We introduce PAT3D, the first physics-augmented text-to-3D scene generation framework that integrates vision-language models with physics-based simulation to produce physically plausible, simulation-ready, and intersection-free 3D scenes. Given a text prompt, PAT3D generates 3D objects, infers their spatial relations, and organizes them into a hierarchical scene tree, which is then converted into initial conditions for simulation. A differentiable rigid-body simulator ensures realistic object interactions under gravity, driving the scene toward static equilibrium without interpenetrations. To further enhance scene quality, we introduce a simulation-in-the-loop optimization procedure that guarantees physical stability and non-intersection, while improving semantic consistency with the input prompt. Experiments demonstrate that PAT3D substantially outperforms prior approaches in physical plausibility, semantic consistency, and visual quality. Beyond high-quality generation, PAT3D uniquely enables simulation-ready 3D scenes for downstream tasks such as scene editing and robotic manipulation. Code and data are available at: https://github.com/Simulation-Intelligence/PAT3D.
@article{arxiv.2511.21978,
title = {PAT3D: Physics-Augmented Text-to-3D Scene Generation},
author = {Guying Lin and Kemeng Huang and Michael Liu and Ruihan Gao and Hanke Chen and Lyuhao Chen and Beijia Lu and Taku Komura and Yuan Liu and Jun-Yan Zhu and Minchen Li},
journal= {arXiv preprint arXiv:2511.21978},
year = {2026}
}