DreamHMR: From Video Generation to Human Motion Modeling via Structural Focusing

Zhengjie Xu, Ruihao Gong, Xianglong Liu

Human mesh recovery (HMR) provides a geometric foundation for understanding human motion, but accurately reconstructing both body motion and fine hand articulation from videos remains challenging, especially in real-world scenes where occlusion and partial visibility are common. Video generation pretraining offers rich spatial and temporal priors for this task, but its focus on visual synthesis leaves a gap between these representations and explicit human geometry. We present DreamHMR, a general framework that adapts video generation priors for complete body-and-hand reconstruction. It follows a global-to-local strategy, first grounding video representations in human geometry and then refining body and hand detail with reliable local evidence. Global Body Representation Transfer trains the video backbone to predict complete human renderings, turning its native prediction task into a means of learning surface correspondence and posed geometry. Local Joint Feature Enhancement combines contextual estimates with reliable local evidence to recover fine body and hand detail, while limiting interference from occluders and background. Experiments on five datasets with four video…