  In this paper, we study the task of 3D human pose estimation in the wild.
This task is challenging due to lack of training data, as existing datasets are
either in the wild images with 2D pose or in the lab images with 3D pose.
  We propose a weakly-supervised transfer learning method that uses mixed 2D
and 3D labels in a unified deep neutral network that presents two-stage
cascaded structure. Our network augments a state-of-the-art 2D pose estimation
sub-network with a 3D depth regression sub-network. Unlike previous two stage
approaches that train the two sub-networks sequentially and separately, our
training is end-to-end and fully exploits the correlation between the 2D pose
and depth estimation sub-tasks. The deep features are better learnt through
shared representations. In doing so, the 3D pose labels in controlled lab
environments are transferred to in the wild images. In addition, we introduce a
3D geometric constraint to regularize the 3D pose prediction, which is
effective in the absence of ground truth depth labels. Our method achieves
competitive results on both 2D and 3D benchmarks.
