MajutsuCity: Language-driven Aesthetic-adaptive City Generation with Controllable 3D Assets and Layouts
Generating realistic 3D cities is fundamental to world models, virtual reality, and game development, where an ideal urban scene must satisfy both stylistic diversity, fine-grained, and controllability. However, existing methods struggle to balance the creative flexibility offered by text-based generation with the object-level editability enabled by explicit structural representations. We introduce MajutsuCity, a natural language-driven and aesthetically adaptive framework for synthesizing structurally consistent and stylistically diverse 3D urban scenes. MajutsuCity represents a city as a composition of controllable layouts, assets, and materials, and operates through a four-stage pipeline. To extend controllability beyond initial generation, we further integrate MajutsuAgent, an interactive language-grounded editing agent} that supports five object-level operations. To support photorealistic and customizable scene synthesis, we also construct MajutsuDataset, a high-quality multimodal dataset} containing 2D semantic layouts and height maps, diverse 3D building assets, and curated PBR materials and skyboxes, each accompanied by detailed annotations. Meanwhile, we develop a practical set of evaluation metrics, covering key dimensions such as structural consistency, scene complexity, material fidelity, and lighting atmosphere. Extensive experiments demonstrate MajutsuCity reduces layout FID by 83.7% compared with CityDreamer and by 20.1% over CityCraft. Our method ranks first across all AQS and RDR scores, outperforming existing methods by a clear margin. These results confirm MajutsuCity as a new state-of-the-art in geometric fidelity, stylistic adaptability, and semantic controllability for 3D city generation. We expect our framework can inspire new avenues of research in 3D city generation. Our dataset and code will be released at https://github.com/LongHZ140516/MajutsuCity.
6926bc60243b2216fb75cb8c
Zilong Huang
claimed_verified
2025-11-26T09:26:57.421Z
6487e158f675b4a7867f45fa
https://cdn-avatars.huggingface.co/v1/production/uploads/6487e158f675b4a7867f45fa/J0sls6zZ682o-SH7iQs7B.jpeg
Zilong Huang
SereinH
user
SereinH
6926bc60243b2216fb75cb8d
Jun He
6926bc60243b2216fb75cb8e
Xiaobin Huang
6926bc60243b2216fb75cb8f
Ziyi Xiong
6926bc60243b2216fb75cb90
Yang Luo
6926bc60243b2216fb75cb91
Junyan Ye
6926bc60243b2216fb75cb92
Weijia Li
6926bc60243b2216fb75cb93
Yiping Chen
6926bc60243b2216fb75cb94
Ting Han
MajutsuCity: Language-driven Aesthetic-adaptive City Generation with Controllable 3D Assets and Layouts
Generating realistic 3D cities is fundamental to world models, virtual reality, and game development, where an ideal urban scene must satisfy both stylistic diversity, fine-grained, and controllability. However, existing methods struggle to balance the creative flexibility offered by text-based generation with the object-level editability enabled by explicit structural representations. We introduce MajutsuCity, a natural language-driven and aesthetically adaptive framework for synthesizing structurally consistent and stylistically diverse 3D urban scenes. MajutsuCity represents a city as a composition of controllable layouts, assets, and materials, and operates through a four-stage pipeline. To extend controllability beyond initial generation, we further integrate MajutsuAgent, an interactive language-grounded editing agent} that supports five object-level operations. To support photorealistic and customizable scene synthesis, we also construct MajutsuDataset, a high-quality multimodal dataset} containing 2D semantic layouts and height maps, diverse 3D building assets, and curated PBR materials and skyboxes, each accompanied by detailed annotations. Meanwhile, we develop a practical set of evaluation metrics, covering key dimensions such as structural consistency, scene complexity, material fidelity, and lighting atmosphere. Extensive experiments demonstrate MajutsuCity reduces layout FID by 83.7% compared with CityDreamer and by 20.1% over CityCraft. Our method ranks first across all AQS and RDR scores, outperforming existing methods by a clear margin. These results confirm MajutsuCity as a new state-of-the-art in geometric fidelity, stylistic adaptability, and semantic controllability for 3D city generation. We expect our framework can inspire new avenues of research in 3D city generation. Our dataset and code will be released at https://github.com/LongHZ140516/MajutsuCity.
MajutsuCity is a natural language-driven framework that synthesizes 3D urban scenes with high structural consistency, stylistic diversity, and controllability through a four-stage pipeline and interactive editing agent.
natural language-driven aesthetically adaptive structurally consistent stylistically diverse 3D urban scenes controllable layouts assets materials interactive language-grounded editing agent semantic layouts height maps 3D building assets PBR materials skyboxes detailed annotations evaluation metrics structural consistency scene complexity material fidelity lighting atmosphere CityDreamer CityCraft AQS RDR scores geometric fidelity stylistic adaptability semantic controllability