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Conference Paper

Capturing and Inferring Dense Full-Body Human-Scene Contact

MPS-Authors
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Huang,  Chun-Hao P.       
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Yi,  Hongwei
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Höschle,  Markus
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Safroshkin,  Matvey
Central Scientific Facility Optics and Sensing Laboratory, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Alexiadis,  Tsvetelina
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Polikovsky,  Senya
Central Scientific Facility Optics and Sensing Laboratory, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Black,  Michael J.       
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

External Resource

https://doi.org/10.1109/CVPR52688.2022.01292
(Publisher version)

https://doi.org/10.48550/arXiv.2206.09553
(Preprint)

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Citation

Huang, C.-H.-P., Yi, H., Höschle, M., Safroshkin, M., Alexiadis, T., Polikovsky, S., et al. (2022). Capturing and Inferring Dense Full-Body Human-Scene Contact. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022) (pp. 13264-13275). Piscataway, NJ: IEEE. doi:10.1109/CVPR52688.2022.01292.


Cite as: https://hdl.handle.net/21.11116/0000-000D-0AEC-F
Abstract
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