Towards Robust and Accurate Single-View Fast Human Motion Capture

This paper proposes a new method for fast human motion capture based on a single RGB-D sensor. By leveraging the human pose detection results for reinitializing the ICP-based sequential human motion tracking algorithm when tracking failure happens, our system achieves a highly robust and accurate human motion tracking performance even for fast motion. Moreover, the calculation and utilization of semantic tracking loss enable body-part-level motion tracking refinement, which is better than whole-body refinement. Finally, a simple yet effective post-processing method, semantic bidirectional motion blending, for single-view human motion capture is proposed to further improve the tracking accuracy, especially under severe occlusions and fast motion. The results and experiments demonstrate that the proposed method achieves highly accurate and robust human motion capture performance in a very efficient way. Applications include AR&VR, human motion analysis, movie, and gaming.