Auto Completion of User Interface Layout Design Using Transformer-Based Tree Decoders

Yang Li, Julien Amelot, Xin Zhou, Samy Bengio, Si Si

TL;DR: The paper investigates several Transformer-based decoder models for predicting a complete layout given a partial layout tree.

Abstract: 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.

Keywords: Transformer, decoder, user interface, layout design