一句话：提出基于SeqGAN的GUI设计自动生成模型，通过复用组件并建模风格兼容性与结构来合成新设计。
该论文针对GUI设计创新要求高、设计负担重的问题，提出自动生成GUI设计的工具GUIGAN。不同于像素级图像生成，GUIGAN将设计生成视为组件序列组合问题，从现有移动应用GUI中收集组件，并基于SeqGAN建模GUI组件的风格兼容性与整体结构，从而复用组件生成新的设计。评估中，模型在FID上比最佳基线方法优30.77%，在1-NNA上优12.35%；试点用户研究进一步表明，所生成的全新GUI设计具有可接受性，展示了该方法辅助设计师的潜力。
Graphical User Interface (GUI) is ubiquitous in almost all modern desktop software, mobile applications and online websites. A good GUI design is crucial to the success of the software in the market, but designing a good GUI which requires much innovation and creativity is difficult even to well-trained designers. In addition, the requirement of rapid development of GUI design also aggravates designers' working load. So, the availability of various automated generated GUIs can help enhance the design personalization and specialization as they can cater to the taste of different designers. To assist designers, we develop a model tool to automatically generate GUI designs. Different from conventional image generation models based on image pixels, our tool is to reuse GUI components collected from existing mobile app GUIs for composing a new design which is similar to natural-language generation. Our tool is based on SeqGAN by modelling the GUI component style compatibility and GUI structure. The evaluation demonstrates that our model significantly outperforms the best of the baseline methods by 30.77% in Fr'echet Inception distance (FID) and 12.35% in 1-Nearest Neighbor Accuracy (1-NNA). Through a pilot user study, we provide initial evidence of the usefulness of our approach for generating acceptable brand new GUI designs.
DOI 原文 · arXiv · PDF（开放获取） · DBLP
@inproceedings{ZhaoCL021,
title = {GUIGAN: Learning to Generate GUI Designs Using Generative Adversarial Networks},
author = {Tianming Zhao and Chunyang Chen and Yuanning Liu and Xiaodong Zhu},
booktitle = {ICSE},
year = {2021},
doi = {10.1109/icse43902.2021.00074}
}