Conference Paper/Proceeding/Abstract 610 views 268 downloads
Spatial layout generation via generative adversarial networks
International Conference on AI-Generated Content (AIGC 2024), Volume: 13649, Start page: 6
Swansea University Authors:
YUE YANG, Hanchi Ren, Xianghua Xie
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DOI (Published version): 10.1117/12.3065211
Abstract
The process of architectural floor plan generation is a complex task traditionally performed by architects, requiring a deep understanding of spatial relationships, structural constraints, and aesthetic principles. In recent years, advances in computational design and Artificial Intelligence (AI) ha...
| Published in: | International Conference on AI-Generated Content (AIGC 2024) |
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| ISBN: | 9781510692114 9781510692121 |
| ISSN: | 0277-786X 1996-756X |
| Published: |
SPIE
2025
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa68386 |
| Abstract: |
The process of architectural floor plan generation is a complex task traditionally performed by architects, requiring a deep understanding of spatial relationships, structural constraints, and aesthetic principles. In recent years, advances in computational design and Artificial Intelligence (AI) have enabled the automation of floor plan generation, significantly enhancing efficiency and creativity in the architectural workflow. In this paper, we explored the integration of traditional architectural design methods with advanced technology, focusing on the transformative role of Generative Adversarial Networks (GAN) in floor plan generation. In this work, we created a new dataset containing more than 1200 carefully processed images for the automatic generation of floor plans. These samples come from different platforms and are processed to become algorithm-friendly types. We will make the dataset public available. The algorithm we used is the pix2pix network, which is enhanced with a self-attention mechanism for better spatial understanding and spectral normalization for improved output quality. We demonstrate the versatility of the GAN model in generating complex floor plans for various architectural needs based on our dataset. It also addresses challenges such as model stability, detail refinement, and generating non-standard room shapes, offering insights for future advancements in the field. |
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| Keywords: |
Design; Gallium nitride; Computer aided design; Education and training; Color; Artificial intelligence; Image processing; Solid modeling; Visualization; Mathematical optimization |
| College: |
Faculty of Science and Engineering |
| Start Page: |
6 |