  Recently, remarkable advances have been achieved in 3D human pose estimation
from monocular images because of the powerful Deep Convolutional Neural
Networks (DCNNs). Despite their success on large-scale datasets collected in
the constrained lab environment, it is difficult to obtain the 3D pose
annotations for in-the-wild images. Therefore, 3D human pose estimation in the
wild is still a challenge. In this paper, we propose an adversarial learning
framework, which distills the 3D human pose structures learned from the fully
annotated dataset to in-the-wild images with only 2D pose annotations. Instead
of defining hard-coded rules to constrain the pose estimation results, we
design a novel multi-source discriminator to distinguish the predicted 3D poses
from the ground-truth, which helps to enforce the pose estimator to generate
anthropometrically valid poses even with images in the wild. We also observe
that a carefully designed information source for the discriminator is essential
to boost the performance. Thus, we design a geometric descriptor, which
computes the pairwise relative locations and distances between body joints, as
a new information source for the discriminator. The efficacy of our adversarial
learning framework with the new geometric descriptor has been demonstrated
through extensive experiments on widely used public benchmarks. Our approach
significantly improves the performance compared with previous state-of-the-art
approaches.
