  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.
