  Volumetric design is the first and critical step for professional building
design, where architects not only depict the rough 3D geometry of the building
but also specify the programs to form a 2D layout on each floor. Though 2D
layout generation for a single story has been widely studied, there is no
developed method for multi-story buildings. This paper focuses on volumetric
design generation conditioned on an input program graph. Instead of outputting
dense 3D voxels, we propose a new 3D representation named voxel graph that is
both compact and expressive for building geometries. Our generator is a
cross-modal graph neural network that uses a pointer mechanism to connect the
input program graph and the output voxel graph, and the whole pipeline is
trained using the adversarial framework. The generated designs are evaluated
qualitatively by a user study and quantitatively using three metrics: quality,
diversity, and connectivity accuracy. We show that our model generates
realistic 3D volumetric designs and outperforms previous methods and baselines.
