Improving Explicit Dynamic Gaussian Splatting Optimization via Update Mixture

Abstract

3D Gaussian Splatting (3DGS) enables real-time, high-fidelity view synthesis via explicit scene representations and has recently been extended to dynamic scene modeling. In spite of excellent quality and interpretability, we find explicit Dynamic GS often exhibits generalization degradation in large-motion scenes. Motivated by generalization insights from deep learning and the characteristics of Gaussian primitive optimization, we propose an update mixture strategy. This work focuses on two representative open-source explicit Dynamic GS pipelines and our approach includes: (i) a space–time dependent Strictly Sparse Update with additional regularization to stabilize adaptive updates; (ii) a constant-corrected adaptive algorithm to attenuate the over-scaling of primitive gradients, yielding a stable mixture of adaptive and non-adaptive steps; and (iii) attributes mixture via Stochastic Attribute Averaging to mitigate frame-preference under motion disturbances. Experiments show consistent improvements and reduced generalization issues, highlighting the role of non-adaptive updates and the influence from frame-preference in explicit Dynamic GS optimization.