SceneX:Procedural Controllable Large-scale Scene Generation via
  Large-language Models
Mengqi Zhou Jun Hou Chuanchen Luo Yuxi Wang Zhaoxiang Zhang Junran Peng
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.