  Most recent approaches to monocular 3D pose estimation rely on Deep Learning.
They either train a Convolutional Neural Network to directly regress from image
to 3D pose, which ignores the dependencies between human joints, or model these
dependencies via a max-margin structured learning framework, which involves a
high computational cost at inference time.
  In this paper, we introduce a Deep Learning regression architecture for
structured prediction of 3D human pose from monocular images that relies on an
overcomplete auto-encoder to learn a high-dimensional latent pose
representation and account for joint dependencies. We demonstrate that our
approach outperforms state-of-the-art ones both in terms of structure
preservation and prediction accuracy.
