3D Human Pose Estimation based on Center of Gravity
Hao Xu, Suping Wu · 2020
In this paper, we propose a method about 3D human pose estimation with only 2D joints as input. Previous methods generally lift 2D poses to 3D space through a single mapping function, in which case some large-pose samples far away from the majority distribution may not be well concerned. To address the issue above, we design a multi-branch network based on the human center of gravity (COG) to enhance the robustness of the model to large-pose samples. Specifically, noticing the correspondence between the COG and human pose, by clustering the COG, we separate the large-pose samples from the normal ones in an unsupervised pattern, and lift them with separate branch network. In addition, we introduce a global loss function to regularize the integrality of 3D joints. Extensive experiments on the largest publicly available dataset demonstrate the validity and efficiency of our method.