Linguistics

Process (computing)

Engineering

Philosophy

Generative model

Theoretical computer science

Systems engineering

Archaeology

Computer science

Natural language processing

Generative grammar

Task (project management)

Graph

Politics

Political science

Artificial intelligence

Representation (politics)

Floor plan

Architectural engineering

Programming language

Engineering drawing

Human–computer interaction

Interior design

Plan (archaeology)

History

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, which will be released at: https:// hidden-link-for-submission.

Intelligent Home 3D: Automatic 3D-House Design From Linguistic Descriptions Only