Contained in:
Book Chapter

Gen AI and Interior Design Representation: Applying Design Styles Using Fine-Tuned Models

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

This paper explores the applicability of Image-generation AI in the field of interior architectural design, with a particular focus on automating interior design representation based on design styles. Interior design representation involves a complex process that integrates visual elements with functionality and user experience. Effectively visualizing this process is essential for facilitating communication among the various stakeholders involved in the design process. However, traditional visualization methods are constrained by expert resources, costs, and time limitations. In contrast, image-generation AI has the potential to automate various design elements, including design styles, components, and spatial arrangements, to enhance representation. In this study, we evaluated the performance of a base model using various design styles and, based on the evaluation results, selected styles for fine-tuning. The methodology for fine-tuning these design styles involved the following steps: 1) data preparation and preprocessing, 2) hyperparameter optimization, and 3) model training and construction. Utilizing the fine-tuned model thus constructed, we conducted image generation demonstrations. The research results revealed that design styles not well represented by the base model were effectively captured, and high-quality images were generated by the fine-tuned model. Notably, this fine-tuned model demonstrated the ability to represent images of specific design styles with a high degree of accuracy in capturing the characteristics and keywords associated with each style, compared to the base model. This implies that through fine-tuning image-generation AI, a wide range of applications can be inferred when aiming to create customized designs by considering these aspects. In conclusion, this study explores an efficient approach to interior design representation in the field of interior architecture by employing image-generation AI and proposes a method to effectively generate visualized images by training on design style keywords. Through this approach, our study can contribute to improving the interior design process by facilitating the generation of visualized images that reflect design styles. Furthermore, the study aims to suggest the potential for applying this approach not only to the field of interior architecture but also across various domains to achieve effective visualization

  • Keywords:
  • Interior Architecture Design,
  • Interior Design Representation,
  • Generative AI,
  • Model Fine-tuning,
+ Show More

Hyun Jeong

Yonsei University, Korea (the Republic of)

Youngchae Kim

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

Youngjin Yoo

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

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. Ah-Soon, C., & Tombre, K. (1997). Variations on the analysis of architectural drawings. In Proceedings of the fourth international conference on document analysis and recognition (Vol. 1, pp. 347-351). IEEE.
  2. Brock, A., Donahue, J., & Simonyan, K. (2018). Large scale GAN training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096.
  3. Ching, Francis DK. (2011). A visual dictionary of architecture: John Wiley & Sons.
  4. 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).
  5. Eckert, C.; Stacey, M. Sources of inspiration: A language of design. Des. Stud. 2000, 21, 523–538.
  6. Goldschmidt, G. Creative architectural design: Reference versus precedence. J. Archit. Plan. Res. 1998, 258–270.
  7. Ho, J., Jain, A., & Abbeel, P. (2020). Denoising diffusion probabilistic models. Advances in neural information processing systems, 33, 6840-6851.
  8. Isola, P., Zhu, J. Y., Zhou, T., & Efros, A. A. (2017). Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1125-1134).
  9. Jeong, H., & Lee, J. K. (2023). Study on the Applicability of Image Generation AI in Interior Architecture: Generating Images Based on Interior Design Styles. Proceeding of Spring Annual Conference of KHA, Vol.35, No.1 (pp. 247-250), Jeju, Korea.
  10. 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).
  11. 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
  12. 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).
  13. Lee, S.H, Kim, J.S, Song, J.Y & Lee, J.K. (2020). Augmented Reality-Based Approach for Design On-Site Visualization - Focusing on the Example of Space Improvement Design at a University Academic Information Center. Journal of the Korean Society of Interior Design, 29(1), 97-104.
  14. Lee, J.K., Lee, S., Kim, Y., & Kim, S. (2023). Augmented virtual reality and 360 spatial visualization for supporting user-engaged design, Journal of Computational Design and Engineering, Volume 10(3), Pages 1047–1059, DOI: 10.1093/jcde/qwad035
  15. Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., & Guo, B. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 10012-10022).
  16. Nichol, A., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., McGrew, B., & Chen, M. (2021). Glide: Towards photorealistic image generation and editing with text-guided diffusion models.
  17. Oppenlaender, J. 2022. The creativity of text-to-image generation. In Proceedings of the 25th International Academic Mindtrek Conference. pp. 192-202.
  18. Oxman, R. (2006). Theory and design in the first digital age. Design studies, 27(3), 229-265.
  19. Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. 2022. Hierarchical text-conditional image generation with clip latents. arXiv preprint 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.
  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.
  22. Song, J., Meng, C., & Ermon, S. (2020). Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502.
  23. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., & Polosukhin, I. 2017. Attention is all you need. Advances in neural information processing systems, 30.
  • Publication Year: 2023
  • Pages: 950-957

  • Publication Year: 2023

Chapter Information

Chapter Title

Gen AI and Interior Design Representation: Applying Design Styles Using Fine-Tuned Models


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



Peer Reviewed

Publication Year


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


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

Peer Reviewed

Publication Year


Copyright Information

© 2023 Author(s)

Content License

CC BY-NC 4.0

Metadata License

CC0 1.0

Publisher Name

Firenze University Press



eISBN (pdf)


eISBN (xml)


Series Title

Proceedings e report

Series ISSN


Series E-ISSN






Export Citation


Open Access Books

in the Catalogue


Book Chapters





from 873 Research Institutions

of 64 Nations


scientific boards

from 340 Research Institutions

of 43 Nations



from 345 Research Institutions

of 37 Nations