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Floorplans are commonly used to represent the layout of buildings. Incomputer aided-design (CAD) floorplans are usually represented in the form ofhierarchical graph structures. Research works towards computational techniquesthat facilitate the design process, such as automated analysis andoptimization, often use simple floorplan representations that ignore thesemantics of the space and do not take into account usage related analytics. Wepresent a floorplan embedding technique that uses an attributed graph torepresent the geometric information as well as design semantics and behavioralfeatures of the inhabitants as node and edge attributes. A Long Short-TermMemory (LSTM) Variational Autoencoder (VAE) architecture is proposed andtrained to embed attributed graphs as vectors in a continuous space. A userstudy is conducted to evaluate the coupling of similar floorplans retrievedfrom the embedding space with respect to a given input (e.g., design layout).The qualitative, quantitative and user-study evaluations show that ourembedding framework produces meaningful and accurate vector representations forfloorplans. In addition, our proposed model is a generative model. We studiedand showcased its effectiveness for generating new floorplans. We also releasethe dataset that we have constructed and which, for each floorplan, includesthe design semantics attributes as well as simulation generated humanbehavioral features for further study in the community.

Graph-Based Generative Representation Learning of Semantically and Behaviorally Augmented Floorplans