Abstract
Generative AI, specifically text-to-image models, have revolutionized interior architectural design by enabling the rapid translation of conceptual ideas into visual representations from simple text prompts. While generative AI can produce visually appealing images they often lack actionable data for designers In this work, we propose a novel pipeline that integrates DALL-E 3 with a materials dataset to enrich AI-generated designs with sustainability metrics and material usage insights. After the model generates an interior design image, a post-processing module identifies the top ten materials present and pairs them with carbon dioxide equivalent (CO2e) values from a general materials dictionary. This approach allows designers to immediately evaluate environmental impacts and refine prompts accordingly. We evaluate the system through three user tests: (1) no mention of sustainability to the user prior to the prompting process with generative AI, (2) sustainability goals communicated to the user before prompting, and (3) sustainability goals communicated along with quantitative CO2e data included in the generative AI outputs. Our qualitative and quantitative analyses reveal that the introduction of sustainability metrics in the third test leads to more informed design decisions, however, it can also trigger decision fatigue and lower overall satisfaction. Nevertheless, the majority of participants reported incorporating sustainability principles into their workflows in the third test, underscoring the potential of integrated metrics to guide more ecologically responsible practices. Our findings showcase the importance of balancing design freedom with practical constraints, offering a clear path toward holistic, data-driven solutions in AI-assisted architectural design.
Abstract (translated)
生成式AI，特别是文本到图像模型，在室内建筑设计领域引发了革命性的变化。这些技术通过从简单的文本提示中快速将概念性想法转化为视觉表现形式，大大提升了设计过程的效率。然而，尽管生成式AI能够创建出吸引人的图像，但它们往往缺乏设计师可用的实际数据信息。 在本文中，我们提出了一种新的工作流程，该流程整合了DALL-E 3与材料数据库，以增强由AI生成的设计方案，使其包含可持续性指标和材料使用情况。具体来说，在模型生成室内设计图之后，一个后处理模块会识别出图像中的前十大材料，并从通用材料字典中匹配每种材料的二氧化碳当量（CO2e）数值。 这种策略允许设计师立即评估环境影响并相应地调整提示内容。我们通过三种用户测试来评价该系统： 1. 在生成AI的过程中，不向用户提供有关可持续性的信息； 2. 在生成过程前，先与用户沟通可持续性目标； 3. 除第二点外，在生成的AI输出中还包含定量的CO2e数据。 我们的定性和定量分析表明，第三种测试中的可持续性指标引入使设计师能够做出更为明智的设计决策，但同时也会导致决策疲劳并降低整体满意度。然而，大多数参与者报告在第三次测试中将可持续原则融入了他们的工作流程之中，这凸显出综合指标引导更具生态责任感实践的潜力。 我们的研究结果强调了在AI辅助建筑设计过程中平衡设计自由度与实际约束的重要性，并为实现全面、数据驱动的设计方案提供了明确的道路。
URL
https://arxiv.org/abs/2506.15008
