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DrivingDiffusion: Layout-Guided multi-view driving scene video generation with latent diffusion model

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arxiv 2310.07771 v1 pith:EFYFEHFF submitted 2023-10-11 cs.CV cs.AI

Xiaofan Li , Yifu Zhang , Xiaoqing Ye This is my paper
classification cs.CVcs.AI
keywords multi-viewgenerationvideoconsistencydrivingmodeldrivingdiffusionlarge-scale
verification ladder T0 review T1 audit T2 compute T3 formal
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With the increasing popularity of autonomous driving based on the powerful and unified bird's-eye-view (BEV) representation, a demand for high-quality and large-scale multi-view video data with accurate annotation is urgently required. However, such large-scale multi-view data is hard to obtain due to expensive collection and annotation costs. To alleviate the problem, we propose a spatial-temporal consistent diffusion framework DrivingDiffusion, to generate realistic multi-view videos controlled by 3D layout. There are three challenges when synthesizing multi-view videos given a 3D layout: How to keep 1) cross-view consistency and 2) cross-frame consistency? 3) How to guarantee the quality of the generated instances? Our DrivingDiffusion solves the problem by cascading the multi-view single-frame image generation step, the single-view video generation step shared by multiple cameras, and post-processing that can handle long video generation. In the multi-view model, the consistency of multi-view images is ensured by information exchange between adjacent cameras. In the temporal model, we mainly query the information that needs attention in subsequent frame generation from the multi-view images of the first frame. We also introduce the local prompt to effectively improve the quality of generated instances. In post-processing, we further enhance the cross-view consistency of subsequent frames and extend the video length by employing temporal sliding window algorithm. Without any extra cost, our model can generate large-scale realistic multi-camera driving videos in complex urban scenes, fueling the downstream driving tasks. The code will be made publicly available.

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Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Streaming Multi-Agent Autoregressive Diffusion Model with World State Registers

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Adding persistently updated, supervised world-state register tokens to streaming multi-agent diffusion improves cross-agent consistency and visual quality in two-agent Minecraft generation.

  2. Reference-Guided Diffusion Inpainting For Multimodal Counterfactual Generation

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A single reference image guides a diffusion model to insert coherent objects into camera-plus-lidar driving scenes and to insert mammographic anomalies into new scans.

  3. SceneCrafter: Controllable Multi-View Driving Scene Editing

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A multi-view diffusion editor that applies global changes (weather, time) and local changes (vehicle insert/remove) to real driving logs with 3D consistency.

  4. Dreamland: Controllable World Creation with Simulator and Generative Models

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A three-stage hybrid pipeline uses an intermediate layered world representation to refine simulator-rendered driving scenes into realistic, controllable images and videos.

  5. GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action Control

    cs.CV 2025-05 conditional novelty 6.0 of 10

    GeoDrive conditions a frozen video diffusion model on a 3D-rendered version of the requested ego trajectory, cutting trajectory-following error by 42% versus Vista while using 99.7% less training data.

  6. ProphetDWM: A Driving World Model for Rolling Out Future Actions and Videos

    cs.CV 2025-05 conditional novelty 6.0 of 10

    ProphetDWM is a one-stage diffusion world model that jointly predicts future driving video and low-level actions from a current frame and a short action sequence.

  7. Diffusion-Based Generative Models for 3D Occupancy Prediction in Autonomous Driving

    cs.CV 2025-05 conditional novelty 5.0 of 10

    Diffusion-based generative models, using discrete categorical diffusion conditioned on BEV features, improve 3D occupancy prediction and downstream planning for autonomous driving.

  8. 2nd Place Solution for CVPR2024 E2E Challenge: End-to-End Autonomous Driving Using Vision Language Model

    cs.CV 2025-09 conditional novelty 3.0 of 10

    A single-camera vision-language-model system scored 0.8747 on the CVPR 2024 E2E driving benchmark, the best camera-only result.

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