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Related papers: MoSA: Motion-Coherent Human Video Generation via S…

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Generating realistic human videos remains a challenging task, with the most effective methods currently relying on a human motion sequence as a control signal. Existing approaches often use existing motion extracted from other videos, which…

Computer Vision and Pattern Recognition · Computer Science 2024-12-18 Hsin-Ping Huang , Yang Zhou , Jui-Hsien Wang , Difan Liu , Feng Liu , Ming-Hsuan Yang , Zhan Xu

In this paper, we find that the generation of 3D human motions and 2D human videos is intrinsically coupled. 3D motions provide the structural prior for plausibility and consistency in videos, while pre-trained video models offer strong…

Computer Vision and Pattern Recognition · Computer Science 2026-04-13 Chengfeng Zhao , Jiazhi Shu , Yubo Zhao , Tianyu Huang , Jiahao Lu , Zekai Gu , Chengwei Ren , Zhiyang Dou , Qing Shuai , Yuan Liu

3D human motion generation has seen substantial advancement in recent years. While state-of-the-art approaches have improved performance significantly, they still struggle with complex and detailed motions unseen in training data, largely…

Computer Vision and Pattern Recognition · Computer Science 2025-01-09 Shanlin Sun , Gabriel De Araujo , Jiaqi Xu , Shenghan Zhou , Hanwen Zhang , Ziheng Huang , Chenyu You , Xiaohui Xie

Enabling virtual humans to dynamically and realistically respond to diverse auditory stimuli remains a key challenge in character animation, demanding the integration of perceptual modeling and motion synthesis. Despite its significance,…

Graphics · Computer Science 2025-11-04 Shuyang Xu , Zhiyang Dou , Mingyi Shi , Liang Pan , Leo Ho , Jingbo Wang , Yuan Liu , Cheng Lin , Yuexin Ma , Wenping Wang , Taku Komura

Generating human videos with realistic and controllable motions is a challenging task. While existing methods can generate visually compelling videos, they lack separate control over four key video elements: foreground subject, background…

Computer Vision and Pattern Recognition · Computer Science 2025-08-13 Jingyun Liang , Jingkai Zhou , Shikai Li , Chenjie Cao , Lei Sun , Yichen Qian , Weihua Chen , Fan Wang

Human video generation is a dynamic and rapidly evolving task that aims to synthesize 2D human body video sequences with generative models given control conditions such as text, audio, and pose. With the potential for wide-ranging…

Computer Vision and Pattern Recognition · Computer Science 2024-07-12 Wentao Lei , Jinting Wang , Fengji Ma , Guanjie Huang , Li Liu

Single-view clothed human reconstruction holds a central position in virtual reality applications, especially in contexts involving intricate human motions. It presents notable challenges in achieving realistic clothing deformation. Current…

Computer Vision and Pattern Recognition · Computer Science 2024-06-25 Hongsheng Wang , Xiang Cai , Xi Sun , Jinhong Yue , Zhanyun Tang , Shengyu Zhang , Feng Lin , Fei Wu

Co-speech gestures, if presented in the lively form of videos, can achieve superior visual effects in human-machine interaction. While previous works mostly generate structural human skeletons, resulting in the omission of appearance…

Computer Vision and Pattern Recognition · Computer Science 2024-04-03 Xu He , Qiaochu Huang , Zhensong Zhang , Zhiwei Lin , Zhiyong Wu , Sicheng Yang , Minglei Li , Zhiyi Chen , Songcen Xu , Xiaofei Wu

While recent text-to-video models excel at generating diverse scenes, they struggle with precise motion control, particularly for complex, multi-subject motions. Although methods for single-motion customization have been developed to…

Computer Vision and Pattern Recognition · Computer Science 2025-10-28 Youcan Xu , Zhen Wang , Jiaxin Shi , Kexin Li , Feifei Shao , Jun Xiao , Yi Yang , Jun Yu , Long Chen

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

Generating realistic full-body motion interacting with objects is critical for applications in robotics, virtual reality, and human-computer interaction. While existing methods can generate full-body motion within 3D scenes, they often lack…

Computer Vision and Pattern Recognition · Computer Science 2025-10-28 Kunal Bhosikar , Siddharth Katageri , Vivek Madhavaram , Kai Han , Charu Sharma

Although humans have the innate ability to imagine multiple possible actions from videos, it remains an extraordinary challenge for computers due to the intricate camera movements and montages. Most existing motion generation methods…

Computer Vision and Pattern Recognition · Computer Science 2024-08-14 Liangdong Qiu , Chengxing Yu , Yanran Li , Zhao Wang , Haibin Huang , Chongyang Ma , Di Zhang , Pengfei Wan , Xiaoguang Han

We introduce a method to synthesize animator guided human motion across 3D scenes. Given a set of sparse (3 or 4) joint locations (such as the location of a person's hand and two feet) and a seed motion sequence in a 3D scene, our method…

Computer Vision and Pattern Recognition · Computer Science 2025-02-04 Aymen Mir , Xavier Puig , Angjoo Kanazawa , Gerard Pons-Moll

Generating videos of complex human motions such as flips, cartwheels, and martial arts remains challenging for current video diffusion models. Text-only conditioning is temporally ambiguous for fine-grained motion control, while explicit…

Computer Vision and Pattern Recognition · Computer Science 2026-03-10 Ashkan Taghipour , Morteza Ghahremani , Zinuo Li , Hamid Laga , Farid Boussaid , Mohammed Bennamoun

Recent advancements in human video synthesis have enabled the generation of high-quality videos through the application of stable diffusion models. However, existing methods predominantly concentrate on animating solely the human element…

Computer Vision and Pattern Recognition · Computer Science 2024-05-29 Jinlin Liu , Kai Yu , Mengyang Feng , Xiefan Guo , Miaomiao Cui

Motion, scene and object are three primary visual components of a video. In particular, objects represent the foreground, scenes represent the background, and motion traces their dynamics. Based on this insight, we propose a two-stage…

Computer Vision and Pattern Recognition · Computer Science 2023-03-17 Mingzhen Sun , Weining Wang , Xinxin Zhu , Jing Liu

Video-based human motion transfer creates video animations of humans following a source motion. Current methods show remarkable results for tightly-clad subjects. However, the lack of temporally consistent handling of plausible clothing…

Computer Vision and Pattern Recognition · Computer Science 2020-12-22 Moritz Kappel , Vladislav Golyanik , Mohamed Elgharib , Jann-Ole Henningson , Hans-Peter Seidel , Susana Castillo , Christian Theobalt , Marcus Magnor

Recent video diffusion models generate photorealistic, temporally coherent videos, yet they fall short as reliable world models for autonomous driving, where structured motion and physically consistent interactions are essential. Adapting…

Computer Vision and Pattern Recognition · Computer Science 2026-01-15 Ahmad Rahimi , Valentin Gerard , Eloi Zablocki , Matthieu Cord , Alexandre Alahi

The ultimate goal of video generation is to satisfy a fundamental trilemma: achieving high visual quality, maintaining rigorous physical consistency, and enabling precise controllability. While recent models can maintain this balance in…

Computer Vision and Pattern Recognition · Computer Science 2026-03-12 Tianshuo Xu , Zhifei Chen , Leyi Wu , Hao Lu , Ying-cong Chen

Human motion video generation has garnered significant research interest due to its broad applications, enabling innovations such as photorealistic singing heads or dynamic avatars that seamlessly dance to music. However, existing surveys…

Computer Vision and Pattern Recognition · Computer Science 2025-09-05 Haiwei Xue , Xiangyang Luo , Zhanghao Hu , Xin Zhang , Xunzhi Xiang , Yuqin Dai , Jianzhuang Liu , Zhensong Zhang , Minglei Li , Jian Yang , Fei Ma , Zhiyong Wu , Changpeng Yang , Zonghong Dai , Fei Richard Yu
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