  Generating a realistic, large-scale 3D virtual city remains a complex
challenge due to the involvement of numerous 3D assets, various city styles,
and strict layout constraints. Existing approaches provide promising attempts
at procedural content generation to create large-scale scenes using Blender
agents. However, they face crucial issues such as difficulties in scaling up
generation capability and achieving fine-grained control at the semantic layout
level. To address these problems, we propose a novel multi-modal controllable
procedural content generation method, named CityX, which enhances realistic,
unbounded 3D city generation guided by multiple layout conditions, including
OSM, semantic maps, and satellite images. Specifically, the proposed method
contains a general protocol for integrating various PCG plugins and a
multi-agent framework for transforming instructions into executable Blender
actions. Through this effective framework, CityX shows the potential to build
an innovative ecosystem for 3D scene generation by bridging the gap between the
quality of generated assets and industrial requirements. Extensive experiments
have demonstrated the effectiveness of our method in creating high-quality,
diverse, and unbounded cities guided by multi-modal conditions. Our project
page: https://cityx-lab.github.io.
