Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Application of Variational AutoEncoder (VAE) Model and Image Processing Approaches in Game Design

Version 1 : Received: 28 February 2023 / Approved: 1 March 2023 / Online: 1 March 2023 (11:17:12 CET)

A peer-reviewed article of this Preprint also exists.

Mak, H.W.L.; Han, R.; Yin, H.H.F. Application of Variational AutoEncoder (VAE) Model and Image Processing Approaches in Game Design. Sensors 2023, 23, 3457. Mak, H.W.L.; Han, R.; Yin, H.H.F. Application of Variational AutoEncoder (VAE) Model and Image Processing Approaches in Game Design. Sensors 2023, 23, 3457.

Abstract

In recent decades, the Variational AutoEncoder (VAE) model has shown good potential and capabilities in image generation and dimensionality reduction. The combination of VAE and various machine learning frameworks has also worked effectively in different daily life applications, however its possibility and effectiveness in modern game design has seldom been explored nor assessed. The use of its feature extractor for data clustering was minimally discussed in literature neither. This paper first attempts to explore different mathematical properties of the VAE model, in particular, the theoretical framework of the encoding and decoding processes, the possible achievable lower bound and loss functions of different applications; then applies the established VAE model into generating new game levels within two well-known game settings; as well as validating the effectiveness of its data clustering mechanism with the aid of the Modified National Institute of Standards and Technology (MNIST) database. Respective statistical metrics and assessments were also utilized for evaluating the performance of the proposed VAE model in aforementioned case studies. Based on the statistical and spatial results, several potential drawbacks and future enhancement of the established model were outlined, with the aim of maximizing the strengths and advantages of VAE for future game design tasks and relevant industrial missions.

Keywords

Game Design; Variational AutoEncoder (VAE); Image and Video Generation; Bayesian Algorithm; Loss Function; Data Clustering; Data and Image Analytics; MNIST database; Generator and Discriminator

Subject

Computer Science and Mathematics, Data Structures, Algorithms and Complexity

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