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.
