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Embodied hands: modeling and capturing hands and bodies together

Authors:

Dimitrios Tzionas,

Michael J. BlackAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 36, Issue 6

Article No.: 245, Pages 1 - 17

https://doi.org/10.1145/3130800.3130883

Published: 20 November 2017 Publication History

Abstract

Humans move their hands and bodies together to communicate and solve tasks. Capturing and replicating such coordinated activity is critical for virtual characters that behave realistically. Surprisingly, most methods treat the 3D modeling and tracking of bodies and hands separately. Here we formulate a model of hands and bodies interacting together and fit it to full-body 4D sequences. When scanning or capturing the full body in 3D, hands are small and often partially occluded, making their shape and pose hard to recover. To cope with low-resolution, occlusion, and noise, we develop a new model called MANO (hand Model with Articulated and Non-rigid defOrmations). MANO is learned from around 1000 high-resolution 3D scans of hands of 31 subjects in a wide variety of hand poses. The model is realistic, low-dimensional, captures non-rigid shape changes with pose, is compatible with standard graphics packages, and can fit any human hand. MANO provides a compact mapping from hand poses to pose blend shape corrections and a linear manifold of pose synergies. We attach MANO to a standard parameterized 3D body shape model (SMPL), resulting in a fully articulated body and hand model (SMPL+H). We illustrate SMPL+H by fitting complex, natural, activities of subjects captured with a 4D scanner. The fitting is fully automatic and results in full body models that move naturally with detailed hand motions and a realism not seen before in full body performance capture. The models and data are freely available for research purposes at http://mano.is.tue.mpg.de.

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Index Terms

Embodied hands: modeling and capturing hands and bodies together
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Image and video acquisition
        Motion capture

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 36, Issue 6

December 2017

973 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3130800

Editor:
Kavita Bala

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Published: 20 November 2017

Published in TOG Volume 36, Issue 6

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Tang YLi GZhang TRen HYang XYang LGuo DShen Y(2025)Digital channel–enabled distributed force decoding via small datasets for hand-centric interactionsScience Advances10.1126/sciadv.adt264111:4Online publication date: 24-Jan-2025
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