Skeleton Cluster Tracking for robust multi-view multi-person 3D human pose estimation

The multi-view 3D human pose estimation task relies on 2D human pose estimation for each view; however, severe occlusion, truncation, and human interaction lead to incorrect 2D human pose estimation for some views. The traditional “Matching-Lifting-Tracking” paradigm amplifies the incorrect 2D human pose into an incorrect 3D human pose, which significantly challenges the robustness of multi-view 3D human pose estimation. In this paper, we propose a novel method that tackles the inherent difficulties of the traditional paradigm. This method is rooted in the newly devised “Skeleton Pooling-Clustering-Tracking (SPCT)” paradigm. It initiates a 2D human pose estimation for each perspective. Then a symmetrical dilated network is created for skeleton pool estimation. Upon clustering the skeleton pool, we introduce and implement an innovative tracking method that is explicitly designed for the SPCT paradigm. The tracking method refines and filters the skeleton clusters, thereby enhancing the robustness of the multi-person 3D human pose estimation results. By coupling the skeleton pool with the tracking refinement process, our method obtains high-quality multi-person 3D human pose estimation results despite severe occlusions that produce erroneous 2D and 3D estimates. By employing the proposed SPCT paradigm and a computationally efficient network architecture, our method outperformed existing approaches regarding robustness on the Shelf, 4D Association, and CMU Panoptic datasets, and could be applied in practical scenarios such as markerless motion capture and animation production.