- CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
- A cura di Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi
Gen AI and Interior Design Representation: Applying Design Styles Using Fine-Tuned Models
- Hyun Jeong
- Youngchae Kim
- Youngjin Yoo
- SeungHyun Cha
- Jin-Kook Lee
- © 2023 Author(s) |
- CC BY-NC 4.0
- DOI: 10.36253/979-12-215-0289-3.95
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,
Yonsei University, Korea (the Republic of)
Yonsei University, Korea (the Republic of) - ORCID: 0000-0003-2009-0376
Yonsei University, Korea (the Republic of) - ORCID: 0009-0002-5362-328X
Korea Advanced Institute of Science Technology, Korea (the Republic of) - ORCID: 0009-0004-7001-2346
Yonsei University, Korea (the Republic of) - ORCID: 0000-0002-5179-6550
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Informazioni sul capitolo
Titolo del capitolo
Gen AI and Interior Design Representation: Applying Design Styles Using Fine-Tuned Models
Autori
Hyun Jeong, Youngchae Kim, Youngjin Yoo, SeungHyun Cha, Jin-Kook Lee
DOI
10.36253/979-12-215-0289-3.95
Opera sottoposta a peer review
Anno di pubblicazione
2023
Copyright
© 2023 Author(s)
Licenza d'uso
Licenza dei metadati
Informazioni bibliografiche
Titolo del libro
CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality
Sottotitolo del libro
Managing the Digital Transformation of Construction Industry
Curatori
Pietro Capone, Vito Getuli, Farzad Pour Rahimian, Nashwan Dawood, Alessandro Bruttini, Tommaso Sorbi
Opera sottoposta a peer review
Anno di pubblicazione
2023
Copyright
© 2023 Author(s)
Licenza d'uso
Licenza dei metadati
Editore
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
Collana
Proceedings e report
ISSN della collana
2704-601X
e-ISSN della collana
2704-5846