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HuMMan: Multi-modal 4D Human Dataset for Versatile Sensing and Modeling

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13667))

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Abstract

4D human sensing and modeling are fundamental tasks in vision and graphics with numerous applications. With the advances of new sensors and algorithms, there is an increasing demand for more versatile datasets. In this work, we contribute HuMMan, a large-scale multi-modal 4D human dataset with 1000 human subjects, 400k sequences and 60M frames. HuMMan has several appealing properties: 1) multi-modal data and annotations including color images, point clouds, keypoints, SMPL parameters, and textured meshes; 2) popular mobile device is included in the sensor suite; 3) a set of 500 actions, designed to cover fundamental movements; 4) multiple tasks such as action recognition, pose estimation, parametric human recovery, and textured mesh reconstruction are supported and evaluated. Extensive experiments on HuMMan voice the need for further study on challenges such as fine-grained action recognition, dynamic human mesh reconstruction, point cloud-based parametric human recovery, and cross-device domain gaps (Homepage: https://caizhongang.github.io/projects/HuMMan/).

Z. Cai, D. Ren, A. Zeng, Z. Lin, T. Yu and W. Wang—Co-first authors.

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Acknowledgements

This work is supported by NTU NAP, MOE AcRF Tier 2 (T2EP20221-0033), NSFC No. 62171255, and under the RIE2020 Industry Alignment Fund - Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner(s).

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Authors and Affiliations

  1. Shanghai AI Laboratory, Shanghai, China

    Zhongang Cai & Lei Yang

  2. S-Lab, Nanyang Technological University, Singapore, Singapore

    Zhongang Cai, Daxuan Ren, Liang Pan, Fangzhou Hong, Mingyuan Zhang, Chen Change Loy & Ziwei Liu

  3. SenseTime Research, Hong Kong, China

    Zhongang Cai, Zhengyu Lin, Wenjia Wang, Xiangyu Fan, Yang Gao, Yifan Yu & Lei Yang

  4. The Chinese University of Hong Kong, Hong Kong, China

    Ailing Zeng

  5. Tsinghua University, Beijing, China

    Tao Yu

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  1. Zhongang Cai
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  2. Daxuan Ren
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  14. Lei Yang
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  15. Ziwei Liu
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Correspondence to Lei Yang or Ziwei Liu .

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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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Cai, Z. et al. (2022). HuMMan: Multi-modal 4D Human Dataset for Versatile Sensing and Modeling. 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 13667. Springer, Cham. https://doi.org/10.1007/978-3-031-20071-7_33

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