Reyhane Askari Hemmat

Xiaochuang Han

Nicolas Ballas

State-of-the-art video generative models produce promising visual content yet often violate basic physics principles, limiting their utility… (voir plus). While some attribute this deficiency to insufficient physics understanding from pre-training, we find that the shortfall in physics plausibility also stems from suboptimal inference strategies. We therefore introduce WMReward and treat improving physics plausibility of video generation as an inference-time alignment problem. In particular, we leverage the strong physics prior of a latent world model (here, VJEPA-2) as a reward to search and steer multiple candidate denoising trajectories, enabling scaling test-time compute for better generation performance. Empirically, our approach substantially improves physics plausibility across image-conditioned, multiframe-conditioned, and text-conditioned generation settings, with validation from human preference study. Notably, in the ICCV 2025 Perception Test PhysicsIQ Challenge, we achieve a final score of 62.64%, winning first place and outperforming the previous state of the art by 7.42%. Our work demonstrates the viability of using latent world models to improve physics plausibility of video generation, beyond this specific instantiation or parameterization.

2026-01-14

ArXiv (prépublication)

Why Less is More (Sometimes): A Theory of Data Curation

2025-10-31

arXiv (publié)

Improving the Scaling Laws of Synthetic Data with Deliberate Practice

Pietro Astolfi

Melissa Hall

Jakob Verbeek

Inspired by the principle of deliberate practice in human learning, we propose Deliberate Practice for Synthetic Data Generation (DP), a nov… (voir plus)el framework that improves sample efficiency through dynamic synthetic data generation. Prior work has shown that scaling synthetic data is inherently challenging, as naively adding new data leads to diminishing returns. To address this, pruning has been identified as a key mechanism for improving scaling, enabling models to focus on the most informative synthetic samples. Rather than generating a large dataset and pruning it afterward, DP efficiently approximates the direct generation of informative samples. We theoretically show how training on challenging, informative examples improves scaling laws and empirically validate that DP achieves better scaling performance with significantly fewer training samples and iterations. On ImageNet-100, DP generates 3.4x fewer samples and requires six times fewer iterations, while on ImageNet-1k, it generates 8x fewer samples with a 30% reduction in iterations, all while achieving superior performance compared to prior work.

2025-10-06

Proceedings of the 42nd International Conference on Machine Learning (publié)

proceedings.mlr.press

Increasing the Utility of Synthetic Images through Chamfer Guidance

Nicola Dall'Asen

Xiaofeng Zhang

Melissa Hall

Jakob Verbeek

Conditional image generative models hold considerable promise to produce infinite amounts of synthetic training data. Yet, recent progress i… (voir plus)n generation quality has come at the expense of generation diversity, limiting the utility of these models as a source of synthetic training data. Although guidance-based approaches have been introduced to improve the utility of generated data by focusing on quality or diversity, the (implicit or explicit) utility functions oftentimes disregard the potential distribution shift between synthetic and real data. In this work, we introduce Chamfer Guidance: a training-free guidance approach which leverages a handful of real exemplar images to characterize the quality and diversity of synthetic data. We show that by leveraging the proposed Chamfer Guidance, we can boost the diversity of the generations w.r.t. a dataset of real images while maintaining or improving the generation quality on ImageNet-1k and standard geo-diversity benchmarks. Our approach achieves state-of-the-art few-shot performance with as little as 2 exemplar real images, obtaining 96.4\% in terms of precision, and 86.4\% in terms of distributional coverage, which increase to 97.5\% and 92.7\%, respectively, when using 32 real images. We showcase the benefits of the Chamfer Guidance generation by training downstream image classifiers on synthetic data, achieving accuracy boost of up to 15\% for in-distribution over the baselines, and up to 16\% in out-of-distribution. Furthermore, our approach does not require using the unconditional model, and thus obtains a 31\% reduction in FLOPs w.r.t. classifier-free-guidance-based approaches at sampling time.

2025-08-14

ArXiv (prépublication)

Improving the Scaling Laws of Synthetic Data with Deliberate Practice

Pietro Astolfi

Melissa Hall

Jakob Verbeek

2025-05-01

ICML.cc/2025/Conference (présentation orale)

openreview.net

Multi-Modal Language Models as Text-to-Image Model Evaluators

Jiahui Chen

Candace Ross

Koustuv Sinha

Melissa Hall

2025-05-01

ArXiv (prépublication)

Multi-Modal Language Models as Text-to-Image Model Evaluators

Jiahui Chen

Candace Ross

Koustuv Sinha

Melissa Hall

2025-05-01

arXiv (publié)

Improving the Scaling Laws of Synthetic Data with Deliberate Practice

Pietro Astolfi

Melissa Hall

Jakob Verbeek

Inspired by the principle of deliberate practice in human learning, we propose Deliberate Practice for Synthetic Data Generation (DP), a nov… (voir plus)el framework that improves sample efficiency through dynamic synthetic data generation. Prior work has shown that scaling synthetic data is inherently challenging, as naively adding new data leads to diminishing returns. To address this, pruning has been identified as a key mechanism for improving scaling, enabling models to focus on the most informative synthetic samples. Rather than generating a large dataset and pruning it afterward, DP efficiently approximates the direct generation of informative samples. We theoretically show how training on challenging, informative examples improves scaling laws and empirically validate that DP achieves better scaling performance with significantly fewer training samples and iterations. On ImageNet-100, DP generates 3.4x fewer samples and requires six times fewer iterations, while on ImageNet-1k, it generates 8x fewer samples with a 30 percent reduction in iterations, all while achieving superior performance compared to prior work.

2025-02-21

ArXiv (prépublication)

EvalGIM: A Library for Evaluating Generative Image Models

Melissa Hall

Oscar Mañas

Mark Ibrahim

Candace Ross

Pietro Astolfi

Tariq Berrada

Marton Havasi

Yohann Benchetrit

Karen Ullrich

Carolina Braga

Abhishek Charnalia

Maeve Ryan

Michael Rabbat

Jakob Verbeek

As the use of text-to-image generative models increases, so does the adoption of automatic benchmarking methods used in their evaluation. Ho… (voir plus)wever, while metrics and datasets abound, there are few unified benchmarking libraries that provide a framework for performing evaluations across many datasets and metrics. Furthermore, the rapid introduction of increasingly robust benchmarking methods requires that evaluation libraries remain flexible to new datasets and metrics. Finally, there remains a gap in synthesizing evaluations in order to deliver actionable takeaways about model performance. To enable unified, flexible, and actionable evaluations, we introduce EvalGIM (pronounced ''EvalGym''), a library for evaluating generative image models. EvalGIM contains broad support for datasets and metrics used to measure quality, diversity, and consistency of text-to-image generative models. In addition, EvalGIM is designed with flexibility for user customization as a top priority and contains a structure that allows plug-and-play additions of new datasets and metrics. To enable actionable evaluation insights, we introduce ''Evaluation Exercises'' that highlight takeaways for specific evaluation questions. The Evaluation Exercises contain easy-to-use and reproducible implementations of two state-of-the-art evaluation methods of text-to-image generative models: consistency-diversity-realism Pareto Fronts and disaggregated measurements of performance disparities across groups. EvalGIM also contains Evaluation Exercises that introduce two new analysis methods for text-to-image generative models: robustness analyses of model rankings and balanced evaluations across different prompt styles. We encourage text-to-image model exploration with EvalGIM and invite contributions at https://github.com/facebookresearch/EvalGIM/.

2024-12-13

ArXiv (prépublication)

Improving Geo-diversity of Generated Images with Contextualized Vendi Score Guidance

Melissa Hall

Alicia Sun

Candace Ross

2024-11-21

Lecture Notes in Computer Science (publié)

On Improved Conditioning Mechanisms and Pre-training Strategies for Diffusion Models

Tariq Berrada

Pietro Astolfi

Melissa Hall

Yohann Benchetrit

Marton Havasi

Matthew J. Muckley

Karteek Alahari

Jakob Verbeek

Large-scale training of latent diffusion models (LDMs) has enabled unprecedented quality in image generation. However, the key components of… (voir plus) the best performing LDM training recipes are oftentimes not available to the research community, preventing apple-to-apple comparisons and hindering the validation of progress in the field. In this work, we perform an in-depth study of LDM training recipes focusing on the performance of models and their training efficiency. To ensure apple-to-apple comparisons, we re-implement five previously published models with their corresponding recipes. Through our study, we explore the effects of (i)~the mechanisms used to condition the generative model on semantic information (e.g., text prompt) and control metadata (e.g., crop size, random flip flag, etc.) on the model performance, and (ii)~the transfer of the representations learned on smaller and lower-resolution datasets to larger ones on the training efficiency and model performance. We then propose a novel conditioning mechanism that disentangles semantic and control metadata conditionings and sets a new state-of-the-art in class-conditional generation on the ImageNet-1k dataset -- with FID improvements of 7% on 256 and 8% on 512 resolutions -- as well as text-to-image generation on the CC12M dataset -- with FID improvements of 8% on 256 and 23% on 512 resolution.

2024-11-05

ArXiv (prépublication)

Deliberate Practice with Synthetic Data

Pietro Astolfi

Melissa Hall

Jakob Verbeek