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Luis Lara

Alumni
Website
Google Scholar

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
Learning Action and Reasoning-Centric Image Editing from Videos and Simulation
Benno Krojer
Dheeraj Vattikonda
Luis Lara
Varun Jampani
Eva Portelance
Chris Pal
Siva Reddy
2024-09-26
NeurIPS.cc/2024/Datasets_and_Benchmarks_Track (spotlight)
openreview.net
openreview.net
Learning Action and Reasoning-Centric Image Editing from Videos and Simulations
Benno Krojer
Dheeraj Vattikonda
Luis Lara
Varun Jampani
Eva Portelance
Chris Pal
Siva Reddy
An image editing model should be able to perform diverse edits, ranging from object replacement, changing attributes or style, to performing… (see more) actions or movement, which require many forms of reasoning. Current general instruction-guided editing models have significant shortcomings with action and reasoning-centric edits. Object, attribute or stylistic changes can be learned from visually static datasets. On the other hand, high-quality data for action and reasoning-centric edits is scarce and has to come from entirely different sources that cover e.g. physical dynamics, temporality and spatial reasoning. To this end, we meticulously curate the AURORA Dataset (Action-Reasoning-Object-Attribute), a collection of high-quality training data, human-annotated and curated from videos and simulation engines. We focus on a key aspect of quality training data: triplets (source image, prompt, target image) contain a single meaningful visual change described by the prompt, i.e., truly minimal changes between source and target images. To demonstrate the value of our dataset, we evaluate an AURORA-finetuned model on a new expert-curated benchmark (AURORA-Bench) covering 8 diverse editing tasks. Our model significantly outperforms previous editing models as judged by human raters. For automatic evaluations, we find important flaws in previous metrics and caution their use for semantically hard editing tasks. Instead, we propose a new automatic metric that focuses on discriminative understanding. We hope that our efforts : (1) curating a quality training dataset and an evaluation benchmark, (2) developing critical evaluations, and (3) releasing a state-of-the-art model, will fuel further progress on general image editing.
2024-07-03
ArXiv (preprint)
doi.org
arxiv.org
Learning Action and Reasoning-Centric Image Editing from Videos and Simulations
Benno Krojer
Dheeraj Vattikonda
Luis Lara
Varun Jampani
Eva Portelance
Chris Pal
Siva Reddy
An image editing model should be able to perform diverse edits, ranging from object replacement, changing attributes or style, to performing… (see more) actions or movement, which require many forms of reasoning. Current general instruction-guided editing models have significant shortcomings with action and reasoning-centric edits. Object, attribute or stylistic changes can be learned from visually static datasets. On the other hand, high-quality data for action and reasoning-centric edits is scarce and has to come from entirely different sources that cover e.g. physical dynamics, temporality and spatial reasoning. To this end, we meticulously curate the AURORA Dataset (Action-Reasoning-Object-Attribute), a collection of high-quality training data, human-annotated and curated from videos and simulation engines. We focus on a key aspect of quality training data: triplets (source image, prompt, target image) contain a single meaningful visual change described by the prompt, i.e., truly minimal changes between source and target images. To demonstrate the value of our dataset, we evaluate an AURORA-finetuned model on a new expert-curated benchmark (AURORA-Bench) covering 8 diverse editing tasks. Our model significantly outperforms previous editing models as judged by human raters. For automatic evaluations, we find important flaws in previous metrics and caution their use for semantically hard editing tasks. Instead, we propose a new automatic metric that focuses on discriminative understanding. We hope that our efforts : (1) curating a quality training dataset and an evaluation benchmark, (2) developing critical evaluations, and (3) releasing a state-of-the-art model, will fuel further progress on general image editing.
2024-07-03
ArXiv (preprint)
doi.org
arxiv.org