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arXiv · 2510.06694

SCas4D: Structural Cascaded Optimization for Boosting Persistent 4D Novel View Synthesis

Jipeng Lyu·Jiahua Dong·Yu-Xiong Wang
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Abstract

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.

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Jipeng Lyu, Jiahua Dong, Yu-Xiong Wang. 2025-10-08. SCas4D: Structural Cascaded Optimization for Boosting Persistent 4D Novel View Synthesis. https://arxiv.org/abs/2510.06694

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