Mind the Gap! Sparse-Input 3D Gaussian Splatting Constrained by Natural Video Completion
Yan Xu, Yixing Wang, Stella X. Yu
We reinterpret sparse-input novel view synthesis as completing a natural video---recovering missing frames between wide-baseline inputs. This perspective allows us to leverage strong generative priors from pretrained video diffusion models to synthesize plausible intermediate views, providing constraints for under-observed regions during 3D-GS training.We propose a \textit{zero-shot, generation-guided reconstruction} framework where pseudo views are generated at specified camera poses via a video diffusion model, guided by an \textit{uncertainty-aware modulation mechanism} for spatial coherence. These generated views provide dense supervision for training 3D-GS and serve as a bridge to \textit{densify Gaussian primitives} in under-observed regions, helping the 3D-GS model reconstruct the scene appearance and geometry. We further refine pseudo views and 3D-GS iteratively, allowing geometry-aware feedback to improve synthesis quality. Our approach enhances the 3D representation in sparse-view settings and significantly reduces rendering artifacts. Experiments on DL3DV, LLFF, and DTU demonstrate that our approach consistently outperforms baseline 3D-GS with substantial improvements.