Text-to-Scene with Large Reasoning Models
Frédéric Berdoz, Luca A Lanzendörfer, Nick Tuninga, Roger Wattenhofer
Abstract
Prompt-driven scene synthesis allows users to generate complete 3D environments from textual descriptions. Current text-to-scene methods often struggle with complex geometries and object transformations, and tend to show weak adherence to complex instructions. We address these limitations by introducing Reason-3D, a text-to-scene model powered by large reasoning models (LRMs). Reason-3D integrates object retrieval using captions covering physical, functional, and contextual attributes. Reason-3D then places the selected objects based on implicit and explicit layout constraints, and refines their positions with collision-aware spatial reasoning. Evaluated on instructions ranging from simple to complex indoor configurations, Reason-3D significantly outperforms previous methods in human-rated visual fidelity, adherence to constraints, and asset retrieval quality. Beyond its contribution to the field of text-to-scene generation, our work showcases the advanced spatial reasoning abilities of modern LRMs. Additionally, we release the codebase to further the research in object retrieval and placement with LRMs.
BibTeX
@inproceedings{aaai2026_texttoscenewithl,
title = {Text-to-Scene with Large Reasoning Models},
author = {Frédéric Berdoz and Luca A Lanzendörfer and Nick Tuninga and Roger Wattenhofer},
booktitle = {AAAI 2026},
year = {2026}
}