  Home design is a complex task that normally requires architects to finish
with their professional skills and tools. It will be fascinating that if one
can produce a house plan intuitively without knowing much knowledge about home
design and experience of using complex designing tools, for example, via
natural language. In this paper, we formulate it as a language conditioned
visual content generation problem that is further divided into a floor plan
generation and an interior texture (such as floor and wall) synthesis task. The
only control signal of the generation process is the linguistic expression
given by users that describe the house details. To this end, we propose a House
Plan Generative Model (HPGM) that first translates the language input to a
structural graph representation and then predicts the layout of rooms with a
Graph Conditioned Layout Prediction Network (GC LPN) and generates the interior
texture with a Language Conditioned Texture GAN (LCT-GAN). With some
post-processing, the final product of this task is a 3D house model. To train
and evaluate our model, we build the first Text-to-3D House Model dataset.
