SCas4D: Structural Cascaded Optimization for Boosting Persistent 4D
  Novel View Synthesis
Authors: name: Jipeng Lyu, name: Jiahua Dong, name: Yu-Xiong Wang
Organization: University of Illinois at Urbana-Champaign

Persistent dynamic scene modeling for tracking and novel-view synthesis
remains challenging due to the difficulty of capturing accurate deformations
while maintaining computational efficiency. We propose SCas4D, a cascaded
optimization framework that leverages structural patterns in 3D Gaussian
Splatting for dynamic scenes. The key idea is that real-world deformations
often exhibit hierarchical patterns, where groups of Gaussians share similar
transformations. By progressively refining deformations from coarse part-level
to fine point-level, SCas4D achieves convergence within 100 iterations per time
frame and produces results comparable to existing methods with only
one-twentieth of the training iterations. The approach also demonstrates
effectiveness in self-supervised articulated object segmentation, novel view
synthesis, and dense point tracking tasks.