ReinDriveGen: Reinforcement Post-Training for Out-of-Distribution Driving Scene Generation

We present ReinDriveGen, a framework that enables full con-trollability over dynamic driving scenes, allowing users to freely edit actortrajectories to simulate safety-critical corner cases such as front-vehiclecollisions, drifting cars, vehicles spinning out of control, pedestrians jay-walking, and cyclists cutting across lanes. Our approach constructs adynamic 3D point cloud scene from multi-frame LiDAR data, introducesa vehicle completion module to reconstruct full 360° geometry from par-tial observations, and renders the edited scene into 2D condition imagesthat guide a video diffusion model to synthesize realistic driving videos.Since such edited scenarios inevitably fall outside the training distri-bution, we further propose an RL-based post-training strategy with apairwise preference model and a pairwise reward mechanism, enablingrobust quality improvement under out-of-distribution conditions with-out ground-truth supervision. Extensive experiments demonstrate thatReinDriveGen outperforms existing approaches on edited driving scenar-ios and achieves state-of-the-art results on novel ego viewpoint synthesis.