Predicting the Torso Direction from HMD Movements for Walk-in-Place Navigation through Deep Learning

Title
Predicting the Torso Direction from HMD Movements for Walk-in-Place Navigation through Deep Learning
Authors
황재인이주영안드레아스 패스토김정현
Keywords
virtual reality; navigation; walk in place; deep learning
Issue Date
2019-11
Publisher
ACM Symposium on Virtual Reality Software and Technology
Abstract
In this paper, we propose to use the deep learning technique to estimate and predict the torso direction from the head movements alone. The prediction allows to implement the walk-in-place navigation interface without additional sensing of the torso direction, and thereby improves the convenience and usability. We created a small dataset and tested our idea by training an LSTM model and obtained a 3-class prediction rate of about 90%, a figure higher than using other conventional machine learning techniques. While preliminary, the results show the possible inter-dependence between the viewing and torso directions, and with richer dataset and more parameters, a more accurate level of prediction seems possible.
URI
http://pubs.kist.re.kr/handle/201004/70680
ISSN
-
Appears in Collections:
KIST Publication > Conference Paper
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