House-GAN: Relational Generative Adversarial Networks for Graph-Constrained House Layout Generation
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.
Cite
Text
Nauata et al. "House-GAN: Relational Generative Adversarial Networks for Graph-Constrained House Layout Generation." Proceedings of the European Conference on Computer Vision (ECCV), 2020. doi:10.1007/978-3-030-58452-8_10Markdown
[Nauata et al. "House-GAN: Relational Generative Adversarial Networks for Graph-Constrained House Layout Generation." Proceedings of the European Conference on Computer Vision (ECCV), 2020.](https://mlanthology.org/eccv/2020/nauata2020eccv-housegan/) doi:10.1007/978-3-030-58452-8_10BibTeX
@inproceedings{nauata2020eccv-housegan,
title = {{House-GAN: Relational Generative Adversarial Networks for Graph-Constrained House Layout Generation}},
author = {Nauata, Nelson and Chang, Kai-Hung and Cheng, Chin-Yi and Mori, Greg and Furukawa, Yasutaka},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
year = {2020},
doi = {10.1007/978-3-030-58452-8_10},
url = {https://mlanthology.org/eccv/2020/nauata2020eccv-housegan/}
}