This paper proposes a novel graph-constrained generative adversarial network,
whose generator and discriminator are built upon relational architecture. The
main idea is to encode the constraint into the graph structure of its
relational networks. We have demonstrated the proposed architecture for a new
house layout generation problem, whose task is to take an architectural
constraint as a graph (i.e., the number and types of rooms with their spatial
adjacency) and produce a set of axis-aligned bounding boxes of rooms. We
measure the quality of generated house layouts with the three metrics: the
realism, the diversity, and the compatibility with the input graph constraint.
Our qualitative and quantitative evaluations over 117,000 real floorplan images
demonstrate that the proposed approach outperforms existing methods and
baselines. We will publicly share all our code and data.

本文提出了一种基于关系架构的新型图约束生成对抗网络，其生成器和鉴别器利用关系结构进行建模，旨在将约束编码到其关系网络的图结构中，将建筑约束作为图形输入，并生成一套轴对齐的房间边框，通过 117000 张实际的平面图像的定量和定性评估表明，该方法优于现有的方法和基线。