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TOCH: Spatio-Temporal Object-to-Hand Correspondence for Motion Refinement

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Computer Vision – ECCV 2022 (ECCV 2022)

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Abstract

We present TOCH, a method for refining incorrect 3D hand-object interaction sequences using a correspondence based prior learnt directly from data. Existing hand trackers, especially those that rely on very few cameras, often produce visually unrealistic results with hand-object intersection or missing contacts. Although correcting such errors requires reasoning about temporal aspects of interaction, most previous works focus on static grasps and contacts. The core of our method are TOCH fields, a novel spatio-temporal representation for modeling correspondences between hands and objects during interaction. TOCH fields are a point-wise, object-centric representation, which encode the hand position relative to the object. Leveraging this novel representation, we learn a latent manifold of plausible TOCH fields with a temporal denoising auto-encoder. Experiments demonstrate that TOCH outperforms state-of-the-art 3D hand-object interaction models, which are limited to static grasps and contacts. More importantly, our method produces smooth interactions even before and after contact. Using a single trained TOCH model, we quantitatively and qualitatively demonstrate its usefulness for correcting erroneous sequences from off-the-shelf RGB/RGB-D hand-object reconstruction methods and transferring grasps across objects. Our code and model are available at [1].

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Acknowledgements

This work is supported by the German Federal Ministry of Education and Research (BMBF): Tübingen AI Center, FKZ: 01IS18039A. This work is funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - 409792180 (Emmy Noether Programme, project: Real Virtual Humans). Gerard Pons-Moll is a member of the Machine Learning Cluster of Excellence, EXC number 2064/1 - Project number 390727645.

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Authors and Affiliations

  1. University of Tübingen, Tübingen, Germany

    Keyang Zhou, Bharat Lal Bhatnagar & Gerard Pons-Moll

  2. Max Planck Institute for Informatics, Saarland Informatics Campus, Munich, Germany

    Keyang Zhou, Bharat Lal Bhatnagar, Jan Eric Lenssen & Gerard Pons-Moll

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  1. Keyang Zhou
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  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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Zhou, K., Bhatnagar, B.L., Lenssen, J.E., Pons-Moll, G. (2022). TOCH: Spatio-Temporal Object-to-Hand Correspondence for Motion Refinement. 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 13663. Springer, Cham. https://doi.org/10.1007/978-3-031-20062-5_1

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