Conference Paper/Proceeding/Abstract 167 views 4 downloads
Spatial Layout Generation via Generative Adversarial Networks
International Conference on AI-generated Content
Swansea University Authors:
YUE YANG, Hans Ren, Xianghua Xie
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PDF | Accepted Manuscript
Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy (rights retention).
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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 |
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Published: |
2024
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URI: | https://cronfa.swan.ac.uk/Record/cronfa68386 |
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2025-02-18T05:39:05Z |
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2025-02-17T14:06:29.8492966 v2 68386 2024-11-29 Spatial Layout Generation via Generative Adversarial Networks 713d5679a24c929264c9e0581fddeff6 YUE YANG YUE YANG true false 9e043b899a2b786672a28ed4f864ffcc Hans Ren Hans 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 23 12 2024 2024-12-23 COLLEGE NANME COLLEGE CODE Swansea University Not Required 2025-02-17T14:06:29.8492966 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 Hans Ren 3 Yunying Wang 4 Jingjing Deng 5 Xianghua Xie 0000-0002-2701-8660 6 68386__33008__dc619f2732fa40de9ecdb54ff9b69ea0.pdf Spatial_Layout_Generation_via_Generative_Adversarial_Networks.pdf 2024-12-02T09:06:54.5512683 Output 2965899 application/pdf Accepted Manuscript true Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy (rights retention). 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 Hans 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_***_Hans Ren b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie |
author |
YUE YANG Hans Ren Xianghua Xie |
author2 |
YUE YANG Yueyue Hu Hans Ren Yunying Wang Jingjing Deng Xianghua Xie |
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Conference Paper/Proceeding/Abstract |
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International Conference on AI-generated Content |
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2024 |
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Swansea University |
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Faculty of Science and Engineering |
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Faculty of Science and Engineering |
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School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science |
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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 |
2024-12-23T14:36:57Z |
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1824587479898914816 |
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11.051735 |