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Volumetric design is the first and critical step for professional buildingdesign, where architects not only depict the rough 3D geometry of the buildingbut also specify the programs to form a 2D layout on each floor. Though 2Dlayout generation for a single story has been widely studied, there is nodeveloped method for multi-story buildings. This paper focuses on volumetricdesign generation conditioned on an input program graph. Instead of outputtingdense 3D voxels, we propose a new 3D representation named voxel graph that isboth compact and expressive for building geometries. Our generator is across-modal graph neural network that uses a pointer mechanism to connect theinput program graph and the output voxel graph, and the whole pipeline istrained using the adversarial framework. The generated designs are evaluatedqualitatively by a user study and quantitatively using three metrics: quality,diversity, and connectivity accuracy. We show that our model generatesrealistic 3D volumetric designs and outperforms previous methods and baselines.

Building-GAN: Graph-Conditioned Architectural Volumetric Design Generation