BAGS: Building Animatable Gaussian Splatting from a Monocular Video with
  Diffusion Priors
Tingyang Zhang Qingzhe Gao Weiyu Li Libin Liu Baoquan Chen
Animatable 3D reconstruction has significant applications across various
fields, primarily relying on artists' handcraft creation. Recently, some
studies have successfully constructed animatable 3D models from monocular
videos. However, these approaches require sufficient view coverage of the
object within the input video and typically necessitate significant time and
computational costs for training and rendering. This limitation restricts the
practical applications. In this work, we propose a method to build animatable
3D Gaussian Splatting from monocular video with diffusion priors. The 3D
Gaussian representations significantly accelerate the training and rendering
process, and the diffusion priors allow the method to learn 3D models with
limited viewpoints. We also present the rigid regularization to enhance the
utilization of the priors. We perform an extensive evaluation across various
real-world videos, demonstrating its superior performance compared to the
current state-of-the-art methods.