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Conference Paper

Accurate 3D Body Shape Regression using Metric and Semantic Attributes

MPS-Authors
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Choutas,  Vasileios
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

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Müller,  Lea
Dept. Perceiving Systems, Max Planck Institute for Intelligent Systems, Max Planck Society;

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

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Tzionas,  Dimitrios
Dept. Perceiving Systems, 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/CVPR52688.2022.00274
(Publisher version)

https://doi.org/10.48550/arXiv.2206.07036
(Preprint)

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Citation

Choutas, V., Müller, L., Huang, C.-H.-P., Tang, S., Tzionas, D., & Black, M. J. (2022). Accurate 3D Body Shape Regression using Metric and Semantic Attributes. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022) (pp. 2708-2718). Piscataway, NJ: IEEE. doi:10.1109/CVPR52688.2022.00274.


Cite as: https://hdl.handle.net/21.11116/0000-000D-0621-7
Abstract
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