  Hand motion capture has been an active research topic in recent years,
following the success of full-body pose tracking. Despite similarities, hand
tracking proves to be more challenging, characterized by a higher
dimensionality, severe occlusions and self-similarity between fingers. For this
reason, most approaches rely on strong assumptions, like hands in isolation or
expensive multi-camera systems, that limit the practical use. In this work, we
propose a framework for hand tracking that can capture the motion of two
interacting hands using only a single, inexpensive RGB-D camera. Our approach
combines a generative model with collision detection and discriminatively
learned salient points. We quantitatively evaluate our approach on 14 new
sequences with challenging interactions.
