Interactive AI for Generative Housing Design Based on Graph Neural Networks and Deep Generative Models
Tian Xia (Design & Construction Management)
Alex Ledbetter (Student TU Delft)
Alexandru Bobe (Student TU Delft)
Jeroen Hofland (Student TU Delft)
Berend Krouwels (Student TU Delft)
Tong Wang (Design & Construction Management)
L. Siebert (TU Delft - Interactive Intelligence)
Paul W. Chan (Design & Construction Management)
Jian Yang (Shanghai Jiao Tong University)
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Abstract
Automatic design tools are being developed to assist designers handle tedious work at scale. However, knowledge gaps still exist in harnessing deep learning models to learn from human experience for more efficient design generation while keeping the data understandable and interoperable. Moreover, human-in-the-loop approach is largely neglected, which are essential for more user-centered design. This research utilizes graph data to parametrically represent housing designs and graph-representative deep generative models for design generation, which provides an interactive design approach for the users at every step. This method would facilitate the human-centered design process by returning feasible and parametric housing design alternatives.
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