SCAPE: Searching Conceptual Architecture Prompts using Evolution
CoRR(2024)
摘要
Conceptual architecture involves a highly creative exploration of novel
ideas, often taken from other disciplines as architects consider radical new
forms, materials, textures and colors for buildings. While today's generative
AI systems can produce remarkable results, they lack the creativity
demonstrated for decades by evolutionary algorithms. SCAPE, our proposed tool,
combines evolutionary search with generative AI, enabling users to explore
creative and good quality designs inspired by their initial input through a
simple point and click interface. SCAPE injects randomness into generative AI,
and enables memory, making use of the built-in language skills of GPT-4 to vary
prompts via text-based mutation and crossover. We demonstrate that compared to
DALL-E 3, SCAPE enables a 67
in quality and effectiveness of use; we show that in just 3 iterations SCAPE
has a 24
optimization of images by users. We use more than 20 independent architects to
assess SCAPE, who provide markedly positive feedback.
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