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.

在本文中，我们提出了 MoDGS，这是一个新的管道，可以利用仅有的随意捕捉的单目视频生成动态场景的新视角图像。MoDGS 采用了最近的单视图深度估计方法来指导动态场景的学习，并提出了一种新的三维感知初始化方法来学习合理的变形场和新的鲁棒深度损失来指导动态场景几何结构的学习。全面的实验证明，MoDGS 能够从仅有的随意捕捉的单目视频中生成高质量的动态场景新视角图像，优于基线方法的效果显著。