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 …

We propose a new method to quickly and accurately predict human pose---the 3D positions
of body joints---from a single depth image, without depending on information from preceding
frames. Our approach is strongly rooted in current object recognition strategies. By
designing an intermediate representation in terms of body parts, the difficult pose estimation
problem is transformed into a simpler per-pixel classification problem, for which efficient
machine learning techniques exist. By using computer graphics to synthesize a very large …