Learning 3D Human Pose Estimation from Dozens of Datasets using a Geometry-Aware Autoencoder to Bridge Between Skeleton Formats

Understand

Deep learning-based 3D human pose estimation performs best when trained on large amounts of labeled data, making combined learning from many datasets an important research direction.

• One obstacle to this endeavor are the different skeleton formats provided by different datasets, i.e., they do not label the same set of anatomical landmarks.
• There is little prior research on how to best supervise one model with such discrepant labels.
• We show that simply using separate output heads for different skeletons results in inconsistent depth estimates and insufficient information sharing across skeletons.