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

• URL: http://arxiv.org/abs/2604.01129v1
• Date: Wed, 01 Apr 2026 16:48:20 GMT
• Title: ReinDriveGen: Reinforcement Post-Training for Out-of-Distribution Driving Scene Generation
• Abstract summary: ReinDriveGen is a framework that enables full controllability over dynamic driving scenes.<n>Our approach constructs a dynamic 3D point cloud scene from multi-frame LiDAR data.<n>Our approach renders the edited scene into 2D condition images that guide a video diffusion model to synthesize realistic driving videos.
• Score: 61.64162666162948
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present ReinDriveGen, a framework that enables full controllability over dynamic driving scenes, allowing users to freely edit actor trajectories to simulate safety-critical corner cases such as front-vehicle collisions, drifting cars, vehicles spinning out of control, pedestrians jaywalking, and cyclists cutting across lanes. Our approach constructs a dynamic 3D point cloud scene from multi-frame LiDAR data, introduces a vehicle completion module to reconstruct full 360° geometry from partial observations, and renders the edited scene into 2D condition images that guide a video diffusion model to synthesize realistic driving videos. Since such edited scenarios inevitably fall outside the training distribution, we further propose an RL-based post-training strategy with a pairwise preference model and a pairwise reward mechanism, enabling robust quality improvement under out-of-distribution conditions without ground-truth supervision. Extensive experiments demonstrate that ReinDriveGen outperforms existing approaches on edited driving scenarios and achieves state-of-the-art results on novel ego viewpoint synthesis.

Related papers

• DriveWeaver: Point-Conditioned Video Inpainting for Controllable Vehicle Insertion in Autonomous Driving Simulation [9.201369685231532]
  DriveWeaver is a novel framework for controllable vehicle insertion in autonomous driving simulation.<n>We perform video inpainting conditioned on vehicle point clouds to generate temporally consistent vehicles.<n>Our method outperforms existing baselines in visual realism and geometric consistency.
  arXiv  Detail & Related papers  (2026-06-30T16:23:32Z)
• InfiniVerse: Occupancy Guided Unbounded Scene Generation for Autonomous Driving [70.7737035270887]
  We introduce InfiniVerse, a unified pipeline for synthesis of dynamic urban scenes from a single frame.<n>InfiniVerse achieves state-of-the-art performance with a FID of 6.4 and FVD of 67.97, significantly outperforming existing benchmarks in both duration and stability.
  arXiv  Detail & Related papers  (2026-06-30T04:08:22Z)
• HorizonForge: Driving Scene Editing with Any Trajectories and Any Vehicles [63.88996084630768]
  Controllable driving scene generation is critical for realistic and scalable autonomous driving simulation.<n>We introduce HorizonForge, a unified framework that reconstructs scenes as editable Gaussian Splats and Meshes.<n>Experiments show that Gaussian-Mesh representation delivers substantially higher fidelity than alternative 3D representations.
  arXiv  Detail & Related papers  (2026-02-24T20:03:47Z)
• SymDrive: Realistic and Controllable Driving Simulator via Symmetric Auto-regressive Online Restoration [37.202523124756034]
  Current approaches often falter in large-angle novel view synthesis and suffer from geometric or lighting artifacts during asset manipulation.<n>We propose SymDrive, a unified diffusion-based framework capable of joint high-quality rendering and scene editing.<n>We demonstrate that SymDrive achieves photorealistic state-of-the-art performance in both novel-view enhancement and realistic 3D vehicle insertion.
  arXiv  Detail & Related papers  (2025-12-25T10:28:43Z)
• Optimization-Guided Diffusion for Interactive Scene Generation [52.23368750264419]
  We present OMEGA, an optimization-guided, training-free framework that enforces structural consistency and interaction awareness during diffusion-based sampling.<n>We show that OMEGA improves generation realism, consistency, and controllability, increasing the ratio of physically and behaviorally valid scenes.<n>Our approach can also generate $5times$ more near-collision frames with a time-to-collision under three seconds.
  arXiv  Detail & Related papers  (2025-12-08T15:56:18Z)
• SceneCrafter: Controllable Multi-View Driving Scene Editing [44.91248700043744]
  We propose SceneCrafter, a versatile editor for realistic 3D-consistent manipulation of driving scenes captured from multiple cameras.<n>SceneCrafter achieves state-of-the-art realism, controllability, 3D consistency, and scene editing quality compared to existing baselines.
  arXiv  Detail & Related papers  (2025-06-24T10:23:47Z)
• GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action Control [50.67481583744243]
  We introduce GeoDrive, which explicitly integrates robust 3D geometry conditions into driving world models.<n>We propose a dynamic editing module during training to enhance the renderings by editing the positions of the vehicles.<n>Our method significantly outperforms existing models in both action accuracy and 3D spatial awareness.
  arXiv  Detail & Related papers  (2025-05-28T14:46:51Z)
• StreetCrafter: Street View Synthesis with Controllable Video Diffusion Models [76.62929629864034]
  We introduce StreetCrafter, a controllable video diffusion model that utilizes LiDAR point cloud renderings as pixel-level conditions.<n>In addition, the utilization of pixel-level LiDAR conditions allows us to make accurate pixel-level edits to target scenes.<n>Our model enables flexible control over viewpoint changes, enlarging the view for satisfying rendering regions.
  arXiv  Detail & Related papers  (2024-12-17T18:58:55Z)
• Driving View Synthesis on Free-form Trajectories with Generative Prior [39.24591650300784]
  DriveX is a novel free-form driving view synthesis framework.<n>It distills generative prior into the 3D Gaussian model during its optimization.<n>It achieves high-quality view synthesis beyond recorded trajectories in real time.
  arXiv  Detail & Related papers  (2024-12-02T17:07:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.