10 papers
Inference-time Physics Alignment of Video Generative Models with Latent World Models
Jianhao Yuan, Xiaofeng Zhang, Felix Friedrich +7
State-of-the-art video generative models produce promising visual content yet often violate basic physics principles, limiting their utility. While some attribute this deficiency t…
Improving the Physics of Video Generation with VJEPA-2 Reward Signal
Jianhao Yuan, Xiaofeng Zhang, Felix Friedrich +7
This is a short technical report describing the winning entry of the PhysicsIQ Challenge, presented at the Perception Test Workshop at ICCV 2025. State-of-the-art video generative…
Increasing the Utility of Synthetic Images through Chamfer Guidance
Nicola Dall'Asen, Xiaofeng Zhang, Reyhane Askari Hemmat +4
Conditional image generative models hold considerable promise to produce infinite amounts of synthetic training data. Yet, recent progress in generation quality has come at the exp…
DIMCIM: A Quantitative Evaluation Framework for Default-mode Diversity and Generalization in Text-to-Image Generative Models
Revant Teotia, Candace Ross, Karen Ullrich +4
Recent advances in text-to-image (T2I) models have achieved impressive quality and consistency. However, this has come at the cost of representation diversity. While automatic eval…
Multi-Modal Language Models as Text-to-Image Model Evaluators
Jiahui Chen, Candace Ross, Reyhane Askari-Hemmat +4
The steady improvements of text-to-image (T2I) generative models lead to slow deprecation of automatic evaluation benchmarks that rely on static datasets, motivating researchers to…
Improving the Scaling Laws of Synthetic Data with Deliberate Practice
Reyhane Askari-Hemmat, Mohammad Pezeshki, Elvis Dohmatob +6
Inspired by the principle of deliberate practice in human learning, we propose Deliberate Practice for Synthetic Data Generation (DP), a novel framework that improves sample effici…