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SAGA: Stochastic Whole-Body Grasping with Contact

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13666))

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Abstract

The synthesis of human grasping has numerous applications including AR/VR, video games and robotics. While methods have been proposed to generate realistic hand–object interaction for object grasping and manipulation, these typically only consider interacting hand alone. Our goal is to synthesize whole-body grasping motions. Starting from an arbitrary initial pose, we aim to generate diverse and natural whole-body human motions to approach and grasp a target object in 3D space. This task is challenging as it requires modeling both whole-body dynamics and dexterous finger movements. To this end, we propose SAGA (StochAstic whole-body Grasping with contAct), a framework which consists of two key components: (a) Static whole-body grasping pose generation. Specifically, we propose a multi-task generative model, to jointly learn static whole-body grasping poses and human-object contacts. (b) Grasping motion infilling. Given an initial pose and the generated whole-body grasping pose as the start and end of the motion respectively, we design a novel contact-aware generative motion infilling module to generate a diverse set of grasp-oriented motions. We demonstrate the effectiveness of our method, which is a novel generative framework to synthesize realistic and expressive whole-body motions that approach and grasp randomly placed unseen objects. Code and models are available at https://jiahaoplus.github.io/SAGA/saga.html.

Y. Wu and J. Wang—Equal contribution.

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Notes

  1. 1.

    Please refer to the Appendix for experimental setup and implementation details..

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Acknowledgement

This work was supported by the SNF grant 200021 204840 and Microsoft Mixed Reality & AI Zurich Lab PhD scholarship.

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Authors and Affiliations

  1. ETH Zürich, Zürich, Switzerland

    Yan Wu, Yan Zhang, Siwei Zhang, Otmar Hilliges, Fisher Yu & Siyu Tang

  2. Max Planck Institute for Informatics, Saarbrücken, Germany

    Jiahao Wang

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  1. Yan Wu
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  2. Jiahao Wang
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  3. Yan Zhang
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  7. Siyu Tang
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Corresponding author

Correspondence to Yan Wu .

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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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Wu, Y. et al. (2022). SAGA: Stochastic Whole-Body Grasping with Contact. 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 13666. Springer, Cham. https://doi.org/10.1007/978-3-031-20068-7_15

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