3DGS-Avatar: Animatable Avatars via Deformable 3D Gaussian Splatting

We introduce an approach that creates animatable hu-man avatars from monocular videos using 3D Gaussian Splatting (3DGS). Existing methods based on neural radi-ance fields (NeRFs) achieve high-quality novel-viewlnovel-pose image synthesis but often require days of training, and are extremely slow at inference time. Recently, the com-munity has explored fast grid structures for efficient training of clothed avatars. Albeit being extremely fast at training, these methods can barely achieve an interactive ren-de ring frame rate with around 15 FPS. In this paper, we use 3D Gaussian Splatting and learn a non-rigid deformation network to reconstruct animatable clothed human avatars that can be trained within 30…