[2304.09012] GUILGET: GUI Layout GEneration with Transformer
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Computer Science > Computer Vision and Pattern Recognition

arXiv:2304.09012 (cs)
[Submitted on 18 Apr 2023]

Title:GUILGET: GUI Layout GEneration with Transformer

Authors:Andrey Sobolevsky, Guillaume-Alexandre Bilodeau, Jinghui Cheng, Jin L.C. Guo
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Abstract:Sketching out Graphical User Interface (GUI) layout is part of the pipeline of designing a GUI and a crucial task for the success of a software application. Arranging all components inside a GUI layout manually is a time-consuming task. In order to assist designers, we developed a method named GUILGET to automatically generate GUI layouts from positional constraints represented as GUI arrangement graphs (GUI-AGs). The goal is to support the initial step of GUI design by producing realistic and diverse GUI layouts. The existing image layout generation techniques often cannot incorporate GUI design constraints. Thus, GUILGET needs to adapt existing techniques to generate GUI layouts that obey to constraints specific to GUI designs. GUILGET is based on transformers in order to capture the semantic in relationships between elements from GUI-AG. Moreover, the model learns constraints through the minimization of losses responsible for placing each component inside its parent layout, for not letting components overlap if they are inside the same parent, and for component alignment. Our experiments, which are conducted on the CLAY dataset, reveal that our model has the best understanding of relationships from GUI-AG and has the best performances in most of evaluation metrics. Therefore, our work contributes to improved GUI layout generation by proposing a novel method that effectively accounts for the constraints on GUI elements and paves the road for a more efficient GUI design pipeline.
Comments: 12 pages, 5 figures, Canadian AI Conference 2023
Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2304.09012 [cs.CV]
  (or arXiv:2304.09012v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2304.09012

Submission history

From: Andrey Sobolevsky [view email]
[v1] Tue, 18 Apr 2023 14:27:34 UTC (1,459 KB)
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