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MEMformer: Transformer-based 3D Human Motion Estimation from MoCap Markers

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Jun XiaoAuthors Info & Claims

SA '22: SIGGRAPH Asia 2022 Posters

Article No.: 5, Pages 1 - 2

https://doi.org/10.1145/3550082.3564197

Published: 13 December 2022 Publication History

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Abstract

We address the problem of 3D human motion estimation from original MoCap optical markers. The original markers are noisy, disordered, and unlabeled, hence recovering 3D human motion from them is non-trivial. Existing works are either time-consuming or assuming the knowledge of the marker labels. We address these problems by presenting an end-to-end method for 3D human motion estimation by leveraging the capability of Transformer to model long-range dependencies. The method takes original markers as inputs and learns joint poses with a Transformer-like architecture. Experimental results show that our method is able to achieve better than centimeter-level errors.

References

[1]

Nima Ghorbani and Michael J Black. 2021. SOMA: Solving Optical Marker-Based MoCap Automatically. In Proceedings of the IEEE/CVF International Conference on Computer Vision. 11117–11126.

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[2]

Matthew Loper, Naureen Mahmood, Javier Romero, Gerard Pons-Moll, and Michael J Black. 2015. SMPL: A skinned multi-person linear model. ACM transactions on graphics (TOG) 34, 6 (2015), 1–16.

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[3]

Meysam Madadi, Hugo Bertiche, and Sergio Escalera. 2021. Deep unsupervised 3D human body reconstruction from a sparse set of landmarks. International Journal of Computer Vision 129, 8 (2021), 2499–2512.

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[4]

Naureen Mahmood, Nima Ghorbani, Nikolaus F Troje, Gerard Pons-Moll, and Michael J Black. 2019. AMASS: Archive of motion capture as surface shapes. In Proceedings of the IEEE/CVF international conference on computer vision. 5442–5451.

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Index Terms

MEMformer: Transformer-based 3D Human Motion Estimation from MoCap Markers
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Tracking
      2. Image and video acquisition
        Motion capture
  2. Computer graphics
    1. Animation
      1. Motion capture
      2. Motion processing
2. Human-centered computing

Index terms have been assigned to the content through auto-classification.

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Published In

SA '22: SIGGRAPH Asia 2022 Posters

December 2022

120 pages

ISBN:9781450394628

DOI:10.1145/3550082

Editors:
Soon Ki Jung
Kyungpook National University, South Korea
,
Neil Dodgson
Victoria University of Wellington, New Zealand

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 December 2022

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the Fundamental Research Funds for the Central Universities

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SA '22

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SIGGRAPH

SA '22: SIGGRAPH Asia 2022

December 6 - 9, 2022

Daegu, Republic of Korea

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Overall Acceptance Rate 178 of 869 submissions, 20%

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Abstract

References

Index Terms

Recommendations

Estimation of missing markers in human motion capture

3D Human Motion Estimation via Motion Compression and Refinement

Real-Time Estimation of Missing Markers for Reconstruction of Human Motion

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