Predicting the Torso Direction from HMD Movements for Walk-in-Place Navigation through Deep Learning
- Predicting the Torso Direction from HMD Movements for Walk-in-Place Navigation through Deep Learning
- 황재인; 이주영; 안드레아스 패스토; 김정현
- virtual reality; navigation; walk in place; deep learning
- Issue Date
- ACM Symposium on Virtual Reality Software and Technology
- 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.
- Appears in Collections:
- KIST Publication > Conference Paper
- Files in This Item:
There are no files associated with this item.
- RIS (EndNote)
- XLS (Excel)
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.