Frequency-aware Dynamic Gaussian Splatting

Qiaowei Miao, JinSheng Quan, Kehan Li, Yichao Xu, Yi Yang, Yawei Luo

Keywords: 4D reconstruction, Gasussian Slpatting, Deformation network

TL;DR: Frequency-differentiated Gaussian Kernel and Fourier Deformation Network for 4D reconstruction

Abstract:

We present \textbf{Frequency-Aware Dynamic Gaussian Splatting (FAGS)}, a novel approach to mitigating motion blur in 4D reconstruction, particularly under novel viewpoints. This blur stems from a fundamental spectral conflict in existing methods, which struggle to \textbf{balance high-frequency rendering details with high-frequency motion.}
FAGS addresses this challenge with two key innovations. First, we introduce a frequency-differentiated Gaussian kernel that refines the alpha-blending process of 3D Gaussian Splatting. By adaptively classifying Gaussians into two types—a slowly varying kernel for smooth, low-frequency regions and a sharp-transitioning kernel for high-frequency boundaries—our method explicitly separates representation responsibilities, preserving fine details without sacrificing continuity.
Second, we propose a Fourier-Deformation Network that enhances motion expressiveness. This network employs high-frequency Fourier embeddings to capture diverse motion patterns by learning amplitudes across frequency components. To further improve accuracy, we integrate a frequency-aware gate in fusion module, which predicts and regulates the relative deformation of each Gaussian.
Extensive experiments on both synthetic and real-world 4D benchmarks demonstrate that FAGS significantly reduces motion blur and enhances structural details, achieving state-of-the-art performance.

Primary Area: learning on graphs and other geometries & topologies

Submission Number: 3105