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Identity-Aware Hand Mesh Estimation and Personalization from RGB Images

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13665))

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Abstract

Reconstructing 3D hand meshes from monocular RGB images has attracted increasing amount of attention due to its enormous potential applications in the field of AR/VR. Most state-of-the-art methods attempt to tackle this task in an anonymous manner. Specifically, the identity of the subject is ignored even though it is practically available in real applications where the user is unchanged in a continuous recording session. In this paper, we propose an identity-aware hand mesh estimation model, which can incorporate the identity information represented by the intrinsic shape parameters of the subject. We demonstrate the importance of the identity information by comparing the proposed identity-aware model to a baseline which treats subject anonymously. Furthermore, to handle the use case where the test subject is unseen, we propose a novel personalization pipeline to calibrate the intrinsic shape parameters using only a few unlabeled RGB images of the subject. Experiments on two large scale public datasets validate the state-of-the-art performance of our proposed method.

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Author information

Authors and Affiliations

  1. University of California-Irvine, Irvine, CA, 92697, USA

    Deying Kong, Haoyu Ma, Xiangyi Yan, Shanlin Sun, Xingwei Liu, Kun Han & Xiaohui Xie

  2. Reality Labs at Meta, Irvine, USA

    Linguang Zhang & Liangjian Chen

Authors
  1. Deying Kong
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  2. Linguang Zhang
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  3. Liangjian Chen
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  4. Haoyu Ma
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  5. Xiangyi Yan
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  6. Shanlin Sun
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  7. Xingwei Liu
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  8. Kun Han
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  9. Xiaohui Xie
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Correspondence to Deying Kong .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Kong, D. et al. (2022). Identity-Aware Hand Mesh Estimation and Personalization from RGB Images. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13665. Springer, Cham. https://doi.org/10.1007/978-3-031-20065-6_31

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