  In this paper, we propose a two-stage depth ranking based method (DRPose3D)
to tackle the problem of 3D human pose estimation. Instead of accurate 3D
positions, the depth ranking can be identified by human intuitively and learned
using the deep neural network more easily by solving classification problems.
Moreover, depth ranking contains rich 3D information. It prevents the 2D-to-3D
pose regression in two-stage methods from being ill-posed. In our method,
firstly, we design a Pairwise Ranking Convolutional Neural Network (PRCNN) to
extract depth rankings of human joints from images. Secondly, a coarse-to-fine
3D Pose Network(DPNet) is proposed to estimate 3D poses from both depth
rankings and 2D human joint locations. Additionally, to improve the generality
of our model, we introduce a statistical method to augment depth rankings. Our
approach outperforms the state-of-the-art methods in the Human3.6M benchmark
for all three testing protocols, indicating that depth ranking is an essential
geometric feature which can be learned to improve the 3D pose estimation.
