Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation

Understand

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.