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

arXiv: https://arxiv.org/abs/2001.05308 · HTML: https://mlsanity.com/paper/2001.05308/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jan 2020.

## Built on by (most cited first, 8 of 8)

- [LayoutPrompter: Awaken the Design Ability of Large Language Models](https://mlsanity.com/paper/2311.06495/) (2023)
- [UICoder: Finetuning Large Language Models to Generate User Interface Code through Automated Feedback](https://mlsanity.com/paper/2406.07739/) (2024)
- [UI Layout Generation with LLMs Guided by UI Grammar](https://mlsanity.com/paper/2310.15455/) (2023)
- [Graph4GUI: Graph Neural Networks for Representing Graphical User Interfaces](https://mlsanity.com/paper/2404.13521/) (2024)
- [SciPostGen: Bridging the Gap between Scientific Papers and Poster Layouts](https://mlsanity.com/paper/2511.22490/) (2025)
- [CoGen: Creation of Reusable UI Components in Figma via Textual Commands](https://mlsanity.com/paper/2601.10536/) (2026)
- [Graph Neural Networks for Natural Language Processing: A Survey](https://mlsanity.com/paper/2106.06090/) (2021)
- [StructLayoutFormer:Conditional Structured Layout Generation via Structure Serialization and Disentanglement](https://mlsanity.com/paper/2510.26141/) (2025)

## Abstract (arXiv)

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.