FreeTimeGS++: Secrets of Dynamic Gaussian Splatting and Their Principles

TL;DR

This paper analyzes the underlying principles of 4D Gaussian Splatting, revealing key factors behind its success, and introduces FreeTimeGS++ with improved stability and dynamic scene reconstruction.

Contribution

The paper systematically dissects 4DGS, uncovers hidden drivers like temporal partitioning, and proposes FreeTimeGS++ with principled techniques for better stability and robustness.

Findings

Uncovered the role of Gaussian durations in temporal partitioning

Identified the gap between photometric fidelity and spatiotemporal consistency

Proposed FreeTimeGS++ with gated marginalization and neural velocity fields

Abstract

The recent surge in 4D Gaussian Splatting (4DGS) has achieved impressive dynamic scene reconstruction. While these methods demonstrate remarkable performance, the specific drivers behind such gains remain less explored, making a systematic understanding of the underlying principles challenging. In this paper, we perform a comprehensive analysis of these hidden factors to provide a clearer perspective on the 4DGS framework. We first establish a controlled baseline, FreeTimeGS_ours, by formalizing and reproducing the heuristics of the state-of-the-art FreeTimeGS. Using this framework, we dissect 4DGS along its fundamental axes and uncover key secrets, including the emergent temporal partitioning driven by Gaussian durations and the discrepancy between photometric fidelity and spatiotemporal consistency. Based on these insights, we propose FreeTimeGS++, a principled method that employs gated marginalization and neural velocity fields to achieve superior stability and robust dynamic representations. Our approach yields reproducible results with reduced run-to-run variance. We will release our implementation to provide a reliable foundation for future 4DGS research.