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Skeleton-Parted Graph Scattering Networks for 3D Human Motion Prediction

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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

Graph convolutional network based methods that model the body-joints’ relations, have recently shown great promise in 3D skeleton-based human motion prediction. However, these methods have two critical issues: first, deep graph convolutions filter features within only limited graph spectrums, losing sufficient information in the full band; second, using a single graph to model the whole body underestimates the diverse patterns on various body-parts. To address the first issue, we propose adaptive graph scattering, which leverages multiple trainable band-pass graph filters to decompose pose features into richer graph spectrum bands. To address the second issue, body-parts are modeled separately to learn diverse dynamics, which enables finer feature extraction along the spatial dimensions. Integrating the above two designs, we propose a novel skeleton-parted graph scattering network (SPGSN). The cores of the model are cascaded multi-part graph scattering blocks (MPGSBs), building adaptive graph scattering on diverse body-parts, as well as fusing the decomposed features based on the inferred spectrum importance and body-part interactions. Extensive experiments have shown that SPGSN outperforms state-of-the-art methods by remarkable margins of \(13.8\%\), \(9.3\%\) and \(2.7\%\) in terms of 3D mean per joint position error (MPJPE) on Human3.6M, CMU Mocap and 3DPW datasets, respectively (The codes are available at https://github.com/MediaBrain-SJTU/SPGSN).

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Notes

  1. 1.

    http://mocap.cs.cmu.edu/.

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Acknowledgements

This work is supported by the National Key Research and Development Program of China (2020YFB1406801), the National Natural Science Foundation of China under Grant (62171276), 111 plan (BP0719010), STCSM (18DZ2270700, 21511100900), State Key Laboratory of UHD Video and Audio Production and Presentation.

Author information

Authors and Affiliations

  1. Cooperative Medianet Innovation Center, Shanghai Jiao Tong University, Shanghai, China

    Maosen Li, Siheng Chen & Ya Zhang

  2. Shanghai AI Laboratory, Shanghai, China

    Siheng Chen & Ya Zhang

  3. Zhejiang University, Hangzhou, China

    Zijing Zhang

  4. Huawei Cloud & AI, Shenzhen, China

    Lingxi Xie & Qi Tian

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  1. Maosen Li
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  2. Siheng Chen
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  3. Zijing Zhang
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  4. Lingxi Xie
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  5. Qi Tian
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  6. Ya Zhang
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Corresponding authors

Correspondence to Siheng Chen or Ya Zhang .

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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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Li, M., Chen, S., Zhang, Z., Xie, L., Tian, Q., Zhang, Y. (2022). Skeleton-Parted Graph Scattering Networks for 3D Human Motion Prediction. 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_2

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