mlsanityData to 7 Oct 2026

BEVControl: Accurately Controlling Street-view Elements with Multi-perspective Consistency via BEV Sketch Layout

arXiv 2308.01661 ↗First posted 3 Aug 2023cs.CVcited by 102

Not on the map. Why?

Cited by 44 in End-to-end autonomous driving, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

Using synthesized images to boost the performance of perception models is a long-standing research challenge in computer vision. It becomes more eminent in visual-centric autonomous driving systems with multi-view cameras as some long-tail scenarios can never be collected. Guided by the BEV segmentation layouts, the existing generative networks seem to synthesize photo-realistic street-view images when evaluated solely on scene-level metrics. However, once zoom-in, they usually fail to produce accurate foreground and background details such as heading. To this end, we propose a two-stage generative method, dubbed BEVControl, that can generate accurate foreground and background contents. In contrast to segmentation-like input, it also supports sketch style input, which is more flexible for humans to edit. In addition, we propose a comprehensive multi-level evaluation protocol to fairly compare the quality of the generated scene, foreground object, and background geometry. Our extensive experiments show that our BEVControl surpasses the state-of-the-art method, BEVGen, by a significant margin, from 5.89 to 26.80 on foreground segmentation mIoU. In addition, we show that using images generated by BEVControl to train the downstream perception model, it achieves on average 1.29 improvement in NDS score.

Key facts. Cited by 102 papers in the map (data through 7 Oct 2026). Builds on 35 of its 37 references that are in the map. First posted on arXiv 3 Aug 2023.

Builds on

35 of 37 references.

References · 35

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  3. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  4. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  5. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
  6. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  7. 2017Neural Discrete Representation Learningcited by 4,650
  8. 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
  9. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  10. 2022A ConvNet for the 2020scited by 3,545
  11. 2023T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Modelscited by 1,339
  12. 2022BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformerscited by 1,213
23 more
  1. 2022MaskGIT: Masked Generative Image Transformercited by 1,082
  2. 2022Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generationcited by 958
  3. 2023GLIGEN: Open-Set Grounded Text-to-Image Generationcited by 774
  4. 2023Text2Video-Zero: Text-to-Image Diffusion Models are Zero-Shot Video Generatorscited by 666
  5. 2023MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generationcited by 529
  6. 2023ControlVideo: Training-free Controllable Text-to-Video Generationcited by 324
  7. 2023Composer: Creative and Controllable Image Synthesis with Composable Conditionscited by 303
  8. 2022Cross-view Transformers for real-time Map-view Semantic Segmentationcited by 264
  9. 2023LayoutDiffusion: Controllable Diffusion Model for Layout-to-image Generationcited by 258
  10. 2021FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Camerascited by 239
  11. 2022SpaText: Spatio-Textual Representation for Controllable Image Generationcited by 217
  12. 2023MVDiffusion: Enabling Holistic Multi-view Image Generation with Correspondence-Aware Diffusioncited by 210
  13. 2022BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Drivingcited by 191
  14. 2022Sketch-Guided Text-to-Image Diffusion Modelscited by 180
  15. 2022ReCo: Region-Controlled Text-to-Image Generationcited by 177
  16. 2022Semantic Image Synthesis via Diffusion Modelscited by 131
  17. 2023Street-View Image Generation from a Bird's-Eye View Layoutcited by 102
  18. 2023Freestyle Layout-to-Image Synthesiscited by 67
  19. 2023LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generationcited by 65
  20. 2022StretchBEV: Stretching Future Instance Prediction Spatially and Temporallycited by 47
  21. 2023MaskSketch: Unpaired Structure-guided Masked Image Generationcited by 27
  22. 2023Modulating Pretrained Diffusion Models for Multimodal Image Synthesiscited by 23
  23. 2023Video ControlNet: Towards Temporally Consistent Synthetic-to-Real Video Translation Using Conditional Image Diffusion Modelscited by 20

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 11; 2024: 33; 2025: 38; 2026: 20.

102 papers.

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  1. 2023Driving into the Future: Multiview Visual Forecasting and Planning with World Model for Autonomous Drivingcited by 309
  2. 2023DriveDreamer: Towards Real-world-driven World Models for Autonomous Drivingcited by 258
  3. 2023MagicDrive: Street View Generation with Diverse 3D Geometry Controlcited by 250
  4. 2023OccWorld: Learning a 3D Occupancy World Model for Autonomous Drivingcited by 230
  5. 2024DriveDreamer-2: LLM-Enhanced World Models for Diverse Driving Video Generationcited by 186
  6. 2023Panacea: Panoramic and Controllable Video Generation for Autonomous Drivingcited by 165
  7. 2024GenAD: Generalized Predictive Model for Autonomous Drivingcited by 150
  8. 2023LLM4Drive: A Survey of Large Language Models for Autonomous Drivingcited by 136
  9. 2023Vision Language Models in Autonomous Driving: A Survey and Outlookcited by 124
  10. 2023ADriver-I: A General World Model for Autonomous Drivingcited by 102
  11. 2024UniScene: Unified Occupancy-centric Driving Scene Generationcited by 102
  12. 2024DriveArena: A Closed-loop Generative Simulation Platform for Autonomous Drivingcited by 98
38 more
  1. 2023WoVoGen: World Volume-aware Diffusion for Controllable Multi-camera Driving Scene Generationcited by 93
  2. 2024Editable Scene Simulation for Autonomous Driving via Collaborative LLM-Agentscited by 91
  3. 2024MagicDrive-V2: High-Resolution Long Video Generation for Autonomous Driving with Adaptive Controlcited by 90
  4. 2024GaussianWorld: Gaussian World Model for Streaming 3D Occupancy Predictioncited by 66
  5. 20253D and 4D World Modeling: A Surveycited by 64
  6. 2025A Survey of World Models for Autonomous Drivingcited by 60
  7. 2024Unleashing Generalization of End-to-End Autonomous Driving with Controllable Long Video Generationcited by 54
  8. 2024Doe-1: Closed-Loop Autonomous Driving with Large World Modelcited by 51
  9. 2024TOD3Cap: Towards 3D Dense Captioning in Outdoor Scenescited by 40
  10. 2024Streetscapes: Large-scale Consistent Street View Generation Using Autoregressive Video Diffusioncited by 39
  11. 2025DiST-4D: Disentangled Spatiotemporal Diffusion with Metric Depth for 4D Driving Scene Generationcited by 39
  12. 2025Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysiscited by 37
  13. 2024DreamDrive: Generative 4D Scene Modeling from Street View Imagescited by 35
  14. 2024SimGen: Simulator-conditioned Driving Scene Generationcited by 34
  15. 2024DreamForge: Motion-Aware Autoregressive Video Generation for Multi-View Driving Scenescited by 34
  16. 2024DrivingSphere: Building a High-fidelity 4D World for Closed-loop Simulationcited by 34
  17. 2024SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Controlcited by 32
  18. 2025OmniNWM: Omniscient Driving Navigation World Modelscited by 32
  19. 2023Towards Knowledge-driven Autonomous Drivingcited by 29
  20. 2024DriveScape: Towards High-Resolution Controllable Multi-View Driving Video Generationcited by 22
  21. 2025WorldLens: Full-Spectrum Evaluations of Driving World Models in Real Worldcited by 22
  22. 2024HoloDrive: Holistic 2D-3D Multi-Modal Street Scene Generation for Autonomous Drivingcited by 21
  23. 2025Genesis: Multimodal Driving Scene Generation with Spatio-Temporal and Cross-Modal Consistencycited by 21
  24. 2025X-Scene: Large-Scale Driving Scene Generation with High Fidelity and Flexible Controllabilitycited by 20
  25. 2024DriveEditor: A Unified 3D Information-Guided Framework for Controllable Object Editing in Driving Scenescited by 17
  26. 2024Urban Scene Diffusion through Semantic Occupancy Mapcited by 14
  27. 2024SyntheOcc: Synthesize Geometric-Controlled Street View Images through 3D Semantic MPIscited by 14
  28. 2024Bench2Drive-R: Turning Real World Data into Reactive Closed-Loop Autonomous Driving Benchmark by Generative Modelcited by 14
  29. 2025Rethinking Driving World Model as Synthetic Data Generator for Perception Taskscited by 14
  30. 2024Text2Street: Controllable Text-to-image Generation for Street Viewscited by 13
  31. 2024X-Drive: Cross-modality consistent multi-sensor data synthesis for driving scenarioscited by 13
  32. 2025DriveGEN: Generalized and Robust 3D Detection in Driving via Controllable Text-to-Image Diffusion Generationcited by 13
  33. 2024PerLDiff: Controllable Street View Synthesis Using Perspective-Layout Diffusion Modelscited by 12
  34. 2024Physical Informed Driving World Modelcited by 12
  35. 2025Unraveling the Effects of Synthetic Data on End-to-End Autonomous Drivingcited by 12
  36. 2025CoGen: 3D Consistent Video Generation via Adaptive Conditioning for Autonomous Drivingcited by 12
  37. 2024Seeing Beyond Views: Multi-View Driving Scene Video Generation with Holistic Attentioncited by 10
  38. 2024OccScene: Semantic Occupancy-based Cross-task Mutual Learning for 3D Scene Generationcited by 10

50 most cited of 102 All 102 (JSON)