  Modern 3D human pose estimation techniques rely on deep networks, which
require large amounts of training data. While weakly-supervised methods require
less supervision, by utilizing 2D poses or multi-view imagery without
annotations, they still need a sufficiently large set of samples with 3D
annotations for learning to succeed.
  In this paper, we propose to overcome this problem by learning a
geometry-aware body representation from multi-view images without annotations.
To this end, we use an encoder-decoder that predicts an image from one
viewpoint given an image from another viewpoint. Because this representation
encodes 3D geometry, using it in a semi-supervised setting makes it easier to
learn a mapping from it to 3D human pose. As evidenced by our experiments, our
approach significantly outperforms fully-supervised methods given the same
amount of labeled data, and improves over other semi-supervised methods while
using as little as 1% of the labeled data.
