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 minutes and rendered at real-time frame rates (50+ FPS). Given the explicit nature of our representation, we further introduce as-isometric-as-possible regularizations on both the Gaussian mean vectors and the covariance matrices, enhancing the generalization of our model on highly articulated unseen poses. Experi-mental results show that our method achieves comparable and even better performance compared to state-of-the-art approaches on animatable avatar creation from a monoc-ular input, while being 400x and 250x faster in training and inference, respectively. Please see our project page at https://neuralbodies.github.ioI3DGS-Avatar.