发表机构
McMaster University（麦克马斯特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
EvoGS是基于3DGS的动态重建框架，将高斯变形建模为时间演化过程，通过持久状态外推、自适应校正及变形感知致密化提升动态新视角合成质量，在基准上表现具竞争力。
AI 中文摘要
3D高斯溅射（3DGS）的近期扩展工作通过学习时间条件下的高斯变形，实现了动态场景下的实时新视角合成。然而，现有的基于多层感知机（MLP）的方法通常在每个时间戳独立估计变形，导致对大位移或突发运动的鲁棒性较差。为解决该问题，我们提出了EvoGS，这是一种基于3DGS的动态重建框架，将高斯变形建模为时间演化过程。EvoGS为每个高斯维持持久的变形状态，从历史变形状态外推未来状态，并通过MLP生成的观测值校正预测结果；校正过程采用时间残差记忆及变形速度、轨迹偏差等演化统计量进行自适应加权。为进一步提升重建质量，EvoGS引入了变形感知的致密化：沿校正后的变形方向执行克隆与分裂操作，同时采用不确定性感知策略抑制变形历史不稳定的高斯的致密化。实验表明，EvoGS提升了动态新视角合成质量，并在多个基准上取得了具有竞争力的性能。
英文摘要
Recent extensions of 3D Gaussian Splatting (3DGS) enable real-time novel view synthesis in dynamic scenes by learning time-conditioned Gaussian deformations. However, existing MLP-based methods typically estimate deformations independently at each timestamp, making them less robust to large or abrupt motions. To address this issue, we propose \textbf{EvoGS}, a 3DGS-based dynamic reconstruction framework that models Gaussian deformation as a temporal evolution process. EvoGS maintains persistent deformation states for each Gaussian, extrapolates future states from historical deformation states, and corrects the predictions with MLP-derived observations. The correction is adaptively weighted using a temporal residual memory and evolution statistics such as deformation velocity and trajectory deviation. To further improve reconstruction quality, EvoGS introduces deformation-aware densification. Clone and split operations are performed along corrected deformation directions, while an uncertainty-aware strategy suppresses densification for Gaussians with unstable deformation histories. Experiments show that EvoGS improves dynamic novel view synthesis quality and achieves competitive performance across benchmarks.
CommentsAccepted by Pacific Graphics 2026 (journal track)
