FreeTimeGS++: Secrets of Dynamic Gaussian Splatting and Their Principles
For researchers in dynamic scene reconstruction, this work provides a systematic understanding and principled improvements to 4DGS, though the gains are incremental over existing methods.
The paper analyzes hidden factors driving performance in 4D Gaussian Splatting, revealing emergent temporal partitioning and fidelity-consistency discrepancies, then proposes FreeTimeGS++ with gated marginalization and neural velocity fields for improved stability and robust dynamic representations.
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.