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From Text to Blueprint: Leveraging Text-to-Image Tools for Floor Plan Creation

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arxiv 2405.17236 v1 pith:IRGN6V34 submitted 2024-05-27 cs.HC

classification cs.HC
keywords designfloortexttext-to-imagetoolsfuturemodelsplan
verification ladder T0 review T1 audit T2 compute T3 formal

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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 potential to drive smarter, more efficient floorplan design, contributing to ongoing discussions on AI integration in the design profession and its future impact.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Text Semantics to Flexible Design: A Residential Layout Generation Method Based on Stable Diffusion Model

    cs.AI 2025-01 reject novelty 5.0 of 10

    A Stable Diffusion model fine-tuned with LoRA and guided by ControlNet generates residential layouts from text prompts that describe room counts, sizes, and connections, with claimed improvements over two baselines.

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