Portrait of Anthony Gosselin

Anthony Gosselin

Master's Research - Polytechnique Montréal
Supervisor
Chris Pal
Research Topics
Reinforcement Learning
Website
Google Scholar
Bluesky
GitHub

Publications

Ctrl-Crash: Controllable Diffusion for Realistic Car Crashes
Anthony Gosselin
Ge Ya Luo
Luis Lara
Florian Golemo
Derek Nowrouzezahrai
Liam Paull
Alexia Jolicoeur-Martineau
Chris Pal
Video diffusion techniques have advanced significantly in recent years; however, they struggle to generate realistic imagery of car crashes … (see more)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.
2025-06-01
arXiv (published)
doi.org
arxiv.org
Ctrl-Crash: Controllable Diffusion for Realistic Car Crashes
Anthony Gosselin
Ge Ya Luo
Luis Lara
Florian Golemo
Derek Nowrouzezahrai
Liam Paull
Alexia Jolicoeur-Martineau
Chris Pal
Video diffusion techniques have advanced significantly in recent years; however, they struggle to generate realistic imagery of car crashes … (see more)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.
2025-05-30
ArXiv (preprint)
arxiv.org
arxiv.org
Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments
Luke Rowe
Roger Girgis
Anthony Gosselin
Liam Paull
Chris Pal
Felix Heide
2025-03-28
ArXiv (preprint)
arxiv.org
arxiv.org
Ctrl-V: Higher Fidelity Autonomous Vehicle Video Generation with Bounding-Box Controlled Object Motion
Ge Ya Luo
Zhi Hao Luo
Anthony Gosselin
Alexia Jolicoeur-Martineau
Chris Pal
2025-01-01
Trans. Mach. Learn. Res. (published)
openreview.net
openreview.net
Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments
Luke Rowe
Roger Girgis
Anthony Gosselin
Liam Paull
Chris Pal
Felix Heide
2025-01-01
CVPR (published)
doi.org
arxiv.org
CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning
Luke Rowe
Roger Girgis
Anthony Gosselin
Bruno Carrez
Florian Golemo
Felix Heide
Liam Paull
Chris Pal
Evaluating autonomous vehicle stacks (AVs) in simulation typically involves replaying driving logs from real-world recorded traffic. However… (see more), agents replayed from offline data do not react to the actions of the AV, and their behaviour cannot be easily controlled to simulate counterfactual scenarios. Existing approaches have attempted to address these shortcomings by proposing methods that rely on heuristics or learned generative models of real-world data but these approaches either lack realism or necessitate costly iterative sampling procedures to control the generated behaviours. In this work, we take an alternative approach and propose CtRL-Sim, a method that leverages return-conditioned offline reinforcement learning within a physics-enhanced Nocturne simulator to efficiently generate reactive and controllable traffic agents. Specifically, we process real-world driving data through the Nocturne simulator to generate a diverse offline reinforcement learning dataset, annotated with various reward terms. With this dataset, we train a return-conditioned multi-agent behaviour model that allows for fine-grained manipulation of agent behaviours by modifying the desired returns for the various reward components. This capability enables the generation of a wide range of driving behaviours beyond the scope of the initial dataset, including those representing adversarial behaviours. We demonstrate that CtRL-Sim can efficiently generate diverse and realistic safety-critical scenarios while providing fine-grained control over agent behaviours. Further, we show that fine-tuning our model on simulated safety-critical scenarios generated by our model enhances this controllability.
2024-09-05
robot-learning.org/CoRL/2024/Conference (accepted)
doi.org
openreview.net
Ctrl-V: Higher Fidelity Video Generation with Bounding-Box Controlled Object Motion
Ge Ya Luo
Zhi Hao Luo
Anthony Gosselin
Alexia Jolicoeur-Martineau
Chris Pal
With recent advances in video prediction, controllable video generation has been attracting more attention. Generating high fidelity videos … (see more)according to simple and flexible conditioning is of particular interest. To this end, we propose a controllable video generation model using pixel level renderings of 2D or 3D bounding boxes as conditioning. In addition, we also create a bounding box predictor that, given the initial and ending frames' bounding boxes, can predict up to 15 bounding boxes per frame for all the frames in a 25-frame clip. We perform experiments across 3 well-known AV video datasets: KITTI, Virtual-KITTI 2 and BDD100k.
2024-06-09
ArXiv (preprint)
doi.org
arxiv.org
Ctrl-V: Higher Fidelity Video Generation with Bounding-Box Controlled Object Motion
Ge Ya Luo
Zhi Hao Luo
Anthony Gosselin
Alexia Jolicoeur-Martineau
Chris Pal
Controllable video generation has attracted significant attention, largely due to advances in video diffusion models. In domains such as aut… (see more)onomous driving, it is essential to develop highly accurate predictions for object motions. This paper tackles a crucial challenge of how to exert precise control over object motion for realistic video synthesis. To accomplish this, we 1) control object movements using bounding boxes and extend this control to the renderings of 2D or 3D boxes in pixel space, 2) employ a distinct, specialized model to forecast the trajectories of object bounding boxes based on their previous and, if desired, future positions, and 3) adapt and enhance a separate video diffusion network to create video content based on these high quality trajectory forecasts. Our method, Ctrl-V, leverages modified and fine-tuned Stable Video Diffusion (SVD) models to solve both trajectory and video generation. Extensive experiments conducted on the KITTI, Virtual-KITTI 2, BDD100k, and nuScenes datasets validate the effectiveness of our approach in producing realistic and controllable video generation.
2024-06-09
ArXiv (preprint)
doi.org
arxiv.org
CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning
Luke Rowe
Roger Girgis
Anthony Gosselin
Bruno Carrez
Florian Golemo
Felix Heide
Liam Paull
Chris Pal
Evaluating autonomous vehicle stacks (AVs) in simulation typically involves replaying driving logs from real-world recorded traffic. However… (see more), agents replayed from offline data are not reactive and hard to intuitively control. Existing approaches address these challenges by proposing methods that rely on heuristics or generative models of real-world data but these approaches either lack realism or necessitate costly iterative sampling procedures to control the generated behaviours. In this work, we take an alternative approach and propose CtRL-Sim, a method that leverages return-conditioned offline reinforcement learning (RL) to efficiently generate reactive and controllable traffic agents. Specifically, we process real-world driving data through a physics-enhanced Nocturne simulator to generate a diverse offline RL dataset, annotated with various rewards. With this dataset, we train a return-conditioned multi-agent behaviour model that allows for fine-grained manipulation of agent behaviours by modifying the desired returns for the various reward components. This capability enables the generation of a wide range of driving behaviours beyond the scope of the initial dataset, including adversarial behaviours. We show that CtRL-Sim can generate realistic safety-critical scenarios while providing fine-grained control over agent behaviours.
2024-03-29
ArXiv (preprint)
doi.org
arxiv.org