| id |
caadria2024_58 |
| authors |
Zhuang, Junling, Li, Guanhong, Xu, Hang, Xu, Jintu and Tian, Runjia |
| year |
2024 |
| title |
Text-to-City: Controllable 3D Urban Block Generation With Latent Diffusion Model |
| doi |
https://doi.org/10.52842/conf.caadria.2024.2.169
|
| source |
Nicole Gardner, Christiane M. Herr, Likai Wang, Hirano Toshiki, Sumbul Ahmad Khan (eds.), ACCELERATED DESIGN - Proceedings of the 29th CAADRIA Conference, Singapore, 20-26 April 2024, Volume 2, pp. 169–178 |
| summary |
The rise of deep learning has introduced novel computational tools for urban block design. Many researchers have explored generative urban block design using either rule-based or deep learning methods. However, these methods often fall short in adequately capturing morphological features and essential design indicators like building density. Latent diffusion models, particularly in the context of urban design, offer a groundbreaking solution. These models can generate cityscapes directly from text descriptions, incorporating a wide array of design indicators. This paper introduces a novel workflow that utilizes Stable Diffusion, a state-of-the-art latent diffusion model, to generate 3D urban environments. The process involves reconstructing 3D urban block models from generated depth images, employing a systematic depth-to-height mapping technique. Additionally, the paper explores the extrapolation between various urban morphological characteristics, aiming to generate novel urban forms that transcend existing city models. This innovative approach not only facilitates the accurate generation of urban blocks with specific morphological characteristics and design metrics, such as building density, but also demonstrates its versatility through application to three distinct cities. This methodology, tested on select cities, holds potential for broader range of urban environments and more design indicators, setting the stage for future computational urban design research. |
| keywords |
deep learning, generative design, latent diffusion model, urban block morphology, artificial intelligence |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (4,427,028 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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