MotionGS: Exploring Explicit Motion Guidance for Deformable 3D Gaussian
  Splatting
Authors: name: Ruijie Zhu, name: Yanzhe Liang, name: Hanzhi Chang, name: Jiacheng Deng, name: Jiahao Lu, name: Wenfei Yang, name: Tianzhu Zhang, name: Yongdong Zhang

Dynamic scene reconstruction is a long-term challenge in the field of 3D
vision. Recently, the emergence of 3D Gaussian Splatting has provided new
insights into this problem. Although subsequent efforts rapidly extend static
3D Gaussian to dynamic scenes, they often lack explicit constraints on object
motion, leading to optimization difficulties and performance degradation. To
address the above issues, we propose a novel deformable 3D Gaussian splatting
framework called MotionGS, which explores explicit motion priors to guide the
deformation of 3D Gaussians. Specifically, we first introduce an optical flow
decoupling module that decouples optical flow into camera flow and motion flow,
corresponding to camera movement and object motion respectively. Then the
motion flow can effectively constrain the deformation of 3D Gaussians, thus
simulating the motion of dynamic objects. Additionally, a camera pose
refinement module is proposed to alternately optimize 3D Gaussians and camera
poses, mitigating the impact of inaccurate camera poses. Extensive experiments
in the monocular dynamic scenes validate that MotionGS surpasses
state-of-the-art methods and exhibits significant superiority in both
qualitative and quantitative results. Project page:
https://ruijiezhu94.github.io/MotionGS_page