C3P: Cross-Domain Pose Prior Propagation for Weakly Supervised 3D Human Pose Estimation

Abstract

This paper first proposes and solves weakly supervised 3D human pose estimation (HPE) problem in point cloud, via propagating the pose prior within unlabelled RGB-point cloud sequence to 3D domain. Our approach termed C3P does not require any labor-consuming 3D keypoint annotation for training. To this end, we propose to transfer 2D HPE annotation information within the existing large-scale RGB datasets (e.g., MS COCO) to 3D task, using unlabelled RGB-point cloud sequence easy to acquire for linking 2D and 3D domains. The self-supervised 3D HPE clues within point cloud sequence are also exploited, concerning spatial-temporal constraints on human body symmetry, skeleton length and joints’ motion. And, a refined point set network structure for weakly supervised 3D HPE is proposed in encoder-decoder manner. The experiments on CMU Panoptic and ITOP datasets demonstrate that, our method can achieve the comparable results to the 3D fully supervised state-of-the-art counterparts. When large-scale unlabelled data (e.g., NTU RGB+D 60) is used, our approach can even outperform them under the more challenging cross-setup test setting. The source code is released at ""https://github.com/wucunlin/C3P"" for research use only.