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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

  1. Abdallah, Z., Du, L., & Webb, G. (2017). Data Preparation. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer. DOI: 10.1007/978-1-4899-7687-1_62
  2. Akin, O. (1978). How do architects design. Artificial Intelligence and Pattern Recognition in Computer Aided Design, 65-104.
  3. Atilola, O., Tomko, M., & Linsey, J. S. (2016). The effects of representation on idea generation and design fixation: A study comparing sketches and function trees. Design studies, 42, 110-136. DOI: 10.1016/j.destud.2015.10.005
  4. Borji, A. (2023). Generated Faces in the Wild: Quantitative Comparison of Stable Diffusion, Midjourney and DALL-E 2. DOI: 10.48550/arXiv.2210.00586
  5. Chiu, M. L. (1995). Collaborative design in CAAD studios: shared ideas, resources, and representations. In Proceedings of International Conference on CAAD Future (Vol. 95, pp. 749-759).
  6. David, A., Joy, E., Kumar, S., & Bezaleel, S.J. (2022). Integrating Virtual Reality with 3D Modeling for Interactive Architectural Visualization and Photorealistic Simulation: A Direction for Future Smart Construction Design Using a Game Engine. Second International Conference on Image Processing and Capsule Networks, 300. DOI: 10.1007/978-3-030-84760-9_17
  7. Eastman, C.M. (1999). Building Product Models: Computer Environments, Supporting Design and Construction. (1st ed.). CRC Press: Florida, (Chapter 1).
  8. Fonseca, D., Redondo, E., Valls, F., & Villagrasa, S. (2017). Technological adaptation of the student to the educational density of the course. A case study: 3D architectural visualization, Computers in Human Behavior, 72, 599-611. DOI: 10.1016/j.chb.2016.05.048
  9. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., ... & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27.
  10. Greenberg, D. P. (1974). Computer graphics in architecture. Scientific American, 230(5), 98-107.
  11. Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., ... & Chen, W. (2021). Lora: Low-rank adaptation of large language models.
  12. Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., & Aila, T. (2020). Analyzing and improving the image quality of stylegan. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 8110-8119). DOI: 10.1109/CVPR42600.2020.00813
  13. Kheir Al-Kodmany. (2001). Visualization Tools and Methods for Participatory Planning and Design. Journal of Urban Technology, 8(2), 1-37. DOI: 10.1080/106307301316904772
  14. Kim, J., & Lee, J. K. (2020). Stochastic detection of interior design styles using a deep-learning model for reference images. Applied Sciences, 10(20), 7299. DOI: 10.3390/app10207299
  15. Kim, J., Song, J., & Lee, J. K. (2019, January). Approach to auto-recognition of design elements for the intelligent management of interior pictures. In Proceedings of the 24th International Conference on Computer-Aided Architectural Design Research in Asia: Intelligent and Informed, CAADRIA (pp. 785-794). DOI: 10.52842/conf.caadria.2019.2.785
  16. Koutamanis, A. (2000) Digital architectural visualization. Automation in Construction, 9(4), 347-360. DOI: 10.1016/S0926-5805(99)00018-7
  17. Oppenlaender, J. (2022, November). The Creativity of Text-to-Image Generation. In Proceedings of the 25th International Academic Mindtrek Conference (pp. 192-202). DOI: 10.1145/3569219.3569352
  18. Oppenlaender, J., Visuri, A., Paananen, V., Linder, R., & Silvennoinen, J. (2023). Text-to-Image Generation: Perceptions and Realities. DOI: 10.48550/arXiv.2303.13530
  19. Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical text-conditional image generation with clip latents. DOI: 10.48550/arXiv.2204.06125
  20. Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10684-10695). DOI: 10.48550/arXiv.2112.10752
  21. Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E. L., ... & Norouzi, M. (2022). Photorealistic text-to-image diffusion models with deep language understanding. Advances in Neural Information Processing Systems, 35, 36479-36494. DOI: 10.48550/arXiv.2205.11487
  • Publication Year: 2023
  • Pages: 917-925

  • Publication Year: 2023

Chapter Information

Chapter Title

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


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



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Publication Year


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© 2023 Author(s)

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CC BY-NC 4.0

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CC0 1.0

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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


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

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© 2023 Author(s)

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CC BY-NC 4.0

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CC0 1.0

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Firenze University Press



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