House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation
Nelson Nauata, Kai-Hung Chang, Chin-Yi Cheng, Greg Mori, Yasutaka Furukawa
Abstract
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. We will publicly share all our code and data."
BibTeX
@inproceedings{eccv2020_houseganrelation,
title = {House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout Generation},
author = {Nelson Nauata and Kai-Hung Chang and Chin-Yi Cheng and Greg Mori and Yasutaka Furukawa},
booktitle = {ECCV 2020},
year = {2020}
}