Abstract:
We propose a new method to quickly and accurately predict 3D positions of body joints from a single depth image,
using no temporal information. We take an object recognition approach, designing an intermediate body parts representation that maps the difficult pose estimation problem
into a simpler per-pixel classification problem. Our large
and highly varied training dataset allows the classifier to
estimate body parts invariant to pose, body shape, clothing,
etc. Finally we generate confidence-scored 3D proposals of
several body joints by reprojecting the classification result
and finding local modes.
The system runs at 200 frames per second on consumer
hardware. Our evaluation shows high accuracy on both
synthetic and real test sets, and investigates the effect of several training parameters. We achieve state of the art accuracy in our comparison with related work and demonstrate
improved generalization over exact whole-skeleton nearest
neighbor matching
