SceneX:Procedural Controllable Large-scale Scene Generation via Large-language Models
<|reference_start|>SceneX:Procedural Controllable Large-scale Scene Generation via Large-language Models: Due to its great application potential, large-scale scene generation has drawn extensive attention in academia and industry. Recent research employs powerful generative models to create desired scenes and achieves promising results. However, most of these methods represent the scene using 3D primitives (e.g. point cloud or radiance field) incompatible with the industrial pipeline, which leads to a substantial gap between academic research and industrial deployment. Procedural Controllable Generation (PCG) is an efficient technique for creating scalable and high-quality assets, but it is unfriendly for ordinary users as it demands profound domain expertise. To address these issues, we resort to using the large language model (LLM) to drive the procedural modeling. 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, PCGBench and PCGPlanner. The former encompasses an extensive collection of accessible procedural assets and thousands of hand-craft API documents. The latter aims to generate executable actions for Blender to produce controllable and precise 3D assets guided by the user's instructions. Our SceneX can generate a city spanning 2.5 km times 2.5 km with delicate layout and geometric structures, drastically reducing the time cost from several weeks for professional PCG engineers to just a few hours for an ordinary user. Extensive experiments demonstrated the capability of our method in controllable large-scale scene generation and editing, including asset placement and season translation.<|reference_end|>
@article{zhou2024scenex:procedural,
    title={SceneX:Procedural Controllable Large-scale Scene Generation via
  Large-language Models},
    author={Mengqi Zhou and Yuxi Wang and Jun Hou and Chuanchen Luo and Zhaoxiang
  Zhang and Junran Peng},
    journal={arXiv preprint arXiv:2403.15698},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.15698},
    primaryClass={cs.CV cs.AI}
}