Chapter Generative Design Intuition from the Fine-Tuned Models of Named Architects’ Style
This paper suggests the potential application of generative artificial intelligence-based image generation technology in the field of architecture, for early phase shape planning, using the styles of renowned architects. The study employed the following approaches: 1) Intensive image generation base...
I tiakina i:
| Ngā kaituhi matua: | , , , , |
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| Hōputu: | Online |
| Reo: | Ingarihi |
| I whakaputaina: |
Firenze University Press
2024
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| Ngā marau: | |
| Urunga tuihono: | ONIX_20240402_9791221502893_10 |
| Ngā Tūtohu: |
Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!
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| _version_ | 1869522668763480064 |
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| author | Jeong, Hyun Yoo, Youngjin Kim, Youngchae Cha, SeungHyun Lee, Jin-Kook |
| author_browse | Cha, SeungHyun Jeong, Hyun Kim, Youngchae Lee, Jin-Kook Yoo, Youngjin |
| author_facet | Jeong, Hyun Yoo, Youngjin Kim, Youngchae Cha, SeungHyun Lee, Jin-Kook |
| author_sort | Jeong, Hyun |
| collection | Directory of Open Access Books |
| description | This paper suggests the potential application of generative artificial intelligence-based image generation technology in the field of architecture, for early phase shape planning, using the styles of renowned architects. The study employed the following approaches: 1) Intensive image generation based on the styles of 20 architects to test the AI's recognition ability and image quality. 2) Additional training was conducted for architects with low recognition rates to construct an enhanced learning model in the quality of image generation. 3) In addition to generating architectural visualization images using existing architects' design styles, alternative styles were proposed through design combinations, aiming to concretize ambiguous idea communication in the early stages of design and enhance its efficiency. The study sheds light on the future prospects of applying this generative AI model in the field of architecture |
| format | Online |
| id | doab-20.500.12854ir-137022 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Firenze University Press |
| publisherStr | Firenze University Press |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1370222024-05-11T09:48:14Z Chapter Generative Design Intuition from the Fine-Tuned Models of Named Architects’ Style Jeong, Hyun Yoo, Youngjin Kim, Youngchae Cha, SeungHyun Lee, Jin-Kook Design Style of Architects Generative AI Image Generation Fine-tuning thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence This paper suggests the potential application of generative artificial intelligence-based image generation technology in the field of architecture, for early phase shape planning, using the styles of renowned architects. The study employed the following approaches: 1) Intensive image generation based on the styles of 20 architects to test the AI's recognition ability and image quality. 2) Additional training was conducted for architects with low recognition rates to construct an enhanced learning model in the quality of image generation. 3) In addition to generating architectural visualization images using existing architects' design styles, alternative styles were proposed through design combinations, aiming to concretize ambiguous idea communication in the early stages of design and enhance its efficiency. The study sheds light on the future prospects of applying this generative AI model in the field of architecture 2024-05-11T09:48:12Z 2024-05-11T09:48:12Z 2024-04-02T15:44:31Z 2023 chapter ONIX_20240402_9791221502893_10 2704-5846 https://library.oapen.org/handle/20.500.12657/89041 9791221502893 https://directory.doabooks.org/handle/20.500.12854/137022 eng Proceedings e report open access image/jpeg n/a https://library.oapen.org/bitstream/20.500.12657/89041/1/9791221502893_91.pdf Firenze University Press 10.36253/979-12-215-0289-3.91 10.36253/979-12-215-0289-3.91 2ec4474d-93b1-4cfa-b313-9c6019b51b1a 9791221502893 9 Florence open access |
| spellingShingle | Design Style of Architects Generative AI Image Generation Fine-tuning thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence Jeong, Hyun Yoo, Youngjin Kim, Youngchae Cha, SeungHyun Lee, Jin-Kook Chapter Generative Design Intuition from the Fine-Tuned Models of Named Architects’ Style |
| title | Chapter Generative Design Intuition from the Fine-Tuned Models of Named Architects’ Style |
| title_full | Chapter Generative Design Intuition from the Fine-Tuned Models of Named Architects’ Style |
| title_fullStr | Chapter Generative Design Intuition from the Fine-Tuned Models of Named Architects’ Style |
| title_full_unstemmed | Chapter Generative Design Intuition from the Fine-Tuned Models of Named Architects’ Style |
| title_short | Chapter Generative Design Intuition from the Fine-Tuned Models of Named Architects’ Style |
| title_sort | chapter generative design intuition from the fine tuned models of named architects style |
| topic | Design Style of Architects Generative AI Image Generation Fine-tuning thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence |
| topic_facet | Design Style of Architects Generative AI Image Generation Fine-tuning thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence |
| url | ONIX_20240402_9791221502893_10 |
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