title: Ctrl-Crash: Controllable Diffusion for Realistic Car Crashes
summary: Video diffusion techniques have advanced significantly in recent years;
  however, they struggle to generate realistic imagery of car crashes due to the
  scarcity of accident events in most driving datasets. Improving traffic safety
  requires realistic and controllable accident simulations. To tackle the
  problem, we propose Ctrl-Crash, a controllable car crash video generation model
  that conditions on signals such as bounding boxes, crash types, and an initial
  image frame. Our approach enables counterfactual scenario generation where
  minor variations in input can lead to dramatically different crash outcomes. To
  support fine-grained control at inference time, we leverage classifier-free
  guidance with independently tunable scales for each conditioning signal.
  Ctrl-Crash achieves state-of-the-art performance across quantitative video
  quality metrics (e.g., FVD and JEDi) and qualitative measurements based on a
  human-evaluation of physical realism and video quality compared to prior
  diffusion-based methods.
paper_authors._id: 683f162def3fb0fc6576af67
paper_authors.name: Anthony Gosselin
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-04T09:01:46.764Z
paper_authors._id: 66feec9a1add69636bd1fff9
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/66feec9a1add69636bd1fff9/U2NWIli3ibS7JWdl98Ftp.png
paper_authors.fullname: Anthony Gosselin
paper_authors.type: user
paper_authors.user: AnthonyGosselin
paper_authors._id: 683f162def3fb0fc6576af68
paper_authors.name: Ge Ya Luo
paper_authors._id: 683f162def3fb0fc6576af69
paper_authors.name: Luis Lara
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-05T15:08:33.287Z
paper_authors._id: 63e972f1ccae1fe5c6211759
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63e972f1ccae1fe5c6211759/AfKPgMdAraUtvbtJpoHFY.jpeg
paper_authors.fullname: Luis Lara
paper_authors.type: user
paper_authors.user: ludolara
paper_authors._id: 683f162def3fb0fc6576af6a
paper_authors.name: Florian Golemo
paper_authors._id: 683f162def3fb0fc6576af6b
paper_authors.name: Derek Nowrouzezahrai
paper_authors._id: 683f162def3fb0fc6576af6c
paper_authors.name: Liam Paull
paper_authors._id: 683f162def3fb0fc6576af6d
paper_authors.name: Alexia Jolicoeur-Martineau
paper_authors._id: 683f162def3fb0fc6576af6e
paper_authors.name: Christopher Pal
paper_title: Ctrl-Crash: Controllable Diffusion for Realistic Car Crashes
paper_summary: Video diffusion techniques have advanced significantly in recent years;
  however, they struggle to generate realistic imagery of car crashes due to the
  scarcity of accident events in most driving datasets. Improving traffic safety
  requires realistic and controllable accident simulations. To tackle the
  problem, we propose Ctrl-Crash, a controllable car crash video generation model
  that conditions on signals such as bounding boxes, crash types, and an initial
  image frame. Our approach enables counterfactual scenario generation where
  minor variations in input can lead to dramatically different crash outcomes. To
  support fine-grained control at inference time, we leverage classifier-free
  guidance with independently tunable scales for each conditioning signal.
  Ctrl-Crash achieves state-of-the-art performance across quantitative video
  quality metrics (e.g., FVD and JEDi) and qualitative measurements based on a
  human-evaluation of physical realism and video quality compared to prior
  diffusion-based methods.
paper_ai_summary: Ctrl-Crash, a controllable car crash video generation model using classifier-free guidance, achieves top performance in video quality and realism compared to existing diffusion-based methods.
paper_ai_keywords: video diffusion techniques car crashes driving datasets accident simulations classifier-free guidance bounding boxes crash types initial image frame counterfactual scenario generation FVD JEDi video quality physical realism