  Recent advancements in zero-shot video diffusion models have shown promise
for text-driven video editing, but challenges remain in achieving high temporal
consistency. To address this, we introduce Video-3DGS, a 3D Gaussian Splatting
(3DGS)-based video refiner designed to enhance temporal consistency in
zero-shot video editors. Our approach utilizes a two-stage 3D Gaussian
optimizing process tailored for editing dynamic monocular videos. In the first
stage, Video-3DGS employs an improved version of COLMAP, referred to as
MC-COLMAP, which processes original videos using a Masked and Clipped approach.
For each video clip, MC-COLMAP generates the point clouds for dynamic
foreground objects and complex backgrounds. These point clouds are utilized to
initialize two sets of 3D Gaussians (Frg-3DGS and Bkg-3DGS) aiming to represent
foreground and background views. Both foreground and background views are then
merged with a 2D learnable parameter map to reconstruct full views. In the
second stage, we leverage the reconstruction ability developed in the first
stage to impose the temporal constraints on the video diffusion model. To
demonstrate the efficacy of Video-3DGS on both stages, we conduct extensive
experiments across two related tasks: Video Reconstruction and Video Editing.
Video-3DGS trained with 3k iterations significantly improves video
reconstruction quality (+3 PSNR, +7 PSNR increase) and training efficiency
(x1.9, x4.5 times faster) over NeRF-based and 3DGS-based state-of-art methods
on DAVIS dataset, respectively. Moreover, it enhances video editing by ensuring
temporal consistency across 58 dynamic monocular videos.
