DriveWeaver: Point-Conditioned Video Inpainting for Controllable Vehicle Insertion in Autonomous Driving Simulation

• URL: http://arxiv.org/abs/2606.31918v2
• Date: Thu, 02 Jul 2026 07:50:25 GMT
• Title: DriveWeaver: Point-Conditioned Video Inpainting for Controllable Vehicle Insertion in Autonomous Driving Simulation
• Abstract summary: 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.
• Score: 9.201369685231532
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A pivotal step in autonomous driving simulation involves inserting foreground vehicles with predefined trajectories into simulated scenes. This process enhances scene diversity and facilitates the creation of various corner cases for testing and improving autonomous driving models. However, existing methods often rely on pre-reconstructed 3D assets, which frequently lead to lighting inconsistencies between the inserted foreground and the background. Moreover, the reliance on limited, manually-curated 3D assets hinders large-scale deployment. To address these challenges, we propose DriveWeaver, a novel framework for controllable vehicle insertion in autonomous driving simulation. Specifically, for a masked target insertion area, DriveWeaver performs video inpainting conditioned on vehicle point clouds to generate high-quality, temporally consistent vehicles. This video-inpainting-based approach ensures seamless blending between the foreground and background, while the readily available point cloud conditions enable superior generalization. To support long-term generation, we further design a global-to-local hierarchical inpainting strategy, ensuring the consistent identity and appearance of the inserted vehicles. Meanwhile, we extract explicit 3D Gaussian representations of the inserted vehicles through an urban reconstruction pipeline to enable real-time rendering for autonomous driving simulation. Extensive experiments across diverse datasets demonstrate that our method outperforms existing baselines in visual realism and geometric consistency, providing a robust tool for scalable autonomous driving scene augmentation.

Related papers

• 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)
• FAR-Drive: Frame-AutoRegressive Video Generation in Closed-Loop Autonomous Driving [11.275815014211046]
  Building a learning-based closed-loop simulator for autonomous driving poses three major challenges.<n>Long-horizon temporal and cross-view consistency, mitigating autoregressive degradation under iterative self-conditioning, and satisfying low-latency inference constraints.<n>We propose FAR-Drive, a frame-level autoregressive video generation framework for autonomous driving.
  arXiv  Detail & Related papers  (2026-03-16T07:40:13Z)
• 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)
• InstaDrive: Instance-Aware Driving World Models for Realistic and Consistent Video Generation [53.47253633654885]
  InstaDrive is a novel framework that enhances driving video realism through two key advancements.<n>By incorporating these instance-aware mechanisms, InstaDrive achieves state-of-the-art video generation quality.<n>Our project page is https://shanpoyang654.io/InstaDrive/page.html.
  arXiv  Detail & Related papers  (2026-02-03T08:22:13Z)
• 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)
• 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)
• Stag-1: Towards Realistic 4D Driving Simulation with Video Generation Model [83.31688383891871]
  We propose a Spatial-Temporal simulAtion for drivinG (Stag-1) model to reconstruct real-world scenes.<n>Stag-1 constructs continuous 4D point cloud scenes using surround-view data from autonomous vehicles.<n>It decouples spatial-temporal relationships and produces coherent driving videos.
  arXiv  Detail & Related papers  (2024-12-06T18:59:56Z)
• AutoSplat: Constrained Gaussian Splatting for Autonomous Driving Scene Reconstruction [17.600027937450342]
  AutoSplat is a framework employing Gaussian splatting to achieve highly realistic reconstructions of autonomous driving scenes. Our method enables multi-view consistent simulation of challenging scenarios including lane changes.
  arXiv  Detail & Related papers  (2024-07-02T18:36:50Z)
• CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation [44.83732884335725]
  Sensor simulation involves modeling traffic participants, such as vehicles, with high quality appearance and articulated geometry. Current reconstruction approaches struggle on in-the-wild sensor data, due to its sparsity and noise. We present CADSim, which combines part-aware object-class priors via a small set of CAD models with differentiable rendering to automatically reconstruct vehicle geometry.
  arXiv  Detail & Related papers  (2023-11-02T17:56:59Z)
• Multi-Modal Fusion Transformer for End-to-End Autonomous Driving [59.60483620730437]
  We propose TransFuser, a novel Multi-Modal Fusion Transformer, to integrate image and LiDAR representations using attention. Our approach achieves state-of-the-art driving performance while reducing collisions by 76% compared to geometry-based fusion.
  arXiv  Detail & Related papers  (2021-04-19T11:48:13Z)

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.