  The recent success of deep networks has significantly advanced 3D human pose
estimation from 2D images. The diversity of capturing viewpoints and the
flexibility of the human poses, however, remain some significant challenges. In
this paper, we propose a view invariant 3D human pose estimation module to
alleviate the effects of viewpoint diversity. The framework consists of a base
network, which provides an initial estimation of a 3D pose, a view-invariant
hierarchical correction network (VI-HC) on top of that to learn the 3D pose
refinement under consistent views, and a view-invariant discriminative network
(VID) to enforce high-level constraints over body configurations. In VI-HC, the
initial 3D pose inputs are automatically transformed to consistent views for
further refinements at the global body and local body parts level,
respectively. For the VID, under consistent viewpoints, we use adversarial
learning to differentiate between estimated poses and real poses to avoid
implausible 3D poses. Experimental results demonstrate that the consistent
viewpoints can dramatically enhance the performance. Our module shows
robustness for different 3D pose base networks and achieves a significant
improvement (about 9%) over a powerful baseline on the public 3D pose
estimation benchmark Human3.6M.
