval_and_Adaptation_for_Cluttered_CVPR_2025_paper.html

title:
GarmentPile: Point-Level Visual Affordance Guided Retrieval and Adaptation for Cluttered Garments Manipulation

abstract:
Cluttered garments manipulation poses significant challenges in robotics due to the complex, deformable nature of garments and intricate garment relations. Unlike single-garment manipulation, cluttered scenarios require managing complex garment entanglements and interactions, while maintaining garment cleanliness and manipulation stability. To address these demands, we propose to learn point-level affordance, the dense representation modeling the complex space and multi-modal manipulation candidates, with novel designs for the awareness of garment geometry, structure, and inter-object relations. Additionally, we introduce an adaptation module, informed by learned affordance, to reorganize cluttered garments into configurations conducive to manipulation. Our framework demonstrates effectiveness over environments featuring diverse garment types and pile scenarios in both simulation and the real world. Project page: https://garmentpile.github.io/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wu_GarmentPile_Point-Level_Visual_Affordance_Guided_Retrieval_and_Adaptation_for_Cluttered_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wu_GarmentPile_Point-Level_Visual_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Wu_2025_CVPR,
    author    = {Wu, Ruihai and Zhu, Ziyu and Wang, Yuran and Chen, Yue and Wang, Jiarui and Dong, Hao},
    title     = {GarmentPile: Point-Level Visual Affordance Guided Retrieval and Adaptation for Cluttered Garments Manipulation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {6950-6959}
}

raw_affiliation:
[{"affiliation": "CFCS, School of Computer Science, PKU", "city": "Beijing", "country": "China"}, {"affiliation": "School of EECS, PKU", "city": "Beijing", "country": "China"}]


title: Uncertain Multimodal Intention and Emotion Understanding in the Wild
authors: Qu Yang, Qinghongya Shi, Tongxin Wang, Mang Ye
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 24700-24709
cities: ["Wuhan", "Wuhan"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_Uncertain_Multimodal_Intention_and_Emotion_Understanding_in_the_Wild_CVPR_2025_paper.html

abstract:
Understanding intention and emotion from social media poses unique challenges due to the inherent uncertainty in multimodal data, where posts often contain incomplete or missing modalities. While this uncertainty reflects real-world scenarios, it remains underexplored within the computer vision community, particularly in conjunction with the intrinsic relationship between emotion and intention. To address these challenges, we introduce the Multimodal IntentioN and Emotion Understanding in the Wild (MINE) dataset, comprising over 20,000 topic-specific social media posts with natural modality variations across text, image, video, and audio. MINE is distinctively constructed to capture both the uncertain nature of multimodal data and the implicit correlations between intentions and emotions, providing extensive annotations for both aspects. To tackle these scenarios, we propose the Bridging Emotion-Intention via Implicit Label Reasoning (BEAR) framework. BEAR consists of two key components: a BEIFormer that leverages emotion-intention correlations, and a Modality Asynchronous Prompt that handles modality uncertainty. Experiments show that BEAR outperforms existing methods in processing uncertain multimodal data while effectively mining emotion-intention relationships for social media content understanding.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_Uncertain_Multimodal_Intention_and_Emotion_Understanding_in_the_Wild_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_Uncertain_Multimodal_Intention_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Qu and Shi, Qinghongya and Wang, Tongxin and Ye, Mang},
    title     = {Uncertain Multimodal Intention and Emotion Understanding in the Wild},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {24700-24709}
}

raw_affiliation:
[{"affiliation": "School of Computer Science, Wuhan University", "city": "Wuhan", "country": "China"}, {"affiliation": "Taikang Center for Life and Medical Sciences, Wuhan University", "city": "Wuhan", "country": "China"}]

affiliations:
["School of Computer Science, Wuhan University", "Taikang Center for Life and Medical Sciences, Wuhan University"]


title: GroomLight: Hybrid Inverse Rendering for Relightable Human Hair Appearance Modeling
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16040-16050
affiliations: ["Stanford University", "Google", "ETH Zurich"]
cities: ["Stanford", "Mountain View", "Zurich"]
countries: ["USA", "USA", "Switzerland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zheng_GroomLight_Hybrid_Inverse_Rendering_for_Relightable_Human_Hair_Appearance_Modeling_CVPR_2025_paper.html

authors:
Yang Zheng, Menglei Chai, Delio Vicini, Yuxiao Zhou, Yinghao Xu, Leonidas Guibas, Gordon Wetzstein, Thabo Beeler

abstract:
We present GroomLight, a novel method for relightable hair appearance modeling from multi-view images. Existing hair capture methods struggle to balance photorealistic rendering with relighting capabilities. Analytical material models, while physically grounded, often fail to fully capture appearance details. Conversely, neural rendering approaches excel at view synthesis but generalize poorly to novel lighting conditions. GroomLight addresses this challenge by combining the strengths of both paradigms. It employs an extended hair BSDF model to capture primary light transport and a light-aware residual model to reconstruct the remaining details. We further propose a hybrid inverse rendering pipeline to optimize both components, enabling high-fidelity relighting, view synthesis, and material editing. Extensive evaluations on real-world hair data demonstrate state-of-the-art performance of our method. Our project website is at: https://syntec-research.github.io/GroomLight.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zheng_GroomLight_Hybrid_Inverse_Rendering_for_Relightable_Human_Hair_Appearance_Modeling_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zheng_GroomLight_Hybrid_Inverse_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Zheng_2025_CVPR,
    author    = {Zheng, Yang and Chai, Menglei and Vicini, Delio and Zhou, Yuxiao and Xu, Yinghao and Guibas, Leonidas and Wetzstein, Gordon and Beeler, Thabo},
    title     = {GroomLight: Hybrid Inverse Rendering for Relightable Human Hair Appearance Modeling},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16040-16050}
}

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


title: Improving Editability in Image Generation with Layer-wise Memory
authors: Daneul Kim, Jaeah Lee, Jaesik Park
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 7889-7898
raw_affiliation: [{"affiliation": "Seoul National University", "city": "Seoul", "country": "Republic of Korea"}]
affiliations: ["Seoul National University"]
cities: ["Seoul"]
countries: ["Republic of Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kim_Improving_Editability_in_Image_Generation_with_Layer-wise_Memory_CVPR_2025_paper.html

abstract:
Most real-world image editing tasks require multiple sequential edits to achieve desired results. Current editing approaches, primarily designed for single-object modifications, struggle with sequential editing: especially with maintaining previous edits along with adapting new objects naturally into the existing content. These limitations significantly hinder complex editing scenarios where multiple objects need to be modified while preserving their contextual relationships. We address this fundamental challenge through two key proposals: enabling rough mask inputs that preserve existing content while naturally integrating new elements and supporting consistent editing across multiple modifications. Our framework achieves this through layer-wise memory, which stores latent representations and prompt embeddings from previous edits. We propose Background Consistency Guidance that leverages memorized latents to maintain scene coherence and Multi-Query Disentanglement in cross-attention that ensures natural adaptation to existing content. To evaluate our method, we present a new benchmark dataset incorporating semantic alignment metrics and interactive editing scenarios. Through comprehensive experiments, we demonstrate superior performance in iterative image editing tasks with minimal user effort, requiring only rough masks while maintaining high-quality results throughout multiple editing steps.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kim_Improving_Editability_in_Image_Generation_with_Layer-wise_Memory_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kim_Improving_Editability_in_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kim_2025_CVPR,
    author    = {Kim, Daneul and Lee, Jaeah and Park, Jaesik},
    title     = {Improving Editability in Image Generation with Layer-wise Memory},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {7889-7898}
}


title: Sea-ing in Low-light
authors: Nisha Varghese, A. N. Rajagopalan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 16629-16640
raw_affiliation: [{"affiliation": "Indian Institute of Technology Madras", "city": "Chennai", "country": "India"}]
affiliations: ["Indian Institute of Technology Madras"]
cities: ["Chennai"]
countries: ["India"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Varghese_Sea-ing_in_Low-light_CVPR_2025_paper.html

abstract:
Underwater (UW) robotics applications require depth and restored images simultaneously in real-time, irrespective of whether the UW images are captured in good lighting conditions or not. Most of the UW image restoration and depth estimation methods have been devised for images under normal lighting. Consequently, they struggle to perform on poorly lit images. Even though artificial illumination can be used when there is insufficient ambient light, it can introduce non-uniform lighting artifacts in the restored images. Hence, the recovery of depth and restored images directly from Low-Light UW (LLUW) images is a critical requirement in marine applications. While a few works have attempted LLUW image restoration, there are no reported works on joint recovery of depth and clean image from LLUW images. We propose a Self-supervised Low-light Underwater Image and Depth recovery network (SelfLUID-Net) for joint estimation of depth and restored image in real-time from a single LLUW image. We have collected an Underwater Low-light Stereo Video (ULVStereo) dataset which is the first-ever UW dataset with stereo pairs of low-light and normally-lit UW images. For the dual tasks of image and depth recovery from a LLUW image, we effectively utilize the stereo data from ULVStereo that provides cues for both depth and illumination-independent clean image. We harness a combination of the UW image formation process, the Retinex model, and constraints enforced by the scene geometry for our self-supervised training. To handle occlusions, we additionally utilize monocular frames from our video dataset and propose a masking scheme to prevent dynamic transients, that do not respect the underlying scene geometry, from misguiding the learning process. Evaluations on five LLUW datasets demonstrate the superiority and generalization ability of our proposed SelfLUID-Net over existing state-of-the-art methods.  The dataset ULVStereo is available at https://github.com/nishavarghese15/ULVStereo.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Varghese_Sea-ing_in_Low-light_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Varghese_Sea-ing_in_Low-light_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Varghese_2025_CVPR,
    author    = {Varghese, Nisha and Rajagopalan, A. N.},
    title     = {Sea-ing in Low-light},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {16629-16640}
}


title: VidTwin: Video VAE with Decoupled Structure and Dynamics
authors: Yuchi Wang, Junliang Guo, Xinyi Xie, Tianyu He, Xu Sun, Jiang Bian
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22922-22932
affiliations: ["Peking University", "Microsoft Research Asia", "CUHK (SZ)"]
cities: ["Beijing", "Beijing", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wang_VidTwin_Video_VAE_with_Decoupled_Structure_and_Dynamics_CVPR_2025_paper.html

abstract:
Recent advancements in video autoencoders (Video AEs) have significantly improved the quality and efficiency of video generation. In this paper, we propose a novel and compact video autoencoder, VidTwin, that decouples video into two distinct latent spaces: Structure latent vectors, which capture overall content and global movement, and Dynamics latent vectors, which represent fine-grained details and rapid movements. Specifically, our approach leverages an Encoder-Decoder backbone, augmented with two submodules for extracting these latent spaces, respectively. The first submodule employs a Q-Former to extract low-frequency motion trends, followed by downsampling blocks to remove redundant content details. The second averages the latent vectors along the spatial dimension to capture rapid motion. Extensive experiments show that VidTwin achieves a high compression rate of 0.2% with high reconstruction quality (PSNR of 28.14 on the MCL-JCV dataset), and performs efficiently and effectively in downstream generative tasks. Moreover, our model demonstrates explainability and scalability, paving the way for future research in video latent representation and generation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wang_VidTwin_Video_VAE_with_Decoupled_Structure_and_Dynamics_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wang_VidTwin_Video_VAE_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wang_2025_CVPR,
    author    = {Wang, Yuchi and Guo, Junliang and Xie, Xinyi and He, Tianyu and Sun, Xu and Bian, Jiang},
    title     = {VidTwin: Video VAE with Decoupled Structure and Dynamics},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22922-22932}
}

raw_affiliation:
[{"affiliation": "Peking University", "city": "Beijing", "country": "China"}, {"affiliation": "Microsoft Research Asia", "city": "Beijing", "country": "China"}, {"affiliation": "CUHK (SZ)", "city": "Shenzhen", "country": "China"}]


title: CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental Learning
authors: Jiangpeng He, Zhihao Duan, Fengqing Zhu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30534-30544
affiliations: ["Massachusetts Institute of Technology", "Purdue University"]
cities: ["Cambridge", "West Lafayette"]
countries: ["U.S.A.", "U.S.A."]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/He_CL-LoRA_Continual_Low-Rank_Adaptation_for_Rehearsal-Free_Class-Incremental_Learning_CVPR_2025_paper.html

abstract:
Class-Incremental Learning (CIL) aims to learn new classes sequentially while retaining the knowledge of previously learned classes. Recently, pre-trained models (PTMs) combined with parameter-efficient fine-tuning (PEFT) have shown remarkable performance in rehearsal-free CIL without requiring exemplars from previous tasks. However, existing adapter-based methods, which incorporate lightweight learnable modules into PTMs for CIL, create new adapters for each new task, leading to both parameter redundancy and failure to leverage shared knowledge across tasks. In this work, we propose ContinuaL Low-Rank Adaptation (CL-LoRA), which introduces a novel dual-adapter architecture combining task-shared adapters to learn cross-task knowledge and task-specific adapters to capture unique features of each new task. Specifically, the shared adapters utilize random orthogonal matrices and leverage knowledge distillation with gradient reassignment to preserve essential shared knowledge. In addition, we introduce learnable block-wise weights for task-specific adapters, which mitigate inter-task interference while maintaining the model's plasticity. We demonstrate CL-LoRA consistently achieves promising performance under multiple benchmarks with reduced training and inference computation, establishing a more efficient and scalable paradigm for continual learning with pre-trained models.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/He_CL-LoRA_Continual_Low-Rank_Adaptation_for_Rehearsal-Free_Class-Incremental_Learning_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/He_CL-LoRA_Continual_Low-Rank_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{He_2025_CVPR,
    author    = {He, Jiangpeng and Duan, Zhihao and Zhu, Fengqing},
    title     = {CL-LoRA: Continual Low-Rank Adaptation for Rehearsal-Free Class-Incremental Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30534-30544}
}

raw_affiliation:
[{"affiliation": "Massachusetts Institute of Technology", "city": "Cambridge", "country": "U.S.A."}, {"affiliation": "Purdue University", "city": "West Lafayette", "country": "U.S.A."}]


title: Generative Modeling of Class Probability for Multi-Modal Representation Learning
authors: JungKyoo Shin, Bumsoo Kim, Eunwoo Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 20737-20746
raw_affiliation: [{"affiliation": "Chung-Ang University", "city": "Seoul", "country": "South Korea"}]
affiliations: ["Chung-Ang University"]
cities: ["Seoul"]
countries: ["South Korea"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shin_Generative_Modeling_of_Class_Probability_for_Multi-Modal_Representation_Learning_CVPR_2025_paper.html

abstract:
Multi-modal understanding plays a crucial role in artificial intelligence by enabling models to jointly interpret inputs from different modalities. However, conventional approaches such as contrastive learning often struggle with modality discrepancies, leading to potential misalignments. In this paper, we propose a novel class anchor alignment approach that leverages class probability distributions for multi-modal representation learning. Our method, Class-anchor-ALigned generative Modeling (CALM), encodes class anchors as prompts to generate and align class probability distributions for each modality, enabling more flexible alignment. Furthermore, we introduce a cross-modal probabilistic variational autoencoder to model uncertainty in the alignment, enhancing the ability to capture deeper relationships between modalities and data variations. Extensive experiments on four benchmark datasets demonstrate that our approach significantly outperforms state-of-the-art methods, especially in out-of-domain evaluations. This highlights its superior generalization capabilities in multi-modal representation learning.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shin_Generative_Modeling_of_Class_Probability_for_Multi-Modal_Representation_Learning_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Shin_2025_CVPR,
    author    = {Shin, JungKyoo and Kim, Bumsoo and Kim, Eunwoo},
    title     = {Generative Modeling of Class Probability for Multi-Modal Representation Learning},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {20737-20746}
}


title: VisionZip: Longer is Better but Not Necessary in Vision Language Models
authors: Senqiao Yang, Yukang Chen, Zhuotao Tian, Chengyao Wang, Jingyao Li, Bei Yu, Jiaya Jia
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19792-19802
affiliations: ["CUHK", "HKUST", "HITSZ"]
cities: ["Hong Kong", "Hong Kong", "Shenzhen"]
countries: ["China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yang_VisionZip_Longer_is_Better_but_Not_Necessary_in_Vision_Language_CVPR_2025_paper.html

abstract:
Recent advancements in vision-language models have enhanced performance by increasing the length of visual tokens, making them much longer than text tokens and significantly raising computational costs.However, we observe that the visual tokens generated by popular vision encoders, such as CLIP and SigLIP, contain significant redundancy. To address this, we introduce VisionZip, a simple yet effective method that selects a set of informative tokens for input to the language model, reducing visual token redundancy and improving efficiency while maintaining model performance. The proposed VisionZip can be widely applied to image and video understanding tasks and is well-suited for multi-turn dialogues in real-world scenarios, where previous methods tend to underperform.Experimental results show that VisionZip outperforms the previous state-of-the-art method by at least 5% performance gains across nearly all settings.Moreover, our method significantly enhances model inference speed, improving the prefilling time by 8xand enabling the LLaVA-Next 13B model to infer faster than the LLaVA-Next 7B model while achieving better results.Furthermore, we analyze the causes of this redundancy and encourage the community to focus on extracting better visual features rather than merely increasing token length. Our code is available at https://github.com/dvlab-research/VisionZip.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yang_VisionZip_Longer_is_Better_but_Not_Necessary_in_Vision_Language_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yang_VisionZip_Longer_is_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yang_2025_CVPR,
    author    = {Yang, Senqiao and Chen, Yukang and Tian, Zhuotao and Wang, Chengyao and Li, Jingyao and Yu, Bei and Jia, Jiaya},
    title     = {VisionZip: Longer is Better but Not Necessary in Vision Language Models},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19792-19802}
}

raw_affiliation:
[{"affiliation": "CUHK", "city": "Hong Kong", "country": "China"}, {"affiliation": "HKUST", "city": "Hong Kong", "country": "China"}, {"affiliation": "HITSZ", "city": "Shenzhen", "country": "China"}]


title: Simplification Is All You Need against Out-of-Distribution Overconfidence
authors: Keke Tang, Chao Hou, Weilong Peng, Xiang Fang, Zhize Wu, Yongwei Nie, Wenping Wang, Zhihong Tian
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 5030-5040
cities: ["Guangzhou", "Singapore", "Hefei", "Guangzhou", "College Station"]
countries: ["China", "Singapore", "China", "China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_Simplification_Is_All_You_Need_against_Out-of-Distribution_Overconfidence_CVPR_2025_paper.html

abstract:
Deep neural networks (DNNs) often exhibit out-of-distribution (OOD) overconfidence, producing overly confident predictions on OOD samples. We attribute this issue to the inherent over-complexity of DNNs and investigate two key aspects: capacity and nonlinearity. First, we demonstrate that reducing model capacity through knowledge distillation can effectively mitigate OOD overconfidence. Second, we show that selectively reducing nonlinearity by removing ReLU operations further alleviates the issue. Building on these findings, we present a practical guide to model simplification, combining both strategies to significantly reduce OOD overconfidence. Extensive experiments validate the effectiveness of this approach in mitigating OOD overconfidence and demonstrate its superiority over state-of-the-art methods. Additionally, our simplification strategies can be combined with existing OOD detection techniques to further enhance OOD detection performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_Simplification_Is_All_You_Need_against_Out-of-Distribution_Overconfidence_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Keke and Hou, Chao and Peng, Weilong and Fang, Xiang and Wu, Zhize and Nie, Yongwei and Wang, Wenping and Tian, Zhihong},
    title     = {Simplification Is All You Need against Out-of-Distribution Overconfidence},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {5030-5040}
}

raw_affiliation:
[{"affiliation": "Guangzhou University", "city": "Guangzhou", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}, {"affiliation": "Hefei University", "city": "Hefei", "country": "China"}, {"affiliation": "South China University of Technology", "city": "Guangzhou", "country": "China"}, {"affiliation": "Texas A&M University", "city": "College Station", "country": "USA"}]

affiliations:
["Guangzhou University", "Nanyang Technological University", "Hefei University", "South China University of Technology", "Texas A&M University"]


title: SpatialDreamer: Self-supervised Stereo Video Synthesis from Monocular Input
authors: Zhen Lv, Yangqi Long, Congzhentao Huang, Cao Li, Chengfei Lv, Hao Ren, Dian Zheng
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 811-821
affiliations: ["Alibaba Group", "Sun Yat-sen University"]
cities: ["Hangzhou", "Guangzhou"]
countries: ["China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lv_SpatialDreamer_Self-supervised_Stereo_Video_Synthesis_from_Monocular_Input_CVPR_2025_paper.html

abstract:
Stereo video synthesis from monocular input is challenging in spatial computing and virtual reality due to the lack of high-quality stereo video pairs for training and the difficulty of maintaining spatio-temporal consistency between frames. Existing methods primarily address these issues by directly applying novel view synthesis (NVS) techniques to video, while facing limitations such as the inability to effectively represent dynamic scenes and the requirement for extensive training data. In this paper, we introduce a novel self-supervised stereo video synthesis paradigm via a video diffusion model, termed SpatialDreamer, which meets the challenges head-on. Firstly, to address the stereo video data insufficiency, we propose a Depth based Video Generation module DVG, which employs a forward-backward rendering mechanism to generate paired videos with geometric and temporal priors. Leveraging data generated by DVG, we propose RefinerNet along with a self-supervised synthetic framework designed to facilitate efficient and dedicated training.More importantly, we devise a consistency control module, which consists of a metric of stereo deviation strength and a Temporal Interaction Learning module TIL for geometric and temporal consistency ensurance respectively. We evaluated the proposed method against various benchmark methods, with the results showcasing its superior performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lv_SpatialDreamer_Self-supervised_Stereo_Video_Synthesis_from_Monocular_Input_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lv_SpatialDreamer_Self-supervised_Stereo_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Lv_2025_CVPR,
    author    = {Lv, Zhen and Long, Yangqi and Huang, Congzhentao and Li, Cao and Lv, Chengfei and Ren, Hao and Zheng, Dian},
    title     = {SpatialDreamer: Self-supervised Stereo Video Synthesis from Monocular Input},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {811-821}
}

raw_affiliation:
[{"affiliation": "Alibaba Group", "city": "Hangzhou", "country": "China"}, {"affiliation": "Sun Yat-sen University", "city": "Guangzhou", "country": "China"}]


title: LOD-GS: Achieving Levels of Detail using Scalable Gaussian Soup
authors: Jianxiong Shen, Yue Qian, Xiaohang Zhan
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 671-680
raw_affiliation: [{"affiliation": "Tencent AI Lab", "city": "Shenzhen", "country": "China"}]
affiliations: ["Tencent AI Lab"]
cities: ["Shenzhen"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Shen_LOD-GS_Achieving_Levels_of_Detail_using_Scalable_Gaussian_Soup_CVPR_2025_paper.html

abstract:
Current 3D Gaussian Splatting methods often overlook structured information, leading to disorganized 3D Gaussians that compromise memory and structure efficiency, particularly in Level-of-Detail (LOD) management. This inefficiency results in excessive memory use, limiting their application in resource-sensitive environments like virtual reality and interactive simulations. To overcome these challenges, we introduce a scalable Gaussian Soup that enables high-performance LOD management with progressively reduced memory usage. Our method utilizes triangle primitives with Gaussian splats embedded and adaptive pruning/growing strategies to ensure high-quality scene rendering with significantly reduced memory demands. By embedding neural Gaussians within the triangle primitives through the triangle-MLP, we achieve further memory savings while maintaining rendering fidelity. Experimental results demonstrate that our approach achieves consistently superior performance than recent leading techniques across various LOD while progressively reducing memory usage.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Shen_LOD-GS_Achieving_Levels_of_Detail_using_Scalable_Gaussian_Soup_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Shen_2025_CVPR,
    author    = {Shen, Jianxiong and Qian, Yue and Zhan, Xiaohang},
    title     = {LOD-GS: Achieving Levels of Detail using Scalable Gaussian Soup},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {671-680}
}


title: BlenderGym: Benchmarking Foundational Model Systems for Graphics Editing
authors: Yunqi Gu, Ian Huang, Jihyeon Je, Guandao Yang, Leonidas Guibas
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18574-18583
raw_affiliation: [{"affiliation": "Stanford University", "city": "Stanford", "country": "USA"}]
affiliations: ["Stanford University"]
cities: ["Stanford"]
countries: ["USA"]
highlight: yes
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Gu_BlenderGym_Benchmarking_Foundational_Model_Systems_for_Graphics_Editing_CVPR_2025_paper.html

abstract:
3D graphics editing is crucial in applications like movie production and game design, yet it remains a time-consuming process that demands highly specialized domain expertise. Automating this process is challenging because graphical editing requires performing a variety of tasks, each requiring distinct skill sets. Recently, vision-language models (VLMs) have emerged as a powerful framework for automating the editing process, but their development and evaluation are bottlenecked by the lack of a comprehensive benchmark that requires human-level perception and presents real-world editing complexity. In this work, we present BlenderGym, the first comprehensive VLM system benchmark for 3D graphics editing. BlenderGym evaluates VLM systems through code-based 3D reconstruction tasks. We evaluate closed- and open-source VLM systems and observe that even the state-of-the-art VLM system struggles with tasks relatively easy for human Blender users. Enabled by BlenderGym, we study how inference scaling techniques impact VLM's performance on graphics editing tasks. Notably, our findings reveal that the verifier used to guide the scaling of generation can itself be improved through inference scaling, complementing recent insights on inference scaling of LLM generation in coding and math tasks. We further show that inference compute is not uniformly effective and can be optimized by strategically distributing it between generation and verification.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Gu_BlenderGym_Benchmarking_Foundational_Model_Systems_for_Graphics_Editing_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Gu_BlenderGym_Benchmarking_Foundational_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Gu_2025_CVPR,
    author    = {Gu, Yunqi and Huang, Ian and Je, Jihyeon and Yang, Guandao and Guibas, Leonidas},
    title     = {BlenderGym: Benchmarking Foundational Model Systems for Graphics Editing},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18574-18583}
}


title: VoteFlow: Enforcing Local Rigidity in Self-Supervised Scene Flow
authors: Yancong Lin, Shiming Wang, Liangliang Nan, Julian Kooij, Holger Caesar
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 17155-17164
affiliations: ["TU Delft", "ETH Zurich"]
cities: ["Delft", "Zurich"]
countries: ["Netherlands", "Switzerland"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_VoteFlow_Enforcing_Local_Rigidity_in_Self-Supervised_Scene_Flow_CVPR_2025_paper.html

abstract:
Scene flow estimation aims to recover per-point motion from two adjacent LiDAR scans. However, in real-world applications such as autonomous driving, points rarely move independently of others, especially for nearby points belonging to the same object, which often share the same motion. Incorporating this locally rigid motion constraint has been a key challenge in self-supervised scene flow estimation, which is often addressed by post-processing or appending extra regularization. While these approaches are able to improve the rigidity of predicted flows, they lack an architectural inductive bias for local rigidity within the model structure, leading to suboptimal learning efficiency and inferior performance. In contrast, we enforce local rigidity with a lightweight add-on module in neural network design, enabling end-to-end learning. We design a discretized voting space that accommodates all possible translations and then identify the one shared by nearby points by differentiable voting.  Additionally, to ensure computational efficiency, we operate on pillars rather than points and learn representative features for voting per pillar. We plug the Voting Module into popular model designs and evaluate its benefit on Argoverse 2 and Waymo datasets. We outperform baseline works with only marginal compute overhead. Code is available at https://github.com/tudelft-iv/VoteFlow.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_VoteFlow_Enforcing_Local_Rigidity_in_Self-Supervised_Scene_Flow_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_VoteFlow_Enforcing_Local_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Yancong and Wang, Shiming and Nan, Liangliang and Kooij, Julian and Caesar, Holger},
    title     = {VoteFlow: Enforcing Local Rigidity in Self-Supervised Scene Flow},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {17155-17164}
}

raw_affiliation:
[{"affiliation": "TU Delft", "city": "Delft", "country": "Netherlands"}, {"affiliation": "ETH Zurich", "city": "Zurich", "country": "Switzerland"}]


title: The Devil is in Low-Level Features for Cross-Domain Few-Shot Segmentation
authors: Yuhan Liu, Yixiong Zou, Yuhua Li, Ruixuan Li
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 4618-4627
affiliations: ["School of Computer Science and Technology, Huazhong University of Science and Technology"]
cities: ["Wuhan"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Liu_The_Devil_is_in_Low-Level_Features_for_Cross-Domain_Few-Shot_Segmentation_CVPR_2025_paper.html

abstract:
Cross-Domain Few-Shot Segmentation (CDFSS) is proposed to transfer the pixel-level segmentation capabilities learned from large-scale source-domain datasets to downstream target-domain datasets, with only a few annotated images per class. In this paper, we focus on a well-observed but unresolved phenomenon in CDFSS: for target domains, particularly those distant from the source domain, segmentation performance peaks at the very early epochs, and declines sharply as the source-domain training proceeds. We delve into this phenomenon for an interpretation: low-level features are vulnerable to domain shifts, leading to sharper loss landscapes during the source-domain training, which is the devil of CDFSS. Based on this phenomenon and interpretation, we further propose a method that includes two plug-and-play modules: one to flatten the loss landscapes for low-level features during source-domain training as a novel sharpness-aware minimization method, and the other to directly supplement target-domain information to the model during target-domain testing by low-level-based calibration. Extensive experiments on four target datasets validate our rationale and demonstrate that our method surpasses the state-of-the-art method in CDFSS signifcantly by 3.71% and 5.34% average MIoU in 1-shot and 5-shot scenarios, respectively.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Liu_The_Devil_is_in_Low-Level_Features_for_Cross-Domain_Few-Shot_Segmentation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Liu_The_Devil_is_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Liu_2025_CVPR,
    author    = {Liu, Yuhan and Zou, Yixiong and Li, Yuhua and Li, Ruixuan},
    title     = {The Devil is in Low-Level Features for Cross-Domain Few-Shot Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {4618-4627}
}

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


title: Design2GarmentCode: Turning Design Concepts to Tangible Garments Through Program Synthesis
authors: Feng Zhou, Ruiyang Liu, Chen Liu, Gaofeng He, Yong-Lu Li, Xiaogang Jin, Huamin Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 23712-23722
cities: ["Hangzhou", "Hangzhou", "Shanghai", "Hangzhou"]
countries: ["China", "China", "China", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhou_Design2GarmentCode_Turning_Design_Concepts_to_Tangible_Garments_Through_Program_Synthesis_CVPR_2025_paper.html

abstract:
Sewing patterns, the essential blueprints for fabric cutting and tailoring, act as a crucial bridge between design concepts and producible garments. However, existing uni-modal sewing pattern generation models struggle to effectively encode complex design concepts with a multi-modal nature and correlate them with vectorized sewing patterns that possess precise geometric structures and intricate sewing relations. In this work, we propose a novel sewing pattern generation approach Design2GarmentCode based on Large Multimodal Models (LMMs), to generate parametric pattern-making programs from multi-modal design concepts. LMM offers an intuitive interface for interpreting diverse design inputs, while pattern-making programs could serve as well-structured and semantically meaningful representations of sewing patterns, and act as a robust bridge connecting the cross-domain pattern-making knowledge embedded in LMMs with vectorized sewing patterns. Experimental results demonstrate that our method can flexibly handle various complex design expressions such as images, textual descriptions, designer sketches, or their combinations, and convert them into size-precise sewing patterns with correct stitches. Compared to previous methods, our approach significantly enhances training efficiency, generation quality, and authoring flexibility. Project page: https://style3d.github.io/design2garmentcode.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhou_Design2GarmentCode_Turning_Design_Concepts_to_Tangible_Garments_Through_Program_Synthesis_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Zhou_Design2GarmentCode_Turning_Design_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Zhou_2025_CVPR,
    author    = {Zhou, Feng and Liu, Ruiyang and Liu, Chen and He, Gaofeng and Li, Yong-Lu and Jin, Xiaogang and Wang, Huamin},
    title     = {Design2GarmentCode: Turning Design Concepts to Tangible Garments Through Program Synthesis},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {23712-23722}
}

raw_affiliation:
[{"affiliation": "Zhejiang Sci-Tech University", "city": "Hangzhou", "country": "China"}, {"affiliation": "Style3D Research", "city": "Hangzhou", "country": "China"}, {"affiliation": "Shanghai Jiao Tong University", "city": "Shanghai", "country": "China"}, {"affiliation": "State Key Lab of CAD&CG, Zhejiang University", "city": "Hangzhou", "country": "China"}]

affiliations:
["Zhejiang Sci-Tech University", "Style3D Research", "Shanghai Jiao Tong University", "State Key Lab of CAD&CG, Zhejiang University"]


title: Uncertainty Weighted Gradients for Model Calibration
authors: Jinxu Lin, Linwei Tao, Minjing Dong, Chang Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 15497-15507
affiliations: ["The University of Sydney", "City University of Hong Kong"]
cities: ["Sydney", "Hong Kong"]
countries: ["Australia", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Lin_Uncertainty_Weighted_Gradients_for_Model_Calibration_CVPR_2025_paper.html

abstract:
Model calibration is essential for ensuring that the predictions of deep neural networks accurately reflect true probabilities in real-world classification tasks. However, deep networks often produce over-confident or under-confident predictions, leading to miscalibration. Various methods have been proposed to address this issue by designing effective loss functions for calibration, such as focal loss. In this paper, we analyze its effectiveness and provide a unified loss framework of focal loss and its variants, where we mainly attribute their superiority in model calibration to the loss weighting factor that estimates sample-wise uncertainty. Based on our analysis, existing loss functions fail to achieve optimal calibration performance due to two main issues: including misalignment during optimization and insufficient precision in uncertainty estimation. Specifically, focal loss cannot align sample uncertainty with gradient scaling and the single logit cannot indicate the uncertainty. To address these issues, we reformulate the optimization from the perspective of gradients, which focuses on uncertain samples. Meanwhile, we propose using the Brier Score as the loss weight factor, which provides a more accurate uncertainty estimation via all the logits. Extensive experiments on various models and datasets demonstrate that our method achieves state-of-the-art (SOTA) performance.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Lin_Uncertainty_Weighted_Gradients_for_Model_Calibration_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Lin_Uncertainty_Weighted_Gradients_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Lin_2025_CVPR,
    author    = {Lin, Jinxu and Tao, Linwei and Dong, Minjing and Xu, Chang},
    title     = {Uncertainty Weighted Gradients for Model Calibration},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {15497-15507}
}

raw_affiliation:
[{"affiliation": "The University of Sydney", "city": "Sydney", "country": "Australia"}, {"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "China"}]


title: Efficient Dynamic Scene Editing via 4D Gaussian-based Static-Dynamic Separation
authors: Joohyun Kwon, Hanbyel Cho, Junmo Kim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26855-26865
affiliations: ["DGIST", "KAIST", "KAIST"]
cities: ["Daegu", "Daejeon", "Daejeon"]
countries: ["South Korea", "South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Kwon_Efficient_Dynamic_Scene_Editing_via_4D_Gaussian-based_Static-Dynamic_Separation_CVPR_2025_paper.html

abstract:
Recent 4D dynamic scene editing methods require editing thousands of 2D images used for dynamic scene synthesis and updating the entire scene with additional training loops, resulting in several hours of processing to edit a single dynamic scene. Therefore, these methods are not scalable with respect to the temporal dimension of the dynamic scene (i.e., the number of timesteps). In this work, we propose Instruct-4DGS, an efficient dynamic scene editing method that is more scalable in terms of temporal dimension. To achieve computational efficiency, we leverage a 4D Gaussian representation that models a 4D dynamic scene by combining static 3D Gaussians with a Hexplane-based deformation field, which captures dynamic information. We then perform editing solely on the static 3D Gaussians, which is the minimal but sufficient component required for visual editing. To resolve the misalignment between the edited 3D Gaussians and the deformation field, which may arise from the editing process, we introduce a refinement stage using a score distillation mechanism. Extensive editing results demonstrate that Instruct-4DGS is efficient, reducing editing time by more than half compared to existing methods while achieving high-quality edits that better follow user instructions.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Kwon_Efficient_Dynamic_Scene_Editing_via_4D_Gaussian-based_Static-Dynamic_Separation_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Kwon_Efficient_Dynamic_Scene_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Kwon_2025_CVPR,
    author    = {Kwon, Joohyun and Cho, Hanbyel and Kim, Junmo},
    title     = {Efficient Dynamic Scene Editing via 4D Gaussian-based Static-Dynamic Separation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26855-26865}
}

raw_affiliation:
[{"affiliation": "DGIST", "city": "Daegu", "country": "South Korea"}, {"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "KAIST", "city": "Daejeon", "country": "South Korea"}]


authors: Zhengyi Zhong, Weidong Bao, Ji Wang, Shuai Zhang, Jingxuan Zhou, Lingjuan Lyu, Wei Yang Bryan Lim
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30661-30670
cities: ["Changsha", "Tokyo", "Singapore"]
countries: ["China", "Japan", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Zhong_Unlearning_through_Knowledge_Overwriting_Reversible_Federated_Unlearning_via_Selective_Sparse_CVPR_2025_paper.html

title:
Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter

abstract:
Federated Learning is a promising paradigm for privacy-preserving collaborative model training. In practice, it is essential not only to continuously train the model to acquire new knowledge but also to guarantee old knowledge the right to be forgotten (i.e., federated unlearning), especially for privacy-sensitive information or harmful knowledge. However, current federated unlearning methods face several challenges, including indiscriminate unlearning of cross-client knowledge, irreversibility of unlearning, and significant unlearning costs. To this end, we propose a method named FUSED, which first identifies critical layers by analyzing each layer's sensitivity to knowledge and constructs sparse unlearning adapters for sensitive ones. Then, the adapters are trained without altering the original parameters, overwriting the unlearning knowledge with the remaining knowledge. This knowledge overwriting process enables FUSED to mitigate the effects of indiscriminate unlearning. Moreover, the introduction of independent adapters makes unlearning reversible and significantly reduces the unlearning costs. Finally, extensive experiments on five datasets across three unlearning scenarios demonstrate that FUSED's effectiveness is comparable to Retraining, surpassing all other baselines while greatly reducing unlearning costs.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Zhong_Unlearning_through_Knowledge_Overwriting_Reversible_Federated_Unlearning_via_Selective_Sparse_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Zhong_2025_CVPR,
    author    = {Zhong, Zhengyi and Bao, Weidong and Wang, Ji and Zhang, Shuai and Zhou, Jingxuan and Lyu, Lingjuan and Lim, Wei Yang Bryan},
    title     = {Unlearning through Knowledge Overwriting: Reversible Federated Unlearning via Selective Sparse Adapter},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30661-30670}
}

raw_affiliation:
[{"affiliation": "Laboratory for Big Data and Decision, National University of Defense T echnology", "city": "Changsha", "country": "China"}, {"affiliation": "Sony AI", "city": "Tokyo", "country": "Japan"}, {"affiliation": "Nanyang T echnological University", "city": "Singapore", "country": "Singapore"}]

affiliations:
["Laboratory for Big Data and Decision, National University of Defense T echnology", "Sony AI", "Nanyang T echnological University"]


title: SocialMOIF: Multi-Order Intention Fusion for Pedestrian Trajectory Prediction
authors: Kai Chen, Xiaodong Zhao, Yujie Huang, Guoyu Fang, Xiao Song, Ruiping Wang, Ziyuan Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22465-22475
cities: ["Nanjing", "Beijing", "Singapore"]
countries: ["China", "China", "Singapore"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chen_SocialMOIF_Multi-Order_Intention_Fusion_for_Pedestrian_Trajectory_Prediction_CVPR_2025_paper.html

abstract:
The analysis and prediction of agent trajectories are crucial for decision-making processes in intelligent systems, with precise short-term trajectory forecasting being highly significant across a range of applications. Agents and their social interactions have been quantified and modeled by researchers from various perspectives; however, substantial limitations exist in the current work due to the inherent high uncertainty of agent intentions and the complex higher-order influences among neighboring groups. SocialMOIF is proposed to tackle these challenges, concentrating on the higher-order intention interactions among neighboring groups while reinforcing the primary role of first-order intention interactions between neighbors and the target agent. This method develops a multi-order intention fusion model to achieve a more comprehensive understanding of both direct and indirect intention information. Within SocialMOIF, a trajectory distribution approximator is designed to guide the trajectories toward values that align more closely with the actual data, thereby enhancing model interpretability. Furthermore, a global trajectory optimizer is introduced to enable more accurate and efficient parallel predictions. By incorporating a novel loss function that accounts for distance and direction during training, experimental results demonstrate that the model outperforms previous state-of-the-art baselines across multiple metrics in both dynamic and static datasets.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chen_SocialMOIF_Multi-Order_Intention_Fusion_for_Pedestrian_Trajectory_Prediction_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chen_SocialMOIF_Multi-Order_Intention_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chen_2025_CVPR,
    author    = {Chen, Kai and Zhao, Xiaodong and Huang, Yujie and Fang, Guoyu and Song, Xiao and Wang, Ruiping and Wang, Ziyuan},
    title     = {SocialMOIF: Multi-Order Intention Fusion for Pedestrian Trajectory Prediction},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22465-22475}
}

raw_affiliation:
[{"affiliation": "Nanjing University of Aeronautics and Astronautics", "city": "Nanjing", "country": "China"}, {"affiliation": "Beihang University", "city": "Beijing", "country": "China"}, {"affiliation": "Nanyang Technological University", "city": "Singapore", "country": "Singapore"}]

affiliations:
["Nanjing University of Aeronautics and Astronautics", "Beihang University", "Nanyang Technological University"]


title: FFaceNeRF: Few-shot Face Editing in Neural Radiance Fields
authors: Kwan Yun, Chaelin Kim, Hangyeul Shin, Junyong Noh
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10825-10835
affiliations: ["KAIST, Visual Media Lab", "Handong Global University"]
cities: ["Daejeon", "Pohang"]
countries: ["South Korea", "South Korea"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Yun_FFaceNeRF_Few-shot_Face_Editing_in_Neural_Radiance_Fields_CVPR_2025_paper.html

abstract:
Recent 3D face editing methods using masks have produced high-quality edited images by leveraging Neural Radiance Fields (NeRF). Despite their impressive performance, existing methods often provide limited user control due to the use of pre-trained segmentation masks. To utilize masks with a desired layout, an extensive training dataset is required, which is challenging to gather. We present FFaceNeRF, a NeRF-based face editing technique that can overcome the challenge of limited user control due to the use of fixed mask layouts. Our method employs a geometry adapter with feature injection, allowing for effective manipulation of geometry attributes. Additionally, we adopt latent mixing for tri-plane augmentation, which enables training with a few samples. This facilitates rapid model adaptation to desired mask layouts, crucial for applications in fields like personalized medical imaging or creative face editing. Our comparative evaluations demonstrate that FFaceNeRF surpasses existing mask based face editing methods in terms of flexibility, control, and generated image quality, paving the way for future advancements in customized and high-fidelity 3D face editing.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Yun_FFaceNeRF_Few-shot_Face_Editing_in_Neural_Radiance_Fields_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Yun_FFaceNeRF_Few-shot_Face_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Yun_2025_CVPR,
    author    = {Yun, Kwan and Kim, Chaelin and Shin, Hangyeul and Noh, Junyong},
    title     = {FFaceNeRF: Few-shot Face Editing in Neural Radiance Fields},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10825-10835}
}

raw_affiliation:
[{"affiliation": "KAIST, Visual Media Lab", "city": "Daejeon", "country": "South Korea"}, {"affiliation": "Handong Global University", "city": "Pohang", "country": "South Korea"}]


title: Discrete to Continuous: Generating Smooth Transition Poses from Sign Language Observations
authors: Shengeng Tang, Jiayi He, Lechao Cheng, Jingjing Wu, Dan Guo, Richang Hong
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 3481-3491
affiliations: ["School of Computer Science and Information Engineering, Hefei University of Technology"]
cities: ["Hefei"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Tang_Discrete_to_Continuous_Generating_Smooth_Transition_Poses_from_Sign_Language_CVPR_2025_paper.html

abstract:
Generating continuous sign language videos from discrete segments is challenging due to the need for smooth transitions that preserve natural flow and meaning. Traditional approaches that simply concatenate isolated signs often result in abrupt transitions, disrupting video coherence. To address this, we propose a novel framework, Sign-D2C, that employs a conditional diffusion model to synthesize contextually smooth transition frames, enabling the seamless construction of continuous sign language sequences. Our approach transforms the unsupervised problem of transition frame generation into a supervised training task by simulating the absence of transition frames through random masking of segments in long-duration sign videos. The model learns to predict these masked frames by denoising Gaussian noise, conditioned on the surrounding sign observations, allowing it to handle complex, unstructured transitions. During inference, we apply a linearly interpolating padding strategy that initializes missing frames through interpolation between boundary frames, providing a stable foundation for iterative refinement by the diffusion model. Extensive experiments on the PHOENIX14T, USTC-CSL100, and USTC-SLR500 datasets demonstrate the effectiveness of our method in producing continuous, natural sign language videos.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Tang_Discrete_to_Continuous_Generating_Smooth_Transition_Poses_from_Sign_Language_CVPR_2025_paper.pdf

bibtex:
@InProceedings{Tang_2025_CVPR,
    author    = {Tang, Shengeng and He, Jiayi and Cheng, Lechao and Wu, Jingjing and Guo, Dan and Hong, Richang},
    title     = {Discrete to Continuous: Generating Smooth Transition Poses from Sign Language Observations},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {3481-3491}
}

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


authors: Farchan Hakim Raswa, Chun-Shien Lu, Jia-Ching Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 30251-30260
affiliations: ["CSIE, National Central University, Taiwan, ROC", "IIS, Academia Sinica, Taiwan, ROC"]
cities: ["Taoyuan City", "Taipei"]
countries: ["Taiwan", "Taiwan"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Raswa_HistoFS_Non-IID_Histopathologic_Whole_Slide_Image_Classification_via_Federated_Style_CVPR_2025_paper.html

title:
HistoFS: Non-IID Histopathologic Whole Slide Image Classification via Federated Style Transfer with RoI-Preserving

abstract:
Federated learning for pathological whole slide image (WSI) classification allows multiple clients to train a global multiple instance learning (MIL) model without sharing their privacy-sensitive WSIs. To accommodate the non-independent and identically distributed (non-i.i.d.) feature shifts, cross-client style transfer has been popularly used but is subject to two fundamental issues: (1) WSI contains multiple morphological structures, each corresponding to a distinct style. (2) Performing style transfer may potentially shift the region of interests (RoIs) in the augmented WSIs. To address these challenges, we propose HistoFS, a federated learning framework for computational pathology on non-i.i.d. feature shifts in WSI classification. Specifically, we introduce pseudo bag styles that capture multiple style variations within a single WSI. In addition, an authenticity module is introduced to ensure that RoIs are preserved, allowing local models to learn WSIs with diverse styles while maintaining essential RoIs. Extensive experiments validate the superiority of HistoFS over state-of-the-art methods on three clinical datasets. Our code is available at https://lalakitchen.github.io/HistoFS/.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Raswa_HistoFS_Non-IID_Histopathologic_Whole_Slide_Image_Classification_via_Federated_Style_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Raswa_HistoFS_Non-IID_Histopathologic_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Raswa_2025_CVPR,
    author    = {Raswa, Farchan Hakim and Lu, Chun-Shien and Wang, Jia-Ching},
    title     = {HistoFS: Non-IID Histopathologic Whole Slide Image Classification via Federated Style Transfer with RoI-Preserving},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {30251-30260}
}

raw_affiliation:
[{"affiliation": "CSIE, National Central University, Taiwan, ROC", "city": "Taoyuan City", "country": "Taiwan"}, {"affiliation": "IIS, Academia Sinica, Taiwan, ROC", "city": "Taipei", "country": "Taiwan"}]


title: Unified Medical Lesion Segmentation via Self-referring Indicator
authors: Shijie Chang, Xiaoqi Zhao, Lihe Zhang, Tiancheng Wang
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 10414-10424
raw_affiliation: [{"affiliation": "Dalian University of Technology", "city": "Dalian", "country": "China"}]
affiliations: ["Dalian University of Technology"]
cities: ["Dalian"]
countries: ["China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Chang_Unified_Medical_Lesion_Segmentation_via_Self-referring_Indicator_CVPR_2025_paper.html

abstract:
The recently emerged in-context-learning-based (ICL-based) models have the potential towards the unification of medical lesion segmentation. However, due to their cross-fusion designs, existing ICL-based unified segmentation models fail to accurately localize lesions with low-matched reference sets. Considering that the query itself can be regarded as a high-matched reference, which better indicates the target, we design a self-referencing mechanism that adaptively extracts self-referring indicator vectors from the query based on coarse predictions, thus effectively overcoming the negative impact caused by low-match reference sets. To further facilitate the self-referring mechanism, we introduce reference indicator generation to efficiently extract reference information for coarse predictions instead of using cross-fusion modules, which heavily rely on reference sets. Our designs successfully address the challenges of applying ICL to unified medical lesion segmentation, forming a novel framework named SR-ICL. Our method achieves state-of-the-art results on 8 medical lesion segmentation tasks with only 4 image-mask pairs as reference. Notably, SR-ICL still accomplishes remarkable performance even when using weak reference annotations such as boxes and points, and maintains fixed and low memory consumption even if more tasks are combined. We hope that SR-ICL can provide new insights for the clinical application of medical lesion segmentation.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Chang_Unified_Medical_Lesion_Segmentation_via_Self-referring_Indicator_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Chang_Unified_Medical_Lesion_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Chang_2025_CVPR,
    author    = {Chang, Shijie and Zhao, Xiaoqi and Zhang, Lihe and Wang, Tiancheng},
    title     = {Unified Medical Lesion Segmentation via Self-referring Indicator},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {10414-10424}
}


title: SGSST: Scaling Gaussian Splatting Style Transfer
authors: Bruno Galerne, Jianling Wang, Lara Raad, Jean-Michel Morel
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 26535-26544
cities: ["Orl\u00e9ans", "Paris", "Montevideo", "Hong Kong"]
countries: ["France", "France", "Uruguay", "Hong Kong"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Galerne_SGSST_Scaling_Gaussian_Splatting_Style_Transfer_CVPR_2025_paper.html

abstract:
Applying style transfer to a full 3D environment is a challenging task that has seen many developments since the advent of neural rendering. 3D Gaussian splatting (3DGS) has recently pushed further many limits of neural rendering in terms of training speed and reconstruction quality. This work introduces SGSST: Scaling Gaussian Splatting Style Transfer, an optimization-based method to apply style transfer to pretrained 3DGS scenes. We demonstrate that a new multiscale loss based on global neural statistics, that we name SOS for Simultaneously Optimized Scales, enables style transfer to ultra-high resolution 3D scenes. Not only SGSST pioneers 3D scene style transfer at such high image resolutions, it also produces superior visual quality as assessed by thorough qualitative, quantitative and perceptual comparisons.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Galerne_SGSST_Scaling_Gaussian_Splatting_Style_Transfer_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Galerne_SGSST_Scaling_Gaussian_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Galerne_2025_CVPR,
    author    = {Galerne, Bruno and Wang, Jianling and Raad, Lara and Morel, Jean-Michel},
    title     = {SGSST: Scaling Gaussian Splatting Style Transfer},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {26535-26544}
}

raw_affiliation:
[{"affiliation": "Universit\u00b4e d\u2019Orl \u00b4eans, Universit \u00b4e de T ours, CNRS, IDP , UMR 7013, Orl \u00b4eans, France", "city": "Orl\u00e9ans", "country": "France"}, {"affiliation": "Institut Universitaire de France (IUF)", "city": "Paris", "country": "France"}, {"affiliation": "Instituto de Ingenier \u00b4\u0131a El \u00b4ectrica, Facultad de Ingenier \u00b4\u0131a, Universidad de la Rep \u00b4ublica", "city": "Montevideo", "country": "Uruguay"}, {"affiliation": "City University of Hong Kong", "city": "Hong Kong", "country": "Hong Kong"}]

affiliations:
["Universit\u00b4e d\u2019Orl \u00b4eans, Universit \u00b4e de T ours, CNRS, IDP , UMR 7013, Orl \u00b4eans, France", "Institut Universitaire de France (IUF)", "Instituto de Ingenier \u00b4\u0131a El \u00b4ectrica, Facultad de Ingenier \u00b4\u0131a, Universidad de la Rep \u00b4ublica", "City University of Hong Kong"]


authors: Takeshi Noda, Chao Chen, Junsheng Zhou, Weiqi Zhang, Yu-Shen Liu, Zhizhong Han
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 22139-22149
cities: ["Beijing", "Detroit"]
countries: ["China", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Noda_Learning_Bijective_Surface_Parameterization_for_Inferring_Signed_Distance_Functions_from_CVPR_2025_paper.html

title:
Learning Bijective Surface Parameterization for Inferring Signed Distance Functions from Sparse Point Clouds with Grid Deformation

abstract:
Inferring signed distance functions (SDFs) from sparse point clouds remains a challenge in surface reconstruction. The key lies in the lack of detailed geometric information in sparse point clouds, which is essential for learning a continuous field. To resolve this issue, we present a novel approach that learns a dynamic deformation network to predict SDFs in an end-to-end manner. To parameterize a continuous surface from sparse points, we propose a bijective surface parameterization (BSP) that learns the global shape from local patches. Specifically, we construct a bijective mapping for sparse points from the parametric domain to 3D local patches, integrating patches into the global surface. Meanwhile, we introduce grid deformation optimization (GDO) into the surface approximation to optimize the deformation of grid points and further refine the parametric surfaces. Experimental results on synthetic and real scanned datasets demonstrate that our method significantly outperforms the current state-of-the-art methods.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Noda_Learning_Bijective_Surface_Parameterization_for_Inferring_Signed_Distance_Functions_from_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Noda_Learning_Bijective_Surface_CVPR_2025_supplemental.zip

bibtex:
@InProceedings{Noda_2025_CVPR,
    author    = {Noda, Takeshi and Chen, Chao and Zhou, Junsheng and Zhang, Weiqi and Liu, Yu-Shen and Han, Zhizhong},
    title     = {Learning Bijective Surface Parameterization for Inferring Signed Distance Functions from Sparse Point Clouds with Grid Deformation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {22139-22149}
}

raw_affiliation:
[{"affiliation": "School of Software, Tsinghua University", "city": "Beijing", "country": "China"}, {"affiliation": "Department of Computer Science, Wayne State University", "city": "Detroit", "country": "USA"}]

affiliations:
["School of Software, Tsinghua University", "Department of Computer Science, Wayne State University"]


title: Minimizing Labeled, Maximizing Unlabeled: An Image-Driven Approach for Video Instance Segmentation
authors: Fangyun Wei, Jinjing Zhao, Kun Yan, Chang Xu
proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 19304-19314
affiliations: ["The University of Sydney", "Peking University"]
cities: ["Sydney", "Beijing"]
countries: ["Australia", "China"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/Wei_Minimizing_Labeled_Maximizing_Unlabeled_An_Image-Driven_Approach_for_Video_Instance_CVPR_2025_paper.html

abstract:
Traditional video instance segmentation (VIS) models rely on extensive per-frame video annotations, which are both time-consuming and costly. In this paper, we present MinMaxVIS, a novel VIS framework that reduces the dependency on fully labeled video datasets by utilizing a small set of labeled images from the target domain along with a large volume of general-domain, unlabeled images. MinMaxVIS operates in three stages: first, a preliminary segmentation model is trained on the small labeled set from the target domain; this model then retrieves relevant instances from the unlabeled dataset to build a high-quality pseudo-labeled set, ensuring a rich content alignment with the target domain while avoiding the inefficiencies of large-scale semi-supervised learning across the entire unlabeled set. Finally, we train MinMaxVIS on a combination of labeled and pseudo-labeled data, addressing challenges such as noise in pseudo-labels and instance association across frames. To simulate object continuity, we augment static images to create paired frames, allowing MinMaxVIS to capture instance associations effectively. MinMaxVIS outperforms the prior image-driven approach, MinVIS, achieving superior mAP scores with significantly reduced labeled data. For instance, MinMaxVIS with a Swin-L backbone attains 62.2 mAP on YouTube-VIS 2019 using only 2% labeled data and additional unlabeled images from SA-1B. This surpasses MinVIS, which uses the same backbone trained on fully labeled YouTube-VIS 2019, by 0.6 mAP.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/Wei_Minimizing_Labeled_Maximizing_Unlabeled_An_Image-Driven_Approach_for_Video_Instance_CVPR_2025_paper.pdf

supp_link:
https://openaccess.thecvf.com/content/CVPR2025/supplemental/Wei_Minimizing_Labeled_Maximizing_CVPR_2025_supplemental.pdf

bibtex:
@InProceedings{Wei_2025_CVPR,
    author    = {Wei, Fangyun and Zhao, Jinjing and Yan, Kun and Xu, Chang},
    title     = {Minimizing Labeled, Maximizing Unlabeled: An Image-Driven Approach for Video Instance Segmentation},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {19304-19314}
}

raw_affiliation:
[{"affiliation": "The University of Sydney", "city": "Sydney", "country": "Australia"}, {"affiliation": "Peking University", "city": "Beijing", "country": "China"}]


proceedings_name: Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)
proceedings_year: 2025
proceedings_pages: pp. 18072-18082
affiliations: ["Georgia Institute of Technology", "Adobe Research"]
cities: ["Atlanta", "San Jose"]
countries: ["USA", "USA"]
html_link:
https://openaccess.thecvf.com/content/CVPR2025/html/You_Layer-_and_Timestep-Adaptive_Differentiable_Token_Compression_Ratios_for_Efficient_Diffusion_CVPR_2025_paper.html

title:
Layer- and Timestep-Adaptive Differentiable Token Compression Ratios for Efficient Diffusion Transformers

authors:
Haoran You, Connelly Barnes, Yuqian Zhou, Yan Kang, Zhenbang Du, Wei Zhou, Lingzhi Zhang, Yotam Nitzan, Xiaoyang Liu, Zhe Lin, Eli Shechtman, Sohrab Amirghodsi, Yingyan Celine Lin

abstract:
Diffusion Transformers (DiTs) have achieved state-of-the-art (SOTA) image generation quality but suffer from high latency and memory inefficiency, making them difficult to deploy on resource-constrained devices. One major efficiency bottleneck is that existing DiTs apply equal computation across all regions of an image. However, not all image tokens are equally important, and certain localized areas require more computation, such as objects. To address this, we propose DiffCR, a dynamic DiT inference framework with differentiable compression ratios, which automatically learns to dynamically route computation across layers and timesteps for each image token, resulting in efficient DiTs. Specifically, DiffCR integrates three features: (1) A token-level routing scheme where each DiT layer includes a router that is fine-tuned jointly with model weights to predict token importance scores. In this way, unimportant tokens bypass the entire layer's computation; (2) A layer-wise differentiable ratio mechanism where different DiT layers automatically learn varying compression ratios from a zero initialization, resulting in large compression ratios in redundant layers while others remain less compressed or even uncompressed; (3) A timestep-wise differentiable ratio mechanism where each denoising timestep learns its own compression ratio. The resulting pattern shows higher ratios for noisier timesteps and lower ratios as the image becomes clearer. Extensive experiments on text-to-image and inpainting tasks show that DiffCR effectively captures dynamism across token, layer, and timestep axes, achieving superior trade-offs between generation quality and efficiency compared to prior works. The project website is available at https://www.haoranyou.com/diffcr.

pdf_link:
https://openaccess.thecvf.com/content/CVPR2025/papers/You_Layer-_and_Timestep-Adaptive_Differentiable_Token_Compression_Ratios_for_Efficient_Diffusion_CVPR_2025_paper.pdf

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

bibtex:
@InProceedings{You_2025_CVPR,
    author    = {You, Haoran and Barnes, Connelly and Zhou, Yuqian and Kang, Yan and Du, Zhenbang and Zhou, Wei and Zhang, Lingzhi and Nitzan, Yotam and Liu, Xiaoyang and Lin, Zhe and Shechtman, Eli and Amirghodsi, Sohrab and Lin, Yingyan Celine},
    title     = {Layer- and Timestep-Adaptive Differentiable Token Compression Ratios for Efficient Diffusion Transformers},
    booktitle = {Proceedings of the Computer Vision and Pattern Recognition Conference (CVPR)},
    month     = {June},
    year      = {2025},
    pages     = {18072-18082}
}

raw_affiliation:
[{"affiliation": "Georgia Institute of Technology", "city": "Atlanta", "country": "USA"}, {"affiliation": "Adobe Research", "city": "San Jose", "country": "USA"}]


