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Generative Design Intuition from the Fine-Tuned Models of Named Architects’ Style

  • Youngjin Yoo
  • Hyun Jeong
  • Youngchae Kim
  • SeungHyun Cha
  • Jin-Kook Lee

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

  • Keywords:
  • Design Style of Architects,
  • Generative AI,
  • Image Generation,
  • Fine-tuning,
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Youngjin Yoo

Yonsei University, Korea (the Republic of) - ORCID: 0009-0002-5362-328X

Hyun Jeong

Yonsei University, Korea (the Republic of)

Youngchae Kim

Yonsei University, Korea (the Republic of) - ORCID: 0000-0003-2009-0376

SeungHyun Cha

Korea Advanced Institute of Science Technology, Korea (the Republic of) - ORCID: 0009-0004-7001-2346

Jin-Kook Lee

Yonsei University, Korea (the Republic of) - ORCID: 0000-0002-5179-6550

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  • Publication Year: 2023
  • Pages: 917-925

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  • Publication Year: 2023

Chapter Information

Chapter Title

Generative Design Intuition from the Fine-Tuned Models of Named Architects’ Style

Authors

Youngjin Yoo, Hyun Jeong, Youngchae Kim, SeungHyun Cha, Jin-Kook Lee

DOI

10.36253/979-12-215-0289-3.91

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Bibliographic Information

Book Title

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

Book Subtitle

Managing the Digital Transformation of Construction Industry

Editors

Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi

Peer Reviewed

Publication Year

2023

Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press

DOI

10.36253/979-12-215-0289-3

eISBN (pdf)

979-12-215-0289-3

eISBN (xml)

979-12-215-0257-2

Series Title

Proceedings e report

Series ISSN

2704-601X

Series E-ISSN

2704-5846

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