OccGSGen: Persistent Gaussian World Generation through Occupancy-Constrained State Updates

Controllable driving scene generation can provide diverse visual observations for training and evaluating autonomous driving systems. A central challenge is to generate newly revealed regions while maintaining consistent geometry and appearance across cameras and over time. We present OccGSGen, a framework for generating persistent Gaussian worlds from semantic occupancy, camera poses, and tracked object motion. The framework builds a shared 3D scene by integrating appearance from a video diffusion model through incremental updates constrained by occupancy, visibility, and surface correspondence. A persistent surface memory separates appearance from rendering primitives, allowing additional Gaussians to extend surface coverage without regenerating the appearance of known surfaces. We further supervise newly stored appearance through later observations of the same surfaces. Together, these mechanisms allow the generated world to expand while preserving appearance for reuse across views and time. Experiments on nuScenes demonstrate the effectiveness of OccGSGen in generating driving scenes with consistent geometry and persistent appearance.