Recent studies show increasing demands and interests in automatic layout generation, while there is still much room for improving the plausibility and robustness. In this paper, we present a data-driven layout generation framework without model formulation and loss term optimization. We achieve and organize priors directly based on samples from datasets instead of sampling probabilistic distributions. Therefore, our method enables expressing relations among three or more objects that are hard to be mathematically modeled. Subsequently, a non-learning geometric algorithm is proposed to arrange objects considering constraints such as positions of walls and windows. Experiments show that the proposed method outperforms the state-of-the-art and our generated layouts are competitive to those designed by professionals.¹

最近的研究表明，人们对自动布局生成的需求和兴趣不断增加，但仍有很大的提高合理性和鲁棒性的空间。在本文中，我们提出了一个没有模型公式和损失项优化的数据驱动布局生成框架。我们直接基于来自数据集的样本而不是采样概率分布来实现和组织先验。因此，我们的方法能够表达难以数学建模的三个或更多对象之间的关系。随后，提出了一种非学习几何算法来排列对象，考虑到墙壁和窗户的位置等约束。实验表明，所提出的方法优于最先进的方法，我们生成的布局与专业人士设计的布局相比具有竞争力。¹