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Related papers: FlexMotion: Lightweight, Physics-Aware, and Contro…

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Creating expressive character animations is labor-intensive, requiring intricate manual adjustment of animators across space and time. Previous works on controllable motion generation often rely on a predefined set of dense spatio-temporal…

Graphics · Computer Science 2025-07-28 Inwoo Hwang , Jinseok Bae , Donggeun Lim , Young Min Kim

Text-conditioned motion synthesis has made remarkable progress with the emergence of diffusion models. However, the majority of these motion diffusion models are primarily designed for a single character and overlook multi-human…

Computer Vision and Pattern Recognition · Computer Science 2024-11-22 Zhenzhi Wang , Jingbo Wang , Yixuan Li , Dahua Lin , Bo Dai

Human motion generation is a significant pursuit in generative computer vision with widespread applications in film-making, video games, AR/VR, and human-robot interaction. Current methods mainly utilize either diffusion-based generative…

Computer Vision and Pattern Recognition · Computer Science 2025-02-03 Canxuan Gang

Real-time synthesis of physically plausible human interactions remains a critical challenge for immersive VR/AR systems and humanoid robotics. While existing methods demonstrate progress in kinematic motion generation, they often fail to…

Computer Vision and Pattern Recognition · Computer Science 2025-08-05 Kaiyang Ji , Ye Shi , Zichen Jin , Kangyi Chen , Lan Xu , Yuexin Ma , Jingyi Yu , Jingya Wang

Hands are central to interacting with our surroundings and conveying gestures, making their inclusion essential for full-body motion synthesis. Despite this, existing human motion synthesis methods fall short: some ignore hand motions…

Computer Vision and Pattern Recognition · Computer Science 2026-01-08 Enes Duran , Nikos Athanasiou , Muhammed Kocabas , Michael J. Black , Omid Taheri

Human motion generation has been widely studied due to its crucial role in areas such as digital humans and humanoid robot control. However, many current motion generation approaches disregard physics constraints, frequently resulting in…

Computer Vision and Pattern Recognition · Computer Science 2026-03-26 Zhuo Li , Mingshuang Luo , Ruibing Hou , Xin Zhao , Hao Liu , Hong Chang , Zimo Liu , Chen Li

Controllable generation of 3D human motions becomes an important topic as the world embraces digital transformation. Existing works, though making promising progress with the advent of diffusion models, heavily rely on meticulously captured…

Computer Vision and Pattern Recognition · Computer Science 2024-01-25 Nhat M. Hoang , Kehong Gong , Chuan Guo , Michael Bi Mi

Thanks to the powerful generative capacity of diffusion models, recent years have witnessed rapid progress in human motion generation. Existing diffusion-based methods employ disparate network architectures and training strategies. The…

Computer Vision and Pattern Recognition · Computer Science 2024-12-10 Yiheng Huang , Hui Yang , Chuanchen Luo , Yuxi Wang , Shibiao Xu , Zhaoxiang Zhang , Man Zhang , Junran Peng

Recent advancements in diffusion models have significantly improved the realism and generalizability of character-driven animation, enabling the synthesis of high-quality motion from just a single RGB image and a set of driving poses.…

Computer Vision and Pattern Recognition · Computer Science 2025-12-02 Alireza Javanmardi , Pragati Jaiswal , Tewodros Amberbir Habtegebrial , Christen Millerdurai , Shaoxiang Wang , Alain Pagani , Didier Stricker

Natural and expressive human motion generation is the holy grail of computer animation. It is a challenging task, due to the diversity of possible motion, human perceptual sensitivity to it, and the difficulty of accurately describing it.…

Computer Vision and Pattern Recognition · Computer Science 2022-10-04 Guy Tevet , Sigal Raab , Brian Gordon , Yonatan Shafir , Daniel Cohen-Or , Amit H. Bermano

This paper tackles the problem of physics-aware human motion synthesis in a dynamic scene. Unlike existing works which mainly tend to generate physically unrealistic motions due to limited contact modeling, typically restricted to hands, in…

Computer Vision and Pattern Recognition · Computer Science 2026-05-05 Chaoyue Xing , Wei Mao , Miaomiao Liu

Denoising diffusion models hold great promise for generating diverse and realistic human motions. However, existing motion diffusion models largely disregard the laws of physics in the diffusion process and often generate…

Computer Vision and Pattern Recognition · Computer Science 2023-08-22 Ye Yuan , Jiaming Song , Umar Iqbal , Arash Vahdat , Jan Kautz

We present a novel approach named OmniControl for incorporating flexible spatial control signals into a text-conditioned human motion generation model based on the diffusion process. Unlike previous methods that can only control the pelvis…

Computer Vision and Pattern Recognition · Computer Science 2024-04-16 Yiming Xie , Varun Jampani , Lei Zhong , Deqing Sun , Huaizu Jiang

Motion control is crucial for generating expressive and compelling video content; however, most existing video generation models rely mainly on text prompts for control, which struggle to capture the nuances of dynamic actions and temporal…

Generating human motion from textual descriptions is a challenging task. Existing methods either struggle with physical credibility or are limited by the complexities of physics simulations. In this paper, we present \emph{ReinDiffuse} that…

Computer Vision and Pattern Recognition · Computer Science 2024-10-16 Gaoge Han , Mingjiang Liang , Jinglei Tang , Yongkang Cheng , Wei Liu , Shaoli Huang

This paper introduces OmniMotion-X, a versatile multimodal framework for whole-body human motion generation, leveraging an autoregressive diffusion transformer in a unified sequence-to-sequence manner. OmniMotion-X efficiently supports…

Computer Vision and Pattern Recognition · Computer Science 2025-10-23 Guowei Xu , Yuxuan Bian , Ailing Zeng , Mingyi Shi , Shaoli Huang , Wen Li , Lixin Duan , Qiang Xu

Human motion modeling is important for many modern graphics applications, which typically require professional skills. In order to remove the skill barriers for laymen, recent motion generation methods can directly generate human motions…

Computer Vision and Pattern Recognition · Computer Science 2022-09-01 Mingyuan Zhang , Zhongang Cai , Liang Pan , Fangzhou Hong , Xinying Guo , Lei Yang , Ziwei Liu

Human motion synthesis is an important problem with applications in graphics, gaming and simulation environments for robotics. Existing methods require accurate motion capture data for training, which is costly to obtain. Instead, we…

Computer Vision and Pattern Recognition · Computer Science 2022-08-15 Kevin Xie , Tingwu Wang , Umar Iqbal , Yunrong Guo , Sanja Fidler , Florian Shkurti

Motion generation is a cornerstone of computer graphics, animation, gaming, and robotics, enabling the creation of realistic and varied character movements. A significant limitation of existing methods is their reliance on specific skeletal…

Computer Vision and Pattern Recognition · Computer Science 2025-05-29 Aliasghar Khani , Arianna Rampini , Evan Atherton , Bruno Roy

In this paper, we address the challenge of generating realistic 3D human motions for action classes that were never seen during the training phase. Our approach involves decomposing complex actions into simpler movements, specifically those…

Computer Vision and Pattern Recognition · Computer Science 2024-09-19 Lorenzo Mandelli , Stefano Berretti
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