  We introduce a learning framework for automated floorplan generation which
combines generative modeling using deep neural networks and user-in-the-loop
designs to enable human users to provide sparse design constraints. Such
constraints are represented by a layout graph. The core component of our
learning framework is a deep neural network, Graph2Plan, which converts a
layout graph, along with a building boundary, into a floorplan that fulfills
both the layout and boundary constraints. Given an input building boundary, we
allow a user to specify room counts and other layout constraints, which are
used to retrieve a set of floorplans, with their associated layout graphs, from
a database. For each retrieved layout graph, along with the input boundary,
Graph2Plan first generates a corresponding raster floorplan image, and then a
refined set of boxes representing the rooms. Graph2Plan is trained on RPLAN, a
large-scale dataset consisting of 80K annotated floorplans. The network is
mainly based on convolutional processing over both the layout graph, via a
graph neural network (GNN), and the input building boundary, as well as the
raster floorplan images, via conventional image convolution.
