Merge branch 'main' of https://huggingface.co/AnthonyGosselin/Ctrl-Crash

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README.md

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----
-license: cc-by-4.0
----
+---
+license: cc-by-4.0
+base_model:
+- stabilityai/stable-video-diffusion-img2vid-xt-1-1
+---
+
+# Model Card for Ctrl-Crash
+
+Generate car crash videos from an initial frame, using bounding-box and crash type control signals.
+
+<p align="center">
+  <table cellspacing="0" cellpadding="0">
+    <tr>
+      <td><img src="etc/genvid_57_11_04453.gif" width="512"></td>
+      <td><img src="etc/genvid_64_48_08386.gif" width="512"></td>
+      <td><img src="etc/genvid_87_21_08924.gif" width="512"></td>
+    </tr>
+  </table>
+</p>
+(Above) Examples of generated crashes
+
+
+## Model Details
+
+<p align="left">
+<img src="architecture_figure.png" width=800>
+</p>
+
+<!-- TODO: Provide a longer summary of what this model is. -->
+
+- Visit the **project page** for demos: https://anthonygosselin.github.io/Ctrl-Crash-ProjectPage/
+- Visit the **repository** to get started: https://github.com/AnthonyGosselin/Ctrl-Crash
+- Read the **paper** for more details: https://arxiv.org/abs/2506.00227
+
+
+This model uses the Stability AI Image-to-Video model (SVD 1.1) as a base model: https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt-1-1
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+<!-- TODO: Here we can describe the different operation modes (Reconstruction, Prediction and counterfactuals) -->
+
+Ctrl-Crash supports different task settings, each enabled by varying the available control signals, namely: 
+- **(1) Crash Reconstruction**: Given an initial image, full bounding box sequence, and a crash type, the model reconstructs a consistent video combining the visual context of the initial frame with agent motion derived from the bounding boxes. 
+- **(2) Crash Prediction**: Given the initial frame and only a few initial bounding box frames (e.g., 0–9), the model predicts the future motion of agents in a way that aligns with the target crash type. 
+- **(3) Crash Counterfactuals**: Extending the prediction task, this mode varies the crash type signal while keeping other inputs fixed, enabling the generation of multiple plausible outcomes for the same scene—supporting counterfactual safety reasoning. 
+
+## Bias, Risks, and Limitations
+
+Despite its strong performance, our approach has several limitations, which motivates future work in this direction. 
+
+- Counterfactual outcomes can be hard to generate when initial scene conditions conflict with the desired crash type.
+- The model relies heavily on bounding boxes, making it sensitive to tracking errors—especially in fully conditioned reconstruction.
+- With no bounding boxes conditioning, motion direction can be ambiguous, and 2D boxes struggle to capture rotation or orientation, limiting realism in behaviors like spinouts
+- Does not support text conditioning
+
+**BibTeX:** 
+```bibtex
+@misc{gosselin2025ctrlcrashcontrollablediffusionrealistic,
+      title={Ctrl-Crash: Controllable Diffusion for Realistic Car Crashes}, 
+      author={Anthony Gosselin and Ge Ya Luo and Luis Lara and Florian Golemo and Derek Nowrouzezahrai and Liam Paull and Alexia Jolicoeur-Martineau and Christopher Pal},
+      year={2025},
+      eprint={2506.00227},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2506.00227}, 
+}
+```