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arXiv · 1704.00515

Capturing Hand Motion with an RGB-D Sensor, Fusing a Generative Model with Salient Points

Dimitrios Tzionas·Abhilash Srikantha·Pablo Aponte·Juergen Gall
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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.

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Dimitrios Tzionas, Abhilash Srikantha, Pablo Aponte, Juergen Gall. 2017-04-03. Capturing Hand Motion with an RGB-D Sensor, Fusing a Generative Model with Salient Points. https://doi.org/10.1007/978-3-319-11752-2_22

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