Ctrl-Crash: Controllable Diffusion for Realistic Car Crashes
Authors: name: Anthony Gosselin, name: Ge Ya Luo, name: Luis Lara, name: Florian Golemo, name: Derek Nowrouzezahrai, name: Liam Paull, name: Alexia Jolicoeur-Martineau, name: Christopher Pal

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.