  3D human pose estimation from a single image is still a challenging problem
despite the large amount of work that has been performed in this field.
Generally, most methods directly use neural networks and ignore certain
constraints (e.g., reprojection constraints, joint angle, and bone length
constraints). While a few methods consider these constraints but train the
network separately, they cannot effectively solve the depth ambiguity problem.
In this paper, we propose a GAN-based model for 3D human pose estimation, in
which a reprojection network is employed to learn the mapping of the
distribution from 3D poses to 2D poses, and a discriminator is employed for
2D-3D consistency discrimination. We adopt a novel strategy to synchronously
train the generator, the reprojection network and the discriminator.
Furthermore, inspired by the typical kinematic chain space (KCS) matrix, we
introduce a weighted KCS matrix and take it as one of the discriminator's
inputs to impose joint angle and bone length constraints. The experimental
results on Human3.6M show that our method significantly outperforms
state-of-the-art methods in most cases.
