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conference paper

Adversarial Parametric Pose Prior

Davydov, Andrey
•
Remizova, Anastasia
•
Constantin, Victor
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September 27, 2022
Conference on Computer Vision and Pattern Recognition (CVPR)
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

The Skinned Multi-Person Linear (SMPL) model represents human bodies by mapping pose and shape parameters to body meshes. However, not all pose and shape parameter values yield physically-plausible or even realistic body meshes. In other words, SMPL is under-constrained and may yield invalid results. We propose learning a prior that restricts the SMPL parameters to values that produce realistic poses via adversarial training. We show that our learned prior covers the diversity of the real-data distribution, facilitates optimization for 3D reconstruction from 2D keypoints, and yields better pose estimates when used for regression from images. For all these tasks, it outperforms the state-of-the-art VAE-based approach to constraining the SMPL parameters. The code will be made available at https://github.com/cvlab epfl/adv_param_pose_prior.

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Davydov22.pdf

Type

Postprint

Version

http://purl.org/coar/version/c_ab4af688f83e57aa

Access type

openaccess

License Condition

CC BY

Size

6.15 MB

Format

Adobe PDF

Checksum (MD5)

02f5ac93e55b5bf645a39061f86a1b75

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