  3D pose estimation is a challenging problem in computer vision. Most of the
existing neural-network-based approaches address color or depth images through
convolution networks (CNNs). In this paper, we study the task of 3D human pose
estimation from depth images. Different from the existing CNN-based human pose
estimation method, we propose a deep human pose network for 3D pose estimation
by taking the point cloud data as input data to model the surface of complex
human structures. We first cast the 3D human pose estimation from 2D depth
images to 3D point clouds and directly predict the 3D joint position. Our
experiments on two public datasets show that our approach achieves higher
accuracy than previous state-of-art methods. The reported results on both ITOP
and EVAL datasets demonstrate the effectiveness of our method on the targeted
tasks.

Computer Vision and Pattern Recognition;Multimedia