Gaze Prediction using Machine Learning for Dynamic Stereo Manipulation in Games
- Submitting institution
-
University of Durham
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 115992
- Type
- E - Conference contribution
- DOI
-
10.1109/vr.2016.7504694
- Title of conference / published proceedings
- IEEE VR 2016 IEEE
- First page
- 113
- Volume
- -
- Issue
- -
- ISSN
- 23755334
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2016
- URL
-
https://doi.org/10.1109/vr.2016.7504694
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
A - Innovative Computing
- Citation count
- 13
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This first paper to have used machine learning for stereo grading in the demanding context of real-time, heavily task-oriented applications such as games, was praised by both a 30-year retrospective survey on gaze-based interaction (10.1016/j.cag.2018.04.002) and a SIGGRAPH course on eye-based interaction on graphical systems (10.1145/3388769.3407492).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -