CVPR 2024highlight94 citations

GART: Gaussian Articulated Template Models

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

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.

BibTeX
@inproceedings{cvpr2024_gartgaussianarti,
  title = {GART: Gaussian Articulated Template Models},
  author = {Jiahui Lei and Yufu Wang and Georgios Pavlakos and Lingjie Liu and Kostas Daniilidis},
  booktitle = {CVPR 2024},
  year = {2024}
}