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Collaborative Learning of Gesture Recognition and 3D Hand Pose Estimation with Multi-order Feature Analysis

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Computer Vision – ECCV 2020 (ECCV 2020)

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Abstract

Gesture recognition and 3D hand pose estimation are two highly correlated tasks, yet they are often handled separately. In this paper, we present a novel collaborative learning network for joint gesture recognition and 3D hand pose estimation. The proposed network exploits joint-aware features that are crucial for both tasks, with which gesture recognition and 3D hand pose estimation boost each other to learn highly discriminative features. In addition, a novel multi-order multi-stream feature analysis method is introduced which learns posture and multi-order motion information from the intermediate feature maps of videos effectively and efficiently. Due to the exploitation of joint-aware features in common, the proposed technique is capable of learning gesture recognition and 3D hand pose estimation even when only gesture or pose labels are available, and this enables weakly supervised network learning with much reduced data labeling efforts. Extensive experiments show that our proposed method achieves superior gesture recognition and 3D hand pose estimation performance as compared with the state-of-the-art.

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Acknowledgement

The research was carried out at the Rapid-Rich Object Search (ROSE) Lab, Nanyang Technological University, Singapore. This research work was partially supported by SUTD projects PIE-SGP-Al-2020-02 and SRG-ISTD-2020-153.

Author information

Authors and Affiliations

  1. Rapid-Rich Object Search (ROSE) Lab, Interdisciplinary Graduate Programme, Nanyang Technological University, Singapore, Singapore

    Siyuan Yang

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore

    Siyuan Yang, Meng Hwa Er & Alex C. Kot

  3. Information Systems Technology and Design Pillar, Singapore University of Technology and Design, Singapore, Singapore

    Jun Liu

  4. School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore

    Shijian Lu

Authors
  1. Siyuan Yang
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  2. Jun Liu
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  3. Shijian Lu
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  4. Meng Hwa Er
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  5. Alex C. Kot
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Corresponding author

Correspondence to Jun Liu .

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Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

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Yang, S., Liu, J., Lu, S., Er, M.H., Kot, A.C. (2020). Collaborative Learning of Gesture Recognition and 3D Hand Pose Estimation with Multi-order Feature Analysis. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12348. Springer, Cham. https://doi.org/10.1007/978-3-030-58580-8_45

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  • DOI: https://doi.org/10.1007/978-3-030-58580-8_45

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-030-58580-8

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