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Detail-preserved real-time hand motion regression from depth

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Abstract

This paper aims to address the very challenging problem of efficient and accurate hand tracking from depth sequences, meanwhile to deform a high-resolution 3D hand model with geometric details. We propose an integrated regression framework to infer articulated hand pose, and regress high-frequency details from sparse high-resolution 3D hand model examples. Specifically, our proposed method mainly consists of four components: skeleton embedding, hand joint regression, skeleton alignment, and high-resolution details integration. Skeleton embedding is optimized via a wrinkle-based skeleton refinement method for faithful hand models with fine geometric details. Hand joint regression is based on a deep convolutional network, from which 3D hand joint locations are predicted from a single depth map, then a skeleton alignment stage is performed to recover fully articulated hand poses. Deformable fine-scale details are estimated from a nonlinear mapping between the hand joints and per-vertex displacements. Experiments on two challenging datasets show that our proposed approach can achieve accurate, robust, and real-time hand tracking, while preserve most high-frequency details when deforming a virtual hand.

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Acknowledgements

We would like to thank the anonymous reviewers for their valuable suggestions. This paper is supported by National Key Technologies R & D Program of China (No.2017YFB1002702).

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Authors and Affiliations

  1. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China

    Qing Fan & Xukun Shen

  2. School of New Media Art and Design, Beihang University, Beijing, China

    Yong Hu

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  1. Qing Fan
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  2. Xukun Shen
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  3. Yong Hu
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Correspondence to Yong Hu.

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Fan, Q., Shen, X. & Hu, Y. Detail-preserved real-time hand motion regression from depth. Vis Comput 34, 1145–1154 (2018). https://doi.org/10.1007/s00371-018-1546-2

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