Self-Supervised Hand Pose Estimation

Investigator: Angela Yao.

We aim to bridge data-driven discriminative hand pose estimation with optimization-based model-fitting.  Using a differentiable hand renderer that aligns estimates by comparing the rendered and input depth maps, our method’s accuracy improves with the amount of data encountered, while not needing human-provided annotations.