Generating Character Animation for the Apex Game Engine using Neural Networks - Implementing immersive character animation in an industryproven game engine by applying machine learning techniques

Abstract

The art of machine learning, here using neural networks to map pairs of inputs to outputs, has been greatly expanded upon recently. It has been shown to be able to produce generalizable solutions within multiple different fields of research and has been deployed in real-world commercial products. One of these research areas in which regular scientific achievements are made is game development, and specifically character animation. However, compared to other fields, even though there has been much work on applying machine learning techniques to character animation, few efforts have been made to applying them in real-world game engines. This thesis project aimed to research the applicability of one such piece of previous work, within the proprietary Apex game engine. The final results included an in-engine solution, producing character animation purely from a predicative phase-functioned neural network. Additionally, several different network configurations were evaluated to compare the impact of using, for example, a deeper network or a network that had trained for a longer period of time, in an attempt to investigate potential improvements to the original model. These alterations were shown to have negligible positive impacts on the final results. Also, an additional network configuration was used to investigate the applicability of this approach on an industry-used skeleton, producing promising but imperfect results.