It has been of increasing interest in the field to develop automatic machineries to facilitate the design process. In this paper, we focus on assisting graphical user interface (UI) layout design, a crucial task in app development. Given a partial layout, which a designer has entered, our model learns to complete the layout by predicting the remaining UI elements with a correct position and dimension as well as the hierarchical structures. Such automation will significantly ease the effort of UI designers and developers. While we focus on interface layout prediction, our model can be generally applicable for other layout prediction problems that involve tree structures and 2-dimensional placements. Particularly, we design two versions of Transformer-based tree decoders: Pointer and Recursive Transformer, and experiment with these models on a public dataset. We also propose several metrics for measuring the accuracy of tree prediction and ground these metrics in the domain of user experience. These contribute a new task and methods to deep learning research.

中文翻译：

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使用基于转换器的树解码器自动完成用户界面布局设计

开发自动机械以促进设计过程在该领域越来越受到关注。在本文中，我们专注于辅助图形用户界面 (UI) 布局设计，这是应用程序开发中的一项关键任务。给定设计师输入的部分布局，我们的模型通过预测具有正确位置和尺寸以及层次结构的剩余 UI 元素来学习完成布局。这种自动化将大大减轻 UI 设计人员和开发人员的工作量。虽然我们专注于界面布局预测，但我们的模型通常适用于其他涉及树结构和二维布局的布局预测问题。特别是，我们设计了两个版本的基于 Transformer 的树解码器：Pointer 和 Recursive Transformer，并在公共数据集上试验这些模型。我们还提出了几个衡量树预测准确性的指标，并将这些指标用于用户体验领域。这些为深度学习研究贡献了新的任务和方法。