From Text to Blueprint: Leveraging Text-to-Image Tools for Floor Plan Creation
Xiaoyu Li, Jonathan Benjamin, Xin Zhang

TL;DR
This paper investigates how AI-powered text-to-image synthesis tools can be used to generate detailed and functional architectural floor plans from textual descriptions, aiming to enhance design efficiency and creativity.
Contribution
It evaluates the effectiveness of current AI models like Stable Diffusion, GANs, and VAEs in generating residential floor plans from text prompts, highlighting their potential and limitations.
Findings
AI models can generate complex floor plans from text prompts
Current tools improve design efficiency and creativity
Limitations include accuracy and detail in generated plans
Abstract
Artificial intelligence is revolutionizing architecture through text-to-image synthesis, converting textual descriptions into detailed visual representations. We explore AI-assisted floor plan design, focusing on technical background, practical methods, and future directions. Using tools like, Stable Diffusion, AI leverages models such as Generative Adversarial Networks and Variational Autoencoders to generate complex and functional floorplans designs. We evaluates these AI models' effectiveness in generating residential floor plans from text prompts. Through experiments with reference images, text prompts, and sketches, we assess the strengths and limitations of current text-to-image technology in architectural visualization. Architects can use these AI tools to streamline design processes, create multiple design options, and enhance creativity and collaboration. We highlight AI's…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsBIM and Construction Integration · 3D Modeling in Geospatial Applications
