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HUMOS: Human Motion Model Conditioned on Body Shape

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Computer Vision – ECCV 2024 (ECCV 2024)

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Abstract

Generating realistic human motion is crucial for many computer vision and graphics applications. The rich diversity of human body shapes and sizes significantly influences how people move. However, existing motion models typically overlook these differences, using a normalized, average body instead. This results in a homogenization of motion across human bodies, with motions not aligning with their physical attributes, thus limiting diversity. To address this, we propose a novel approach to learn a generative motion model conditioned on body shape. We demonstrate that it is possible to learn such a model from unpaired training data using cycle consistency, intuitive physics, and stability constraints that model the correlation between identity and movement. The resulting model produces diverse, physically plausible, and dynamically stable human motions that are quantitatively and qualitatively more realistic than existing state of the art. More details are available on our project page https://github.com/CarstenEpic/humos.

S. Tripathi—work done during an internship at Epic Games.

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Notes

  1. 1.

    All datasets were obtained and used only by the authors affiliated with academic institutions.

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Acknowledgements

We sincerely thank Tsvetelina Alexiadis, Alpar Cseke, Tomasz Niewiadomski, and Taylor McConnell for facilitating the perceptual study, and Giorgio Becherini for his help with the Rokoko baseline. We are grateful to Iain Matthews, Brian Karis, Nikos Athanasiou, Markos Diomataris, and Mathis Petrovich for valuable discussions and advice. Their invaluable contributions enriched this research significantly.

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Authors and Affiliations

  1. Max Planck Institute for Intelligent Systems, Tübingen, Germany

    Shashank Tripathi, Omid Taheri & Michael Black

  2. Epic Games, Cary, USA

    Shashank Tripathi, Christoph Lassner, Daniel Holden & Carsten Stoll

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  1. Shashank Tripathi
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  2. Omid Taheri
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Correspondence to Shashank Tripathi .

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Tripathi, S., Taheri, O., Lassner, C., Black, M., Holden, D., Stoll, C. (2025). HUMOS: Human Motion Model Conditioned on Body Shape. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15074. Springer, Cham. https://doi.org/10.1007/978-3-031-72640-8_8

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