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.
我们介绍了高斯铰接模板模型GART,这是一种明确的、高效的、表现力丰富的表示方法,用于从单眼视频捕捉和渲染非刚性铰接主体。GART利用移动的3D高斯混合显式近似可变形主体的几何和外观。它利用分类模板模型先验(如SMPL、SMAL等)以及可学习的前向蒙皮技术,同时通过新颖的潜在骨骼进一步泛化到更复杂的非刚性变形。GART可以通过单眼视频的可微分渲染在几秒或几分钟内重建,并且在新姿势下的渲染速度超过150fps。