This paper proposes a novel generative adversarial layout refinement network for automated floorplan generation. Our architecture is an integration of a graph-constrained relational GAN and a conditional GAN, where a previously generated layout becomes the next input constraint, enabling iterative refinement. A surprising discovery of our research is that a simple non-iterative training process, dubbed component-wise GT-conditioning, is effective in learning such a generator. The iterative generator also creates a new opportunity in further improving a metric of choice via meta-optimization techniques by controlling when to pass which input constraints during iterative layout refinement. Our qualitative and quantitative evaluation based on the three standard metrics demonstrate that the proposed system makes significant improvements over the current state-of-the-art, even competitive against the ground-truth floorplans, designed by professional architects.

翻译：本文建议为自动地平计划生成建立一个新型的基因对抗版面设计改进网络。 我们的架构是整合一个受图形限制的GAN和有条件的GAN, 将先前形成的版面设计变成下一个输入限制, 使得能够进行迭接改进。 我们的研究中令人惊讶的一个发现是,一个简单的非模块化培训过程,即所谓的组件化的GT-调制,能够有效地学习这样的发电机。 迭接生成器还创造了一个新机会,通过元优化技术,控制何时通过迭接版设计改进中的投入限制来进一步改进一个选择指标。 我们基于三个标准指标的定性和定量评估表明,拟议的系统大大改进了专业建筑师设计的当前最先进的系统,甚至有竞争力的地面图层规划。