FlowHMR: Physically Plausible Motion Capture from Video

We present FlowHMR, a framework for recovering global 3D human motion from monocular video. Previous methods commonly learn to recover human poses and trajectories through geometric supervision. However, geometric reconstruction ob- jectives do not explicitly account for the dynamics and contact constraints of human motion. Our approach models the video-conditioned distribution of global human motion using Flow Matching. We train the model on a large collection of videos paired with 3D motion annotations and further optimize it through reinforcement learning, jointly rewarding reconstruction fidelity and accurate tracking in simu- lation. To evaluate motion recovery performance, we introduce Wild-4K, a large and diverse evaluation set of internet videos. Experiments show that FlowHMR outperforms state-of-the-art methods in overall motion quality as judged by human evaluators and in simulation tracking performance. Our code will be made publicly available.