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Incorporating physics in human motion capture to avoid artifacts likefloating, foot sliding, and ground penetration is a promising direction.Existing solutions always adopt kinematic results as reference motions, and thephysics is treated as a post-processing module. However, due to the depthambiguity, monocular motion capture inevitably suffers from noises, and thenoisy reference often leads to failure for physics-based tracking. To addressthe obstacles, our key-idea is to employ physics as denoising guidance in thereverse diffusion process to reconstruct physically plausible human motion froma modeled pose probability distribution. Specifically, we first train a latentgaussian model that encodes the uncertainty of 2D-to-3D lifting to facilitatereverse diffusion. Then, a physics module is constructed to track the motionsampled from the distribution. The discrepancies between the tracked motion andimage observation are used to provide explicit guidance for the reversediffusion model to refine the motion. With several iterations, thephysics-based tracking and kinematic denoising promote each other to generate aphysically plausible human motion. Experimental results show that our methodoutperforms previous physics-based methods in both joint accuracy and successrate. More information can be found at\url{https://github.com/Me-Ditto/Physics-Guided-Mocap}.

Physics-Guided Human Motion Capture with Pose Probability Modeling