PAT3D: Physics-Augmented Text-to-3D Scene Generation

Guying Lin, Kemeng Huang, Michael Liu, Ruihan Gao, Hanke Chen +6 more
11/26/2025
cs.CV

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Abstract

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 will be released upon acceptance.

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Cite this paper

@article{lin2025patd,
  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},
  year   = {2025},
  eprint = {2511.21978},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2511.21978v1}
}

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