让动态3D高斯点云更准地追踪运动，减少背景伪静态残留。
MVFusion-GS: Motion-Variance Guided Temporal Attention for High-Quality Dynamic Gaussian Splatting
- 用运动方差引导变形，区分动/静区域
- 引入时序注意力建模短时运动关联，提升一致性
- 适合需要精准动态重建的场景，如视频生成
3D高斯点云（3DGS）可实现实时静态场景的新视角合成。将其扩展至动态场景需依赖形变场，近期研究聚焦于动态场景重建与去干扰重建。然而现有形变网络缺乏显式运动感知：既未捕捉长期运动强度，也未利用短期时序一致性，导致前景形变不准、背景出现伪静态残留。本文提出MVFusion-GS，通过两种互补的运动感知机制增强形变网络。运动方差引导优化模块在时间维度聚合每个高斯点的形变统计量，估计运动方差，并用于指导形变预测中的动-静分离。运动变换器时序注意力模块在邻近时间步上应用Transformer自注意力，建模局部运动依赖性，提升时序一致性。在动态场景重建与去干扰重建基准上的大量实验表明，该方法达到当前最优性能，证明显式运动感知能同时提升前景运动建模与静态背景重建质量。
原文摘要 · Abstract (English)
3D Gaussian Splatting (3DGS) enables real-time novel view synthesis for static scenes. Extending it to dynamic scenes via deformation fields has recently attracted significant attention, particularly for dynamic scene reconstructionband distractor-free. However, existing deformation networks lack explicit motion awareness: they neither capture long-term motion intensity nor exploit short-term temporal coherence, leading to inaccurate foreground deformation and pseudo-static residuals in the background. We present MVFusion-GS, a method that enhances deformation networks with two complementary motion-aware mechanisms. The Motion-Variance Guided Refinement aggregates per-Gaussian deformation statistics across time to estimate motion variance and uses it to guide dynamic-static separation during deformation prediction. The MotionFormer Temporal Attention module applies Transformer self-attention over neighboring timesteps to model local motion dependencies and improve temporal consistency. Extensive experiments on both dynamic scene reconstruction and distractor-free reconstruction benchmarks demonstrate state-of-the-art performance, showing that explicit motion awareness improves both foreground motion modeling and static background reconstruction.
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