We introduce an approach that creates animatable human avatars from monocular
videos using 3D Gaussian Splatting (3DGS). Existing methods based on neural
radiance fields (NeRFs) achieve high-quality novel-view/novel-pose image
synthesis but often require days of training, and are extremely slow at
inference time. Recently, the community has explored fast grid structures for
efficient training of clothed avatars. Albeit being extremely fast at training,
these methods can barely achieve an interactive rendering 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. Experimental results show that our method
achieves comparable and even better performance compared to state-of-the-art
approaches on animatable avatar creation from a monocular input, while being
400x and 250x faster in training and inference, respectively.

使用 3D 高斯喷洒（3DGS），我们介绍了一种从单目视频中创建可以动画化的人体化身的方法。通过学习非刚性变形网络和引入不同性的正则化，我们的方法在训练和推理速度上比现有的技术分别快 400 倍和 250 倍，并实现了与最先进方法相媲美甚至更好的性能。