FillGS: Filling Observation Gaps in 4D Gaussian Splatting via Viewpoint-Time Selection and Generative Refinement

4D Gaussian Splatting (4DGS) can render dynamic scenesphotorealistically. However, with limited viewpoint coverage, some spa-tiotemporal regions remain sparsely observed, leading to artifacts, partic-ularly in scenes with large motion. Existing approaches leveraging gener-ative models rely on heuristic virtual-viewpoint selection before refiningrendered views. As a result, they cannot actively explore such sparselyobserved regions. To address this issue, we propose a pipeline that ac-tively selects spatiotemporal virtual viewpoints to improve 4DGS recon-struction. Our method selects virtual viewpoints for generative enhance-ment based on the rendering sensitivity and motion-aware observationdensity of 4D Gaussians, prioritizing views that alleviate observationsparsity. In the refined images, we filter out regions that conflict withcaptured observations or are likely to contain generative artifacts andthen fine-tune 4DGS using only the reliable regions. We evaluate ourmethod on multi-view video benchmarks using new train/test splits de-signed to induce observation gaps. Results show consistent improvementsover prior viewpoint selection strategies and fine-tuning methods in bothqualitative and quantitative evaluations, while reducing artifacts. 1