Development of an Automated Building Façade Visualization Tool Using a Local Identity Additional Trained Model

This study proposes an automated visualization tool for generating building facade images that reflect local identity, focusing on commercial buildings in the Seoul metropolitan area. The research evaluates existing image generation models and improves them through additional training with region-specific data, ultimately implementing an automated facade image generation tool. The process involved constructing a dataset using street view images, optimizing the model through hyperparameter tuning, and conducting additional training on commercial buildings in Seoul. The developed tool integrates 3D modeling environment with trained models, allowing users to easily create and visualize diverse facade design ideas from basic mass forms. It supports various applications, including initial design phase ideation, renovation project visualization, and region-specific design proposals. By combining Image-to-image generative AI with architectural modeling processes, this research presents a novel approach to architectural visualization. The integration of 3D modeling, building information, and realistic visualization in a single tool streamlines the architectural design process and fosters more informed decision-making in the early stages of design, enhancing communication between designers, clients, and stakeholders, potentially.