Using generative adversarial networks to create graphical user Interfaces for video games

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Designing and creating a Graphical User Interface (GUI) is a difficult and slow process. It requires a number of professions to all contribute to its development and it can be heavily detrimental to a product if implemented poorly. This research aims to investigate a method of using Generative Adversarial Networks (GANs) to generate new and usable designs for GUIs. GANs are a relatively new architecture for adversarial learning and have been used to good effect in replicating instances of a real dataset. The primary aim is to develop a GAN that is capable of processing a collection of existing GUIs and learn how to replicate these to allow for creation of further designs. These GUI designs need to be formatted in a manner that enables modification, allowing for them to be used by a development team to enhance their production process. Completed work demonstrates numerous approaches at using GANs to create text files that contain the component elements of a GUI. Their results and the release of a similar research paper (GUIGAN) has led to a new approach focusing on more abstract data representation, with a quality control system for ensuring the output data is properly formatted. It is hypothesised that the approach will develop a model capable of creating new, editable GUI designs.
Original languageEnglish
Title of host publicationICMI'21
Subtitle of host publicationproceedings 23rd ACM International Conference on Multimodal Interaction
Place of PublicationNew York
PublisherAssociation for Computing Machinery (ACM)
Number of pages5
ISBN (Print)9781450384810
Publication statusPublished - 18 Oct 2021
Event23rd ACM International Conference on Multimodal Interaction - Le Westin Montréal, Montreal, Canada
Duration: 18 Oct 202122 Oct 2021


Conference23rd ACM International Conference on Multimodal Interaction
Abbreviated titleICMI '21
Internet address


  • Generative adversarial networks
  • Graphical user interfaces,
  • Machine learning
  • Datasets


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