Generative AI models for different steps in architectural design: A literature review
Recent advances in generative artificial intelligence (AI) technologies have been significantly driven by models such as generative adversarial networks (GANs), variational autoencoders (VAEs), and denoising diffusion probabilistic models (DDPMs). Although architects recognize the potential of gener...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-06-01
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| Series: | Frontiers of Architectural Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S209526352400147X |
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| _version_ | 1849344593861017600 |
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| author | Chengyuan Li Tianyu Zhang Xusheng Du Ye Zhang Haoran Xie |
| author_facet | Chengyuan Li Tianyu Zhang Xusheng Du Ye Zhang Haoran Xie |
| author_sort | Chengyuan Li |
| collection | DOAJ |
| description | Recent advances in generative artificial intelligence (AI) technologies have been significantly driven by models such as generative adversarial networks (GANs), variational autoencoders (VAEs), and denoising diffusion probabilistic models (DDPMs). Although architects recognize the potential of generative AI in design, personal barriers often restrict their access to the latest technological developments, thereby causing the application of generative AI in architectural design to lag behind. Therefore, it is essential to comprehend the principles and advancements of generative AI models and analyze their relevance in architecture applications. This paper first provides an overview of generative AI technologies, with a focus on probabilistic diffusion models (DDPMs), 3D generative models, and foundation models, highlighting their recent developments and main application scenarios. Then, the paper explains how the abovementioned models could be utilized in architecture. We subdivide the architectural design process into six steps and review related research projects in each step from 2020 to the present. Lastly, this paper discusses potential future directions for applying generative AI in the architectural design steps. This research can help architects quickly understand the development and latest progress of generative AI and contribute to the further development of intelligent architecture. |
| format | Article |
| id | doaj-art-8d65f07fe7d14f878c9d01e8a3be8416 |
| institution | Kabale University |
| issn | 2095-2635 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Frontiers of Architectural Research |
| spelling | doaj-art-8d65f07fe7d14f878c9d01e8a3be84162025-08-20T03:42:38ZengKeAi Communications Co., Ltd.Frontiers of Architectural Research2095-26352025-06-0114375978310.1016/j.foar.2024.10.001Generative AI models for different steps in architectural design: A literature reviewChengyuan Li0Tianyu Zhang1Xusheng Du2Ye Zhang3Haoran Xie4School of Architecture, Tianjin University, Tianjin 300000, ChinaGraduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Ishikawa 923-1292, JapanGraduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Ishikawa 923-1292, JapanSchool of Architecture, Tianjin University, Tianjin 300000, China; Corresponding author.Graduate School of Advanced Science and Technology, Japan Advanced Institute of Science and Technology, Ishikawa 923-1292, JapanRecent advances in generative artificial intelligence (AI) technologies have been significantly driven by models such as generative adversarial networks (GANs), variational autoencoders (VAEs), and denoising diffusion probabilistic models (DDPMs). Although architects recognize the potential of generative AI in design, personal barriers often restrict their access to the latest technological developments, thereby causing the application of generative AI in architectural design to lag behind. Therefore, it is essential to comprehend the principles and advancements of generative AI models and analyze their relevance in architecture applications. This paper first provides an overview of generative AI technologies, with a focus on probabilistic diffusion models (DDPMs), 3D generative models, and foundation models, highlighting their recent developments and main application scenarios. Then, the paper explains how the abovementioned models could be utilized in architecture. We subdivide the architectural design process into six steps and review related research projects in each step from 2020 to the present. Lastly, this paper discusses potential future directions for applying generative AI in the architectural design steps. This research can help architects quickly understand the development and latest progress of generative AI and contribute to the further development of intelligent architecture.http://www.sciencedirect.com/science/article/pii/S209526352400147XGenerative AIArchitectural designDiffusion models3D generative modelsLarge-scale models |
| spellingShingle | Chengyuan Li Tianyu Zhang Xusheng Du Ye Zhang Haoran Xie Generative AI models for different steps in architectural design: A literature review Frontiers of Architectural Research Generative AI Architectural design Diffusion models 3D generative models Large-scale models |
| title | Generative AI models for different steps in architectural design: A literature review |
| title_full | Generative AI models for different steps in architectural design: A literature review |
| title_fullStr | Generative AI models for different steps in architectural design: A literature review |
| title_full_unstemmed | Generative AI models for different steps in architectural design: A literature review |
| title_short | Generative AI models for different steps in architectural design: A literature review |
| title_sort | generative ai models for different steps in architectural design a literature review |
| topic | Generative AI Architectural design Diffusion models 3D generative models Large-scale models |
| url | http://www.sciencedirect.com/science/article/pii/S209526352400147X |
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