LAUSR

Pose tracking from range image sequences remains a difficult task due to strong noise and serious self-occlusion of human body. Existing work either rely on extremely large and precisely annotated datasets, or rely on accurate human mesh model and GPU acceleration. In this paper, we propose an unsupervised real-time framework of pose tracking from range image sequences. Our framework consists of a visible hybrid model (VHM), a componentwise correspondence optimization (CCO) and a dynamic database lookup (DDL). VHM consists of component sphere sets and component visible spherical point sets which exhibits both simplicity and high accuracy. CCO converts the matching between VHM and input point cloud into several subproblems regarding local rotations of components and a global translation of body abdominal joint, each of which has an efficient closed form solution. DDL is designed to recover correct pose when tracking fails, which effectively mitigates accumulative error during tracking. Experiments on $\mathsf{SMMC}$, $\mathsf{PDT}$, $\mathsf{EVAL}$ datasets indicate that our framework not only achieves better or competitive precision compared with state-of-the-art methods, but also produces real-time efficiency in personal computers without GPU acceleration.