Recent advancements in dynamic 3D scene reconstruction have shown promising
results, enabling high-fidelity 3D novel view synthesis with improved temporal
consistency. Among these, 4D Gaussian Splatting (4DGS) has emerged as an
appealing approach due to its ability to model high-fidelity spatial and
temporal variations. However, existing methods suffer from substantial
computational and memory overhead due to the redundant allocation of 4D
Gaussians to static regions, which can also degrade image quality. In this
work, we introduce hybrid 3D-4D Gaussian Splatting (3D-4DGS), a novel framework
that adaptively represents static regions with 3D Gaussians while reserving 4D
Gaussians for dynamic elements. Our method begins with a fully 4D Gaussian
representation and iteratively converts temporally invariant Gaussians into 3D,
significantly reducing the number of parameters and improving computational
efficiency. Meanwhile, dynamic Gaussians retain their full 4D representation,
capturing complex motions with high fidelity. Our approach achieves
significantly faster training times compared to baseline 4D Gaussian Splatting
methods while maintaining or improving the visual quality.
