Abstract
This paper presents some preliminary results on how generative AI (Artificial Intelligence) can transform key tasks in a typical interior design workflow, namely, ideation, schematic drafting, and layout planning. We show via examples how targeted fine-tuning and deliberated prompt engineering can jumpstart the creative process and improve the overall workflow efficiency and effectiveness. Ideation by collecting targeted reference designs to fine-tune a Stable Diffusion model and cranking out various ideas by simply engineering prompts. Schematic drafting is to test design ideas in the actual environment, where designers model the space structure with specific design elements. We used an actual photo of a plain office as a guidance and teach the model to generate images with specified features, such as different styles, materials, décor objects, while maintaining the underlying spatial configuration. Layout Planning is to organize the functions with comprehensive consideration of functional relationships and layout design principles. The generative model is fine-tuned using selected layout plans, which can accommodate different functional strategies, such as private rooms, large open areas, etc. and provide viable layout options.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Haddad, R.: Research and methodology for interior designers. Procedia Soc. Behav. Sci. 122, 283–291 (2014). https://doi.org/10.1016/j.sbspro.2014.01.1343
McCormack, J., Dorin, A., Innocent, T.: Generative design: a paradigm for design research. In: DRS Bienn. Conf. Ser. (2004)
Singh, V., Gu, N.: Towards an integrated generative design framework. Des. Stud. 33, 185–207 (2012). https://doi.org/10.1016/j.destud.2011.06.001
Goodfellow, I., et al.: Generative adversarial networks. Commun. ACM. 63, 139–144 (2020). https://doi.org/10.1145/3422622
Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5967–5976. IEEE, Honolulu, HI (2017). https://doi.org/10.1109/CVPR.2017.632
Nauata, N., Chang, K.-H., Cheng, C.-Y., Mori, G., Furukawa, Y.: House-GAN: relational generative adversarial networks for graph-constrained house layout generation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) Computer Vision – ECCV 2020. LNCS, vol. 12346, pp. 162–177. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_10
Chaillou, S.: ArchiGAN: Artificial intelligence × architecture. In: Yuan, P.F., Xie, M., Leach, N., Yao, J., Wang, X. (eds.) Architectural Intelligence, pp. 117–127. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-6568-7_8
Krish, S.: A practical generative design method. Comput. Aided Des. 43, 88–100 (2011). https://doi.org/10.1016/j.cad.2010.09.009
Gozalo-Brizuela, R., Garrido-Merchan, E.C.: ChatGPT is Not All You Need. A State of the Art Review of Large Generative AI Models (2023). http://arxiv.org/abs/2301.04655
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-Resolution Image Synthesis with Latent Diffusion Models (2022). https://doi.org/10.48550/arXiv.2112.10752
Zhang, L., Agrawala, M.: Adding Conditional Control to Text-to-Image Diffusion Models (2023). https://doi.org/10.48550/arXiv.2302.05543
Hu, E.J., et al.: LoRA: Low-Rank Adaptation of Large Language Models (2021). http://arxiv.org/abs/2106.09685
Li, J., Li, D., Xiong, C., Hoi, S.: BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation (2022). http://arxiv.org/abs/2201.12086
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
He, Z., Li, X., Fan, L., Wang, H.J. (2023). Revamping Interior Design Workflow Through Generative Artificial Intelligence. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2023 Posters. HCII 2023. Communications in Computer and Information Science, vol 1835. Springer, Cham. https://doi.org/10.1007/978-3-031-36001-5_78
Download citation
DOI: https://doi.org/10.1007/978-3-031-36001-5_78
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-36000-8
Online ISBN: 978-3-031-36001-5
eBook Packages: Computer ScienceComputer Science (R0)