CityGen: Infinite and Controllable City Layout Generation
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866912319788810240 |
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| author | Deng, Jie Chai, Wenhao Guo, Jianshu Huang, Qixuan Huang, Junsheng Hu, Wenhao Hao, Shengyu Hwang, Jenq-Neng Wang, Gaoang |
| author_facet | Deng, Jie Chai, Wenhao Guo, Jianshu Huang, Qixuan Huang, Junsheng Hu, Wenhao Hao, Shengyu Hwang, Jenq-Neng Wang, Gaoang |
| contents | The recent surge in interest in city layout generation underscores its significance in urban planning and smart city development. The task involves procedurally or automatically generating spatial arrangements for urban elements such as roads, buildings, water, and vegetation. Previous methods, whether procedural modeling or deep learning-based approaches like VAEs and GANs, rely on complex priors, expert guidance, or initial layouts, and often lack diversity and interactivity. In this paper, we present CityGen, an end-to-end framework for infinite, diverse, and controllable city layout generation. Our framework introduces an infinite expansion module to extend local layouts to city-scale layouts and a multi-scale refinement module to upsample and refine them. We also designed a user-friendly control scheme, allowing users to guide generation through simple sketching. Additionally, we convert the 2D layout to 3D by synthesizing a height field, facilitating downstream applications. Extensive experiments demonstrate CityGen's state-of-the-art performance across various metrics, making it suitable for a wide range of downstream applications. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2312_01508 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | CityGen: Infinite and Controllable City Layout Generation Deng, Jie Chai, Wenhao Guo, Jianshu Huang, Qixuan Huang, Junsheng Hu, Wenhao Hao, Shengyu Hwang, Jenq-Neng Wang, Gaoang Computer Vision and Pattern Recognition The recent surge in interest in city layout generation underscores its significance in urban planning and smart city development. The task involves procedurally or automatically generating spatial arrangements for urban elements such as roads, buildings, water, and vegetation. Previous methods, whether procedural modeling or deep learning-based approaches like VAEs and GANs, rely on complex priors, expert guidance, or initial layouts, and often lack diversity and interactivity. In this paper, we present CityGen, an end-to-end framework for infinite, diverse, and controllable city layout generation. Our framework introduces an infinite expansion module to extend local layouts to city-scale layouts and a multi-scale refinement module to upsample and refine them. We also designed a user-friendly control scheme, allowing users to guide generation through simple sketching. Additionally, we convert the 2D layout to 3D by synthesizing a height field, facilitating downstream applications. Extensive experiments demonstrate CityGen's state-of-the-art performance across various metrics, making it suitable for a wide range of downstream applications. |
| title | CityGen: Infinite and Controllable City Layout Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.01508 |