FlowHMR: Physically Plausible Motion Capture from Video
We present FlowHMR, a framework for recovering physically plausible global 3D human motion from monocular video. Previous learning-based methods typically regress human motion directly from video and train the network with geometric supervision. However, recovering human motion from monocular video is inherently ambiguous in depth, and direct regression tends to collapse toward an averaged solution. Moreover, the recovered motions are not guaranteed to be physically plausible, so physics-based tracking of them often fails. To address these challenges, we formulate video motion capture as a video-conditioned motion generation problem and first pretrain a flow matching model for this task. ...
| Authors | Zhanke Wang, Chengfeng Zhao, Qing Shuai et al. |
|---|---|
| Primary category | cs.CV |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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