3D human pose estimation by depth map

Abstract

We present a new approach for 3D human pose estimation from a single image. State-of-the-art methods for 3D pose estimation have focused on predicting a full-body pose of a single person and have not given enough attention to the challenges in application: incompleteness of body pose and existence of multiple persons in image. In this paper, we introduce depth maps to solve these problems. Our approach predicts the depths of human pose over all spatial grids, which supports 3D poses estimation for incomplete or full bodies of multiple persons. The proposed depth maps encode depths of limbs rather than joints. They are more informative and reversibly convertible to depths of joints. The unified network is trained end to end using mixed 2D and 3D annotated samples. The experiments reveal that our algorithm achieves the state of the art on Human3.6M, the largest publicly available 3D pose estimation benchmark. Moreover, qualitative results have been reported to demonstrate the effectiveness of our approach for 3D pose estimation for incomplete human bodies and multiple persons.