Frequency-aware Dynamic Gaussian Splatting

We present 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 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.