Auto Completion of User Interface Layout Design Using Transformer-Based Tree Decoders
This work addresses a crucial task in app development to ease the effort of UI designers and developers, though it is incremental as it builds on existing Transformer-based methods.
The paper tackles the problem of automatically completing graphical user interface (UI) layouts from partial inputs by predicting remaining elements with correct positions, dimensions, and hierarchical structures, achieving results on a public dataset with proposed metrics grounded in user experience.
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.