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Conference Paper/Proceeding/Abstract 609 views 268 downloads

Spatial layout generation via generative adversarial networks

YUE YANG, Yueyue Hu, Hanchi Ren, Yunying Wang, Jingjing Deng, Xianghua Xie Orcid Logo

International Conference on AI-Generated Content (AIGC 2024), Volume: 13649, Start page: 6

Swansea University Authors: YUE YANG, Hanchi Ren, Xianghua Xie Orcid Logo

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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...

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Published in: International Conference on AI-Generated Content (AIGC 2024)
ISBN: 9781510692114 9781510692121
ISSN: 0277-786X 1996-756X
Published: SPIE 2025
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa68386
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spelling 2025-07-24T12:59:16.5491226 v2 68386 2024-11-29 Spatial layout generation via generative adversarial networks 713d5679a24c929264c9e0581fddeff6 YUE YANG YUE YANG true false 9e043b899a2b786672a28ed4f864ffcc Hanchi Ren Hanchi Ren true false b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2024-11-29 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. Conference Paper/Proceeding/Abstract International Conference on AI-Generated Content (AIGC 2024) 13649 6 SPIE 9781510692114 9781510692121 0277-786X 1996-756X Design; Gallium nitride; Computer aided design; Education and training; Color; Artificial intelligence; Image processing; Solid modeling; Visualization; Mathematical optimization 7 7 2025 2025-07-07 10.1117/12.3065211 COLLEGE NANME COLLEGE CODE Swansea University Not Required 2025-07-24T12:59:16.5491226 2024-11-29T11:19:40.5794459 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science YUE YANG 1 Yueyue Hu 2 Hanchi Ren 3 Yunying Wang 4 Jingjing Deng 5 Xianghua Xie 0000-0002-2701-8660 6 68386__33008__ef91e643fb4842d280ddd002ced7550e.pdf Spatial_Layout_Generation_via_Generative_Adversarial_Networks.pdf 2024-12-02T09:06:54.5512683 Output 2965899 application/pdf Accepted Manuscript true Copyright 2025. Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited. true eng https://creativecommons.org/licenses/by/4.0/deed.en
title Spatial layout generation via generative adversarial networks
spellingShingle Spatial layout generation via generative adversarial networks
YUE YANG
Hanchi Ren
Xianghua Xie
title_short Spatial layout generation via generative adversarial networks
title_full Spatial layout generation via generative adversarial networks
title_fullStr Spatial layout generation via generative adversarial networks
title_full_unstemmed Spatial layout generation via generative adversarial networks
title_sort Spatial layout generation via generative adversarial networks
author_id_str_mv 713d5679a24c929264c9e0581fddeff6
9e043b899a2b786672a28ed4f864ffcc
b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv 713d5679a24c929264c9e0581fddeff6_***_YUE YANG
9e043b899a2b786672a28ed4f864ffcc_***_Hanchi Ren
b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
author YUE YANG
Hanchi Ren
Xianghua Xie
author2 YUE YANG
Yueyue Hu
Hanchi Ren
Yunying Wang
Jingjing Deng
Xianghua Xie
format Conference Paper/Proceeding/Abstract
container_title International Conference on AI-Generated Content (AIGC 2024)
container_volume 13649
container_start_page 6
publishDate 2025
institution Swansea University
isbn 9781510692114
9781510692121
issn 0277-786X
1996-756X
doi_str_mv 10.1117/12.3065211
publisher SPIE
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
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description 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.
published_date 2025-07-07T05:21:10Z
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score 11.090009

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