Capturing Hand Motion with an RGB-D Sensor, Fusing a Generative Model with Salient Points
arXiv:1704.00515 · doi:10.1007/978-3-319-11752-2_22
Abstract
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.
German Conference on Pattern Recognition (GCPR) 2014, http://files.is.tue.mpg.de/dtzionas/GCPR_2014.html