将4D高斯点阵重构成可学习的动力系统，实现高效动态场景建模与时间外推。
4D Gaussian Splatting as a Learned Dynamical System
- 用神经动力场替代逐帧变形，让高斯点随时间连续演化。
- 在稀疏时间监督下仍能高效学习，支持前后时间外推。
- 支持局部动态注入，适合可控场景生成，实时渲染且运动更连贯。
我们将4D高斯点阵重新解释为连续时间动力系统，其中场景运动由学习的神经动力场积分产生，而非逐帧变形。这一方法称为EvoGS，将高斯表示视为一个受学习运动规律驱动的连续演化物理系统。该框架实现了传统变形方法缺失的能力：(1) 在稀疏时间监督下实现样本高效的运动规律学习；(2) 支持时间外推，可进行超观测范围的前向与后向预测；(3) 具备组合式动态特性，支持局部动态注入以实现可控场景合成。在动态场景基准上的实验表明，EvoGS在运动连贯性和时间一致性上优于基于变形场的基线方法，同时保持实时渲染能力。
原文摘要 · Abstract (English)
We reinterpret 4D Gaussian Splatting as a continuous-time dynamical system, where scene motion arises from integrating a learned neural dynamical field rather than applying per-frame deformations. This formulation, which we call EvoGS, treats the Gaussian representation as an evolving physical system whose state evolves continuously under a learned motion law. This unlocks capabilities absent in deformation-based approaches:(1) sample-efficient learning from sparse temporal supervision by modeling the underlying motion law; (2) temporal extrapolation enabling forward and backward prediction beyond observed time ranges; and (3) compositional dynamics that allow localized dynamics injection for controllable scene synthesis. Experiments on dynamic scene benchmarks show that EvoGS achieves better motion coherence and temporal consistency compared to deformation-field baselines while maintaining real-time rendering
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