Environment-Based Prompt Framework for AI-Generated Façade Design

Abstract

As automated retail environments continue to expand globally, their façade design has emerged as a critical factor influencing consumer perception, psychological comfort, and visual appeal. At the same time, current design practices often prioritize technological efficiency over visual wellness. This study proposes a generative AI-assisted design methodology grounded in the Environment-Based Design (EBD) framework. The approach emphasizes visual dimensions of the WELL Building Standard and integrates biophilic design principles to enhance façade aesthetics in automated retail contexts. This study has four research objectives: (1) to extract WELL-aligned visual design variables for façade design through literature, certification mapping, and case analysis; (2) to develop structured prompt strategies and ControlNet-based image generation workflows using Stable Diffusion XL; (3) to evaluate perceptual outcomes through Learned Perceptual Image Patch Similarity (LPIPS) metrics and expert scoring across wellness-relevant dimensions; and (4) to analyze design trade-offs and limitations, and identify opportunities for recursive improvement within the EBD framework. Eight façade images, consisting of the original and seven AI-generated variants, were evaluated by five experts using a 7-point Likert scale across five perceptual criteria. The results show that the "Material + Pattern" strategy received the highest ratings in perceived material quality and natural features; "Color + Material + Pattern" showed the most balanced overall performance. Perceptual similarity was quantitatively assessed using LPIPS, confirming that multidimensional interventions led to greater visual deviation from the original design. Expert comments emphasized the warmth and affinity created by natural textures, while cautioning against excessive decorative complexity. Open-ended feedback was subjected to thematic analysis, revealing nuanced perceptions of design richness, comfort, and realism. This study demonstrates the feasibility of operationalizing health-focused visual principles within an AI-assisted design pipeline. The proposed approach offers a scalable and reproducible method for enhancing the emotional and aesthetic quality of automated retail façades. Future research should extend the scope of visual dimensions, including form, signage clarity, and transparency, and incorporate multimodal user experience evaluations to better reflect real-world engagement.