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
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Typ
Examensarbete för masterexamen
Program
Publicerad
2021
Författare
SEGERSTEDT, JOHN
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
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.
Beskrivning
Ämne/nyckelord
machine learning , phase-functioned neural network , locomotive character animation , Avalanche Studios Group , Apex , thesis