IEEE Transactions on Pattern Analysis and Machine Intelligence最新文献_第9页

MotionLLM: Understanding Human Behaviors from Human Motions and Videos MotionLLM：从人体运动和视频中理解人类行为

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-10-31 DOI: 10.1109/tpami.2025.3627546

Ling-Hao Chen, Shunlin Lu, Ailing Zeng, Hao Zhang, Benyou Wang, Ruimao Zhang, Lei Zhang

引用次数: 0

DreamScene: 3D Gaussian-based End-to-end Text-to-3D Scene Generation DreamScene: 3D基于高斯的端到端文本到3D场景生成

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-10-15 DOI: 10.1109/tpami.2025.3620403

Haoran Li, Yuli Tian, Kun Lan, Yong Liao, Lin Wang, Pan Hui, Peng Yuan Zhou

引用次数: 0

HeadArtist-VL: Vision / Language Guided 3D Head Generation with Self Score Distillation HeadArtist-VL：视觉/语言引导3D头部生成与自我评分蒸馏

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-10-10 DOI: 10.1109/tpami.2025.3619576

Hongyu Liu, Xuan Wang, Ziyu Wan, Yujun Shen, Yibing Song, Jing Liao, Qifeng Chen

引用次数: 0

Lodge++: High-Quality and Long Dance Generation With Robust Choreography Patterns 洛奇++：以稳健的编舞模式生成高质量、长时间的舞蹈

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-10-06 DOI: 10.1109/tpami.2025.3618016

Ronghui Li, Hongwen Zhang, Yachao Zhang, Yuxiang Zhang, Youliang Zhang, Jie Guo, Yan Zhang, Xiu Li, Yebin Liu

引用次数: 0

ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis ViewCrafter：训练视频扩散模型用于高保真新视图合成

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-09-23 DOI: 10.1109/tpami.2025.3613256

Wangbo Yu, Jinbo Xing, Li Yuan, Wenbo Hu, Xiaoyu Li, Zhipeng Huang, Xiangjun Gao, Tien-Tsin Wong, Ying Shan, Yonghong Tian

引用次数: 0

DreamReward-X: Boosting High-Quality 3D Generation with Human Preference Alignment. DreamReward-X：促进高质量的3D生成与人类偏好对齐。

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-09-15 DOI: 10.1109/tpami.2025.3609680

Fangfu Liu,Junliang Ye,Yikai Wang,Hanyang Wang,Zhengyi Wang,Jun Zhu,Yueqi Duan

{"title":"DreamReward-X: Boosting High-Quality 3D Generation with Human Preference Alignment.","authors":"Fangfu Liu,Junliang Ye,Yikai Wang,Hanyang Wang,Zhengyi Wang,Jun Zhu,Yueqi Duan","doi":"10.1109/tpami.2025.3609680","DOIUrl":"https://doi.org/10.1109/tpami.2025.3609680","url":null,"abstract":"Recent advancements in 3D content generation have shown remarkable success by leveraging pretrained large-scale diffusion models. However, existing 3D generation results are far from perfect as one primary challenge lies in aligning 3D content with human preference, especially in text-driven 3D generation. In this paper, we propose a novel 3D generation framework, coined DreamReward, to learn and improve text-driven 3D generation models from human preference feedback. First, we collect 25K+ expert comparisons based on a systematic annotation pipeline including filtering, rating, and ranking. Then, we build Reward3D, the first general-purpose text-to-3D human preference reward model to encode human preferences effectively. Building upon the 3D reward model, we finally perform theoretical analysis and present the Reward3D Feedback Learning (DreamFL) algorithm to guide the noisy pretrained distribution toward the actual user-prompt distributions in optimization. With the rapid development and growing popularity of 4D and image-driven 3D generation, we further extend our DreamReward into 4D generation (DreamReward-4D) and image-to-3D generation (DreamReward-img) in a low-cost but effective manner. Despite the impressive results created by DreamReward, the diversity in text-driven 3D generation is limited due to inherent maximum likelihood-seeking issues. To address this, we explore the gap between Denoising Diffusion Implicit Models (DDIM) and SDS-based DreamFL in the generation process and propose DreamReward++, where we introduce a reward-aware noise sampling strategy to unleash text-driven diversity during the generation process while ensuring human preference alignment. Grounded by theoretical proof and extensive experiment comparisons, our method successfully generates high-fidelity and diverse 3D results with significant boosts in prompt alignment with human intention. Our results demonstrate the great potential for learning from human feedback to improve 3D generation.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"73 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145068470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Combo: Co-speech holistic 3D human motion generation and efficient customizable adaptation in harmony. Combo：协同语音整体3D人体运动生成和高效的自定义适应和谐。

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-09-09 DOI: 10.1109/tpami.2025.3607711

Chao Xu,Mingze Sun,Zhi-Qi Cheng,Fei Wang,Yang Liu,Baigui Sun,Ruqi Huang,Alexander Hauptmann

{"title":"Combo: Co-speech holistic 3D human motion generation and efficient customizable adaptation in harmony.","authors":"Chao Xu,Mingze Sun,Zhi-Qi Cheng,Fei Wang,Yang Liu,Baigui Sun,Ruqi Huang,Alexander Hauptmann","doi":"10.1109/tpami.2025.3607711","DOIUrl":"https://doi.org/10.1109/tpami.2025.3607711","url":null,"abstract":"In this paper, we propose a novel framework, Combo, for harmonious co-speech holistic 3D human motion generation and efficient customizable adaption. In particular, we identify that one fundamental challenge as the multiple-input-multiple-output (MIMO) nature of the generative model of interest. More concretely, on the input end, the model typically consumes both speech signals and character guidance (e.g., identity and emotion), which hinders further adaptation to varying guidance; on the output end, holistic human motions mainly consist of facial expressions and body movements, which are inherently correlated but non-trivial to coordinate in current data-driven generation process. In response to the above challenge, we propose tailored designs to both ends. For the former, we propose to pre-train on data regarding a fixed identity with neutral emotion, and defer the incorporation of customizable conditions (identity and emotion) to fine-tuning stage, which is boosted by our novel X-Adapter for parameter-efficient fine-tuning. For the latter, we propose a simple yet effective transformer design, DU-Trans, which first divides into two branches to learn individual features of face expression and body movements, and then unites those to learn a joint bi-directional distribution and directly predicts combined coefficients. Evaluated on BEAT2 and SHOW datasets, Combo is highly effective in generating high-quality motions but also efficient in transferring identity and emotion. Project website: https://xc-csc101.github.io/combo/.","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"56 1","pages":""},"PeriodicalIF":23.6,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145025288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MCGS: Multiview Consistency Enhancement for Sparse-View 3D Gaussian Radiance Fields MCGS：稀疏视图3D高斯辐射场的多视图一致性增强

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-09-08 DOI: 10.1109/tpami.2025.3607103

Yuru Xiao, Deming Zhai, Wenbo Zhao, Kui Jiang, Junjun Jiang, Xianming Liu

引用次数: 0

Supervision by Denoising. 去噪监督

IF 20.4 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-09-01 DOI: 10.1109/TPAMI.2023.3299789

Sean I Young, Adrian V Dalca, Enzo Ferrante, Polina Golland, Christopher A Metzler, Bruce Fischl, Juan Eugenio Iglesias

{"title":"Supervision by Denoising.","authors":"Sean I Young, Adrian V Dalca, Enzo Ferrante, Polina Golland, Christopher A Metzler, Bruce Fischl, Juan Eugenio Iglesias","doi":"10.1109/TPAMI.2023.3299789","DOIUrl":"10.1109/TPAMI.2023.3299789","url":null,"abstract":"<p><p>Learning-based image reconstruction models, such as those based on the U-Net, require a large set of labeled images if good generalization is to be guaranteed. In some imaging domains, however, labeled data with pixel- or voxel-level label accuracy are scarce due to the cost of acquiring them. This problem is exacerbated further in domains like medical imaging, where there is no single ground truth label, resulting in large amounts of repeat variability in the labels. Therefore, training reconstruction networks to generalize better by learning from both labeled and unlabeled examples (called semi-supervised learning) is problem of practical and theoretical interest. However, traditional semi-supervised learning methods for image reconstruction often necessitate handcrafting a differentiable regularizer specific to some given imaging problem, which can be extremely time-consuming. In this work, we propose \"supervision by denoising\" (SUD), a framework to supervise reconstruction models using their own denoised output as labels. SUD unifies stochastic averaging and spatial denoising techniques under a spatio-temporal denoising framework and alternates denoising and model weight update steps in an optimization framework for semi-supervision. As example applications, we apply SUD to two problems from biomedical imaging-anatomical brain reconstruction (3D) and cortical parcellation (2D)-to demonstrate a significant improvement in reconstruction over supervised-only and ensembling baselines.</p>","PeriodicalId":13426,"journal":{"name":"IEEE Transactions on Pattern Analysis and Machine Intelligence","volume":"PP ","pages":"7279-7291"},"PeriodicalIF":20.4,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12498241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9958188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

NaviNeRF++: Towards Interpretable 3D Reconstruction via Unsupervised Disentangled Representation Learning navinerf++：通过无监督解纠缠表示学习实现可解释的3D重建

IF 23.6 1区计算机科学

IEEE Transactions on Pattern Analysis and Machine Intelligence Pub Date : 2025-08-28 DOI: 10.1109/tpami.2025.3601145

Baao Xie, Zequn Zhang, Huanting Guo, Qiuyu Chen, Hu Zhu, Bohan Li, Wenjun Zeng, Xin Jin

引用次数: 0