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Planning Alternative Building Façade Designs Using Image Generative AI and Local Identity

  • Hayoung Jo
  • Sumin Chae
  • Su Hyung Choi
  • Jin-Kook Lee

This paper describes an approach utilizing Generative AI to support diverse design alternatives for building facades based on the local identity. Extensive research is currently being conducted for exploring the applications of LLM-based generative AI models to diverse kinds of visualizations. By applying generative AI to facade design, the study aims to develop additional training models that generate alternative design options reflecting local identity, facilitating the acquisition of remodel design images from multiple texts and images. Building facades in cities and regions are essential for people's aesthetic perception and understanding of the local environment, enabling the recognition and differentiation of specific areas from others. Therefore, implementation method of the additional training model based on generative AI in this study, reflecting this, can be summarized as follows: 1) collection and pre-processing of image data using Street View, 2) pairing text data with image data, 3) conducting additional training and testing with various inputs, 4) proposing relevant application methods. This approach can be expected to enable efficient communication of design at an early stage of the architectural design process beyond traditional 3D modeling and rendering tools

  • Keywords:
  • Building facade,
  • Generative AI,
  • Local identity,
  • Design alternative,
  • Additional Training Model,
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Hayoung Jo

Yonsei University, Korea (the Republic of)

Sumin Chae

Yonsei University, Korea (the Republic of)

Su Hyung Choi

Yonsei University, Korea (the Republic of)

Jin-Kook Lee

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

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  2. Kim, J., & Lee, J.K. (2020) Stochastic Detection of Interior Design Styles Using a Deep-Learning Model for Reference Images. Appl. Sci. 10, 7299. DOI: 10.3390/app10207299
  3. Kim, Y., & Lee, J.K. (2022). PROCESSING OF 360 PANORAMIC IMAGES FOR ARCHITECTURAL INTERIOR IMAGE TRAINING ARCHIVE, ConVR 2022 conference.
  4. Hu, E. J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L. & Chen, W. (2021). Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685.
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  7. Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125.
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  10. Rob Kier, Elements of Architecture, (1984).
  11. OpenAI, "ChatGPT: Optimizing Language Models for Dialogue," 2023. [Online]. Available: https://openai.com/blog/chatgpt/.
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  • Publication Year: 2023
  • Pages: 926-932

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  • Publication Year: 2023

Chapter Information

Chapter Title

Planning Alternative Building Façade Designs Using Image Generative AI and Local Identity

Authors

Hayoung Jo, Sumin Chae, Su Hyung Choi, Jin-Kook Lee

DOI

10.36253/979-12-215-0289-3.92

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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