Using Generative Adversarial Networks to Create Graphical User Interfaces for Video Games
Christopher Acornley · 2021
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.