Ctrl-Crash: Controllable Diffusion for Realistic Car Crashes
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.
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Anthony Gosselin
claimed_verified
2025-06-04T09:01:46.764Z
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Anthony Gosselin
user
AnthonyGosselin
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Ge Ya Luo
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Luis Lara
claimed_verified
2025-06-05T15:08:33.287Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/63e972f1ccae1fe5c6211759/AfKPgMdAraUtvbtJpoHFY.jpeg
Luis Lara
user
ludolara
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Florian Golemo
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Derek Nowrouzezahrai
683f162def3fb0fc6576af6c
Liam Paull
683f162def3fb0fc6576af6d
Alexia Jolicoeur-Martineau
claimed_verified
2025-08-06T19:36:25.723Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/1660479589894-noauth.jpeg
Alexia Jolicoeur-Martineau
user
AlexiaJM
683f162def3fb0fc6576af6e
Christopher Pal
Ctrl-Crash: Controllable Diffusion for Realistic Car Crashes
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.
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.
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