Novel view synthesis for dynamic scenes is still a challenging problem in
computer vision and graphics. Recently, Gaussian splatting has emerged as a
robust technique to represent static scenes and enable high-quality and
real-time novel view synthesis. Building upon this technique, we propose a new
representation that explicitly decomposes the motion and appearance of dynamic
scenes into sparse control points and dense Gaussians, respectively. Our key
idea is to use sparse control points, significantly fewer in number than the
Gaussians, to learn compact 6 DoF transformation bases, which can be locally
interpolated through learned interpolation weights to yield the motion field of
3D Gaussians. We employ a deformation MLP to predict time-varying 6 DoF
transformations for each control point, which reduces learning complexities,
enhances learning abilities, and facilitates obtaining temporal and spatial
coherent motion patterns. Then, we jointly learn the 3D Gaussians, the
canonical space locations of control points, and the deformation MLP to
reconstruct the appearance, geometry, and dynamics of 3D scenes. During
learning, the location and number of control points are adaptively adjusted to
accommodate varying motion complexities in different regions, and an ARAP loss
following the principle of as rigid as possible is developed to enforce spatial
continuity and local rigidity of learned motions. Finally, thanks to the
explicit sparse motion representation and its decomposition from appearance,
our method can enable user-controlled motion editing while retaining
high-fidelity appearances. Extensive experiments demonstrate that our approach
outperforms existing approaches on novel view synthesis with a high rendering
speed and enables novel appearance-preserved motion editing applications.

基于高斯散开技术的稀疏控制点和稠密高斯的运动与外观分解，用于动态场景的新视角合成。