TY - GEN
T1 - UnrealLLM
T2 - 63rd Annual Meeting of the Association for Computational Linguistics, ACL 2025
AU - Tang, Song
AU - Zhao, Kaiyong
AU - Wang, Lei
AU - Li, Yuliang
AU - Liu, Xuebo
AU - Zou, Junyi
AU - Wang, Qiang
AU - Chu, Xiaowen
N1 - Publisher Copyright:
© 2025 Association for Computational Linguistics.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105028599621
U2 - 10.18653/v1/2025.findings-acl.994
DO - 10.18653/v1/2025.findings-acl.994
M3 - 会议稿件
AN - SCOPUS:105028599621
T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics
SP - 19417
EP - 19435
BT - Findings of the Association for Computational Linguistics
A2 - Che, Wanxiang
A2 - Nabende, Joyce
A2 - Shutova, Ekaterina
A2 - Pilehvar, Mohammad Taher
PB - Association for Computational Linguistics (ACL)
Y2 - 27 July 2025 through 1 August 2025
ER -