GART: Gaussian Articulated Template Models

Jiahui Lei; Yufu Wang; Georgios Pavlakos; Lingjie Liu; Kostas Daniilidis

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arXiv · 2311.16099

GART: Gaussian Articulated Template Models

Jiahui Lei·Yufu Wang·Georgios Pavlakos·Lingjie Liu·Kostas Daniilidis

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Abstract

We introduce Gaussian Articulated Template Model GART, an explicit, efficient, and expressive representation for non-rigid articulated subject capturing and rendering from monocular videos. GART utilizes a mixture of moving 3D Gaussians to explicitly approximate a deformable subject's geometry and appearance. It takes advantage of a categorical template model prior (SMPL, SMAL, etc.) with learnable forward skinning while further generalizing to more complex non-rigid deformations with novel latent bones. GART can be reconstructed via differentiable rendering from monocular videos in seconds or minutes and rendered in novel poses faster than 150fps.

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Jiahui Lei, Yufu Wang, Georgios Pavlakos, Lingjie Liu, Kostas Daniilidis. 2023-11-27. GART: Gaussian Articulated Template Models. https://arxiv.org/abs/2311.16099

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