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GIMO: Gaze-Informed Human Motion Prediction in Context

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13673))

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Abstract

Predicting human motion is critical for assistive robots and AR/VR applications, where the interaction with humans needs to be safe and comfortable. Meanwhile, an accurate prediction depends on understanding both the scene context and human intentions. Even though many works study scene-aware human motion prediction, the latter is largely underexplored due to the lack of ego-centric views that disclose human intent and the limited diversity in motion and scenes. To reduce the gap, we propose a large-scale human motion dataset that delivers high-quality body pose sequences, scene scans, as well as ego-centric views with the eye gaze that serves as a surrogate for inferring human intent. By employing inertial sensors for motion capture, our data collection is not tied to specific scenes, which further boosts the motion dynamics observed from our subjects. We perform an extensive study of the benefits of leveraging the eye gaze for ego-centric human motion prediction with various state-of-the-art architectures. Moreover, to realize the full potential of the gaze, we propose a novel network architecture that enables bidirectional communication between the gaze and motion branches. Our network achieves the top performance in human motion prediction on the proposed dataset, thanks to the intent information from eye gaze and the denoised gaze feature modulated by the motion. Code and data can be found at https://github.com/y-zheng18/GIMO.

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Notes

  1. 1.

    https://noitom.com/perception-neuron-series.

  2. 2.

    https://www.microsoft.com/en-us/hololens.

  3. 3.

    https://apps.apple.com/us/app/3d-scanner-app/id1419913995.

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Acknowledgments

The authors are supported by a grant from the Stanford HAI Institute, a Vannevar Bush Faculty Fellowship, a gift from the Amazon Research Awards program, the NSFC grant No. 62125107, and No. 62171255. Also, Toyota Research Institute provided funds to support this work.

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Authors and Affiliations

  1. Stanford University, Stanford, USA

    Yang Zheng, Yanchao Yang, Kaichun Mo, Jiaman Li, C. Karen Liu & Leonidas J. Guibas

  2. Tsinghua University, Beijing, China

    Yang Zheng, Tao Yu & Yebin Liu

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  1. Yang Zheng
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  2. Yanchao Yang
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  3. Kaichun Mo
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  4. Jiaman Li
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  6. Yebin Liu
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  8. Leonidas J. Guibas
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Corresponding author

Correspondence to Yanchao Yang .

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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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Zheng, Y. et al. (2022). GIMO: Gaze-Informed Human Motion Prediction in Context. 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 13673. Springer, Cham. https://doi.org/10.1007/978-3-031-19778-9_39

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