AI 中文总结
本文针对现有4DGS动态建模的不足，提出傅里叶运动建模模块与运动感知正则化策略，在N3V和Google Immersive数据集上验证了方法在复杂动态场景下的性能。
AI 中文摘要
4D高斯溅射（4DGS）凭借高效的4D高斯表示和可并行渲染，在动态3D重建与实时新视图合成中表现出色。但现有4DGS方法依赖单一多项式建模运动，在存在高频运动分量的复杂动态场景中性能受限，且因轨迹漂移累积无法保证长期稳定性。为解决这些问题，本文提出傅里叶运动建模模块：该范式将运动分解为基于频率的正弦分量，同时捕获低频全局轨迹与高频局部细节，以准确建模复杂运动模式，在保留4DGS实时渲染能力的同时，提升复杂运动拟合与长期一致性。此外，本文在损失函数中融入运动感知正则化策略：采用频率相关权重抑制高频抖动，同时保留低频运动一致性。在N3V和Google Immersive数据集的多场景实验验证了所提方法的有效性。
英文摘要
4D Gaussian Splatting (4DGS) excels in dynamic 3D reconstruction and real-time novel view synthesis via efficient 4D Gaussian representations and parallelizable rendering. However, existing 4DGS approaches rely on a single polynomial to model motion, which limits performance in complex dynamic scenes where high-frequency motion components are prevalent, and fails to ensure long-term stability due to cumulative trajectory drift. To address these issues, we propose a Fourier Motion Modeling module: this paradigm decomposes motion into frequency-based sinusoidal components, capturing both low-frequency global trajectories and high-frequency local details to model complex motion patterns accurately. It retains the real-time rendering capability of 4DGS while improving complex motion fitting and long-term coherence. Additionally, we integrate a motion-aware regularization strategy into the loss function: it uses frequency-dependent weights to suppress high-frequency jitter while preserving low-frequency motion coherence. Extensive experiments on N3V and Google Immersive datasets from multiple scenarios demonstrate the effectiveness of our method.
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