SceneX: Procedural Controllable Large-Scale Scene Generation

Developing comprehensive explicit world models is crucial for understanding and simulating real-world scenarios. Recently, Procedural Controllable Generation (PCG) has gained significant attention in large-scale scene generation by enabling the creation of scalable, high-quality assets. However, PCG faces challenges such as limited modular diversity, high expertise requirements, and challenges in managing the diverse elements and structures in complex scenes. In this paper, we introduce a large-scale scene generation framework, SceneX, which can automatically produce high-quality procedural models according to designers' textual descriptions. Specifically, the proposed method comprises two components, PCGHub and PCGPlanner. The former encompasses an extensive collection of accessible procedural assets and thousands of hand-craft API documents to perform as a standard protocol for PCG controller. The latter aims to generate executable actions for Blender to produce controllable and precise 3D assets guided by the user's instructions. Extensive experiments demonstrated the capability of our method in controllable large-scale scene generation, including nature scenes and unbounded cities, as well as scene editing such as asset placement and season translation.

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Publication Details

Journal
Proceedings of the AAAI Conference on Artificial Intelligence
Published
2025-04-11
DOI
https://doi.org/10.1609/aaai.v39i10.33174
Citations
6
Primary Topic
Computer Graphics and Visualization Techniques
Type
article
Field-Weighted Citation Impact
4.78
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article

SceneX: Procedural Controllable Large-Scale Scene Generation

Shi Qing Zhang, Chuanchen Luo, Zhaoxiang Zhang, Mengqi Zhou et al.
6 citations
Proceedings of the AAAI Conference on Artificial Intelligence
Computer Graphics and Visualization Techniques
4.78
article

SceneX: Procedural Controllable Large-Scale Scene Generation

Shi Qing Zhang, Chuanchen Luo, Zhaoxiang Zhang, Mengqi Zhou, Yuxi Wang, Yiwei Li, Jun Hou, Junran Peng
article en
6 citations

Abstract

Developing comprehensive explicit world models is crucial for understanding and simulating real-world scenarios. Recently, Procedural Controllable Generation (PCG) has gained significant attention in large-scale scene generation by enabling the creation of scalable, high-quality assets. However, PCG faces challenges such as limited modular diversity, high expertise requirements, and challenges in managing the diverse elements and structures in complex scenes. In this paper, we introduce a large-scale scene generation framework, SceneX, which can automatically produce high-quality procedural models according to designers' textual descriptions. Specifically, the proposed method comprises two components, PCGHub and PCGPlanner. The former encompasses an extensive collection of accessible procedural assets and thousands of hand-craft API documents to perform as a standard protocol for PCG controller. The latter aims to generate executable actions for Blender to produce controllable and precise 3D assets guided by the user's instructions. Extensive experiments demonstrated the capability of our method in controllable large-scale scene generation, including nature scenes and unbounded cities, as well as scene editing such as asset placement and season translation.

Proceedings of the AAAI Conference on Artificial IntelligenceVol. 39(10)
Shandong University (CN), China University of Geosciences (Beijing) (CN), Institute of Automation (CN), Centre for Artificial Intelligence and Robotics (IN), University of Chinese Academy of Sciences (CN), University of Science and Technology Beijing (CN)
Openalex Percentile: Top 3%
Computer Graphics and Visualization Techniques
4.78
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