Using Text-to-Image Generation for Architectural Design Ideation
April 20, 2023 Β· Declared Dead Β· π International Journal of Architectural Computing
"No code URL or promise found in abstract"
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Authors
Ville Paananen, Jonas Oppenlaender, Aku Visuri
arXiv ID
2304.10182
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.AI,
cs.CV
Citations
107
Venue
International Journal of Architectural Computing
Last Checked
3 months ago
Abstract
The recent progress of text-to-image generation has been recognized in architectural design. Our study is the first to investigate the potential of text-to-image generators in supporting creativity during the early stages of the architectural design process. We conducted a laboratory study with 17 architecture students, who developed a concept for a culture center using three popular text-to-image generators: Midjourney, Stable Diffusion, and DALL-E. Through standardized questionnaires and group interviews, we found that image generation could be a meaningful part of the design process when design constraints are carefully considered. Generative tools support serendipitous discovery of ideas and an imaginative mindset, enriching the design process. We identified several challenges of image generators and provided considerations for software development and educators to support creativity and emphasize designers' imaginative mindset. By understanding the limitations and potential of text-to-image generators, architects and designers can leverage this technology in their design process and education, facilitating innovation and effective communication of concepts.
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