Sparse4DGS: Flow-Geometry Assisted 4D Gaussian Splatting for Dynamic Sparse View Synthesis

摘要

Previous dynamic view synthesis works often struggle with limited available views, resulting in noticeable artifacts and blurriness in the outputs. In this paper, we present Sparse4DGS, a novel 4D Gaussian splatting framework that enables high-fidelity view synthesis from sparse inputs through three key innovations: 1) a global-local feature extraction encoder that integrates global Hexplane fields with local hash grids, effectively capturing both rough backgrounds and fine details; 2) a flow-guided feature aggregation module that stabilizes dynamic 3D Gaussians between adjacent frames, ensuring temporal continuity; and 3) a 4D geometry constraint scheme that utilizes monocular depth and pseudo-viewpoint depth supervision to enhance the structural consistency of dynamic scene details. Our approach achieves higher rendering quality while maintaining model compactness. Experimental results on various benchmarks demonstrate that our method performs favorably against state-of-the-art methods in terms of both rendering quality and training time. The source code and trained models are available at https://github.com/hu-dong-dong/Sparse4DGS.