This work introduces an interaction technique to determine the user’s non-verbal deixis in Virtual Reality (VR) applications. We tailored it for multimodal speech & gesture interfaces (MMIs). Here, non-verbal deixis is often determined by the use of ray-casting due to its simplicity and intuitiveness. However, ray-casting’s rigidness and dichotomous nature pose limitations concerning the MMI’s flexibility and efficiency. In contrast, our technique considers a more comprehensive set of directional cues to determine non-verbal deixis and provides probabilistic output to tackle these limitations. We present a machine-learning-based reference implementation of our technique in VR and the results of a first performance benchmark. Future work includes an in-depth user study evaluating our technique’s user experience in an MMI.
%0 Conference Paper
%1 9974498
%A Stingl, René
%A Zimmerer, Chris
%A Fischbach, Martin
%A Latoschik, Marc Erich
%B 2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)
%D 2022
%K mmi myown
%P 671-673
%R 10.1109/ISMAR-Adjunct57072.2022.00139
%T Are You Referring to Me? - Giving Virtual Objects Awareness
%U https://downloads.hci.informatik.uni-wuerzburg.de/2022-ismar-natural-pointing-preprint.pdf
%X This work introduces an interaction technique to determine the user’s non-verbal deixis in Virtual Reality (VR) applications. We tailored it for multimodal speech & gesture interfaces (MMIs). Here, non-verbal deixis is often determined by the use of ray-casting due to its simplicity and intuitiveness. However, ray-casting’s rigidness and dichotomous nature pose limitations concerning the MMI’s flexibility and efficiency. In contrast, our technique considers a more comprehensive set of directional cues to determine non-verbal deixis and provides probabilistic output to tackle these limitations. We present a machine-learning-based reference implementation of our technique in VR and the results of a first performance benchmark. Future work includes an in-depth user study evaluating our technique’s user experience in an MMI.
@inproceedings{9974498,
abstract = {This work introduces an interaction technique to determine the user’s non-verbal deixis in Virtual Reality (VR) applications. We tailored it for multimodal speech & gesture interfaces (MMIs). Here, non-verbal deixis is often determined by the use of ray-casting due to its simplicity and intuitiveness. However, ray-casting’s rigidness and dichotomous nature pose limitations concerning the MMI’s flexibility and efficiency. In contrast, our technique considers a more comprehensive set of directional cues to determine non-verbal deixis and provides probabilistic output to tackle these limitations. We present a machine-learning-based reference implementation of our technique in VR and the results of a first performance benchmark. Future work includes an in-depth user study evaluating our technique’s user experience in an MMI.},
added-at = {2023-04-13T14:04:12.000+0200},
author = {Stingl, René and Zimmerer, Chris and Fischbach, Martin and Latoschik, Marc Erich},
biburl = {https://www.bibsonomy.org/bibtex/2be91cf523ae8daf60dc945e248b4c659/hci-uwb},
booktitle = {2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)},
doi = {10.1109/ISMAR-Adjunct57072.2022.00139},
interhash = {30b3703ead7b6d6e7442e4778eda9467},
intrahash = {be91cf523ae8daf60dc945e248b4c659},
keywords = {mmi myown},
pages = {671-673},
timestamp = {2024-05-06T17:22:37.000+0200},
title = {Are You Referring to Me? - Giving Virtual Objects Awareness},
url = {https://downloads.hci.informatik.uni-wuerzburg.de/2022-ismar-natural-pointing-preprint.pdf},
year = 2022
}