MoDGS: Dynamic Gaussian Splatting from Casually-captured Monocular Videos with Depth Priors
Qingming LIU Yuan Liu Jiepeng Wang Xianqiang Lyu Peng Wang Wenping Wang Junhui Hou
In this paper, we propose MoDGS, a new pipeline to render novel-view images in dynamic scenes using only casually captured monocular videos. Previous monocular dynamic NeRF or Gaussian Splatting methods strongly rely on the rapid movement of input cameras to construct multiview consistency but fail to reconstruct dynamic scenes on casually captured input videos whose cameras are static or move slowly. To address this challenging task, MoDGS adopts recent single-view depth estimation methods to guide the learning of the dynamic scene. Then, a novel 3D-aware initialization method is proposed to learn a reasonable deformation field and a new robust depth loss is proposed to guide the learning of dynamic scene geometry. Comprehensive experiments demonstrate that MoDGS is able to render high-quality novel view images of dynamic scenes from just a casually captured monocular video, which outperforms baseline methods by a significant margin.  Project page: https://MoDGS.github.io
3D Gaussian Splatting, Dynamic Novel-view Synthesis, Neural Rendering