User Position Prediction in 6-DoF Mixed Reality Applications Using Recurrent Neural Networks
This thesis is focusing on designing and evaluation of the approach for the prediction of human head position in a 6-dimensional degree of freedom (6-DoF) of Extended Reality (XR) applications for a given look-ahead time (LAT) in order to reduce the Motion-to-Photon (M2P) latency of the network and computational delays. At the beginning of the work the existing head motion prediction methods were analysed, and their similarities differences will be taken into account when a proposed Recurrent Neural Network-based predictor will be developed. Main goal is the systematic analysis of the potential of recurrent neural networks for head motion prediction. The proposed approach was evaluated on a real head motion dataset collected from Microsoft HoloLens. Based on a discussion of the obtained results, suggestions for future work are provided.