Given just a few glimpses of a scene, can you imagine the movie playing out as the camera glides through it? That's the lens we take on \emph{ sparse-input novel view synthesis}, not only as filling spatial gaps between widely spaced views, but also as \emph{ completing a natural video} unfolding through space. We recast the task as \emph{ test-time natural video completion}, using powerful priors from \emph{ pretrained video diffusion models} to hallucinate plausible in-between views. Our \emph{ zero-shot, generation-guided} framework produces pseudo views at novel camera poses, modulated by an \emph{ uncertainty-aware mechanism} for spatial coherence. These synthesized frames densify supervision for \emph{ 3D Gaussian Splatting} (3D-GS) for scene reconstruction, especially in under-observed regions. An iterative feedback loop lets 3D geometry and 2D view synthesis inform each other, improving both the scene reconstruction and the generated views. The result is coherent, high-fidelity renderings from sparse inputs \emph{ without any scene-specific training or fine-tuning}. On LLFF, DTU, DL3DV, and MipNeRF-360, our method significantly outperforms strong 3D-GS baselines under extreme sparsity.
Categories: cs.CV cs.GR