CGANs for generating architectural interior designs

In interior space planning, the furnishing stage usually entails manual iterative processes. Machine learning has the potential to automate and improve these processes while maintaining creativity and quality. The aim of this study is to develop a furnishing method that leverages machine learning as a means for enhancing design processes. A secondary aim is to develop a set of evaluation metrics for assessing the quality of the results generated from such methods, enabling comparisons between the performance of different models. To achieve these aims, floor plans were tagged and assembled into a comprehensive dataset that was then employed for training and evaluating Conditional Generative Adversarial Network models  to generate furniture layouts within given room boundaries.