Advanced Motion Prediction for Virtual Reality Gaming: a CNN-Based Approach
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
A novel motion prediction model (MPM) for virtual reality (VR) video games was developed, consisting of a motion recognition model (MRM) and a next movement prediction model (NMPM), both using convolutional neural networks (CNNs). Motion capture was performed with HTC Vive Pro and Meta Quest 2. Two custom datasets were created to train the MRM and NMPM. Our method achieved a top-1 accuracy of 77% and a top-2 accuracy of 90%, even with motion data sequences sharing similar initial stages but diverging in subsequent movements.
Description
CCS Concepts: Computing methodologies → Virtual reality; Neural networks; Hardware → Sensor devices and platforms
@inproceedings{10.2312:egve.20241379,
booktitle = {ICAT-EGVE 2024 - International Conference on Artificial Reality and Telexistence and Eurographics Symposium on Virtual Environments - Posters and Demos},
editor = {Tanabe, Takeshi and Yem, Vibol},
title = {{Advanced Motion Prediction for Virtual Reality Gaming: a CNN-Based Approach}},
author = {Jegierski, Hubert and Jegierski, Maciej and Igras-Cybulska, Magdalena and Węgrzyn, Paweł and Łapczyński, Adrian and Babiuch, Paweł and Płaza, Mirosław and Pięta, Paweł and Łukawski, Grzegorz and Deniziak, Stanisław and Opałka, Jacek and Jasiński, Artur},
year = {2024},
publisher = {The Eurographics Association},
ISSN = {1727-530X},
ISBN = {978-3-03868-246-2},
DOI = {10.2312/egve.20241379}
}