  In this paper, a novel deep-learning based framework is proposed to infer 3D
human poses from a single image. Specifically, a two-phase approach is
developed. We firstly utilize a generator with two branches for the extraction
of explicit and implicit depth information respectively. During the training
process, an adversarial scheme is also employed to further improve the
performance. The implicit and explicit depth information with the estimated 2D
joints generated by a widely used estimator, in the second step, are together
fed into a deep 3D pose regressor for the final pose generation. Our method
achieves MPJPE of 58.68mm on the ECCV2018 3D Human Pose Estimation Challenge.
