UnrealLLM: Towards Highly Controllable and Interactable 3D Scene Generation by LLM-powered Procedural Content Generation
Song Tang, Kaiyong Zhao, Lei Wang, Yuliang Li, Xuebo Liu, Junyi Zou, Qiang Wang, Xiaowen Chu
Abstract
The creation of high-quality 3D scenes is essential for applications like video games and simulations, yet automating this process while retaining the benefits of Procedural Content Generation (PCG) remains challenging. In this paper, we introduce UnrealLLM, a novel multi-agent framework that connects natural language descriptions with the professional PCG system (Unreal Engine 5) to automate scene generation. UnrealLLM constructs a comprehensive knowledge base to translate text into executable PCG blueprints and a diverse asset library that guarantees high-quality scene generation. Additionally, it also introduces a text-based blueprint system with a spline-based control mechanism for geometric arrangement, enabling natural language interaction and enhancing interactivity in 3D environments using UE5’s advanced capabilities. Through extensive experiments, we show that UnrealLLM achieves competitive performance in technical metrics and aesthetic quality, offering unique advantages in generation scale and interactivity. This work makes a valuable contribution to automated 3D content creation, benefiting both novice users and professional designers.
BibTeX
@inproceedings{songtang-etal-2025-unrealllm,
title = "{U}nreal{LLM}: Towards Highly Controllable and Interactable 3{D} Scene Generation by {LLM}-powered Procedural Content Generation",
author = "Tang, Song and
Zhao, Kaiyong and
Wang, Lei and
Li, Yuliang and
Liu, Xuebo and
Zou, Junyi and
Wang, Qiang and
Chu, Xiaowen",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.994/",
doi = "10.18653/v1/2025.findings-acl.994",
pages = "19417--19435",
ISBN = "979-8-89176-256-5"
}