Neural Network Based Motion Synthesis for Close Combat in Games

Abstract

In this thesis we present a comparison of different neural network based motion synthesis techniques applied to the animation of a sword-fighting character. We record a set of motions in our motion capture lab, documenting the gathering and processing of data. We compare two data-driven models, Encoder-Recurrent-Decoder and Phase-Functioned Neural Networks, by training them to our data set.