PUB - Publikationen an der Universität Bielefeld

Extended Reality (XR) has a potential to allow social interaction for people that are distant from one another, in educational, clinical or co-working applications, as well as for scientific studies. However, a full-blown embodied social presence and interaction via avatars in XR requires motion tracking hardware that many users do not have. At the same time, modern machine learning approaches enable the synthesis of natural and life-like nonverbal behavior, but only in offline settings and with considerable lag. We evaluate the applicability of current gesture generation systems for online interaction in social XR. We define a set of requirements for real-time-capable gesture generation and propose an approach to employ a state-of-the-art model in a real-time XR interaction pipeline. To test the model under conditions of online interaction, we divide an input audio stream into chunks of different lengths and stitch the resulting gesture animations together to form continuous motion. We evaluate the quality of the resulting multimodal avatar behavior in a user study. Our results show a significant trade-off between real-time generation capabilities and gesture quality. Suggestions for future improvement to retain model performance during online interaction in Social XR are made. A project page with videos of the generated gestures is available at https://nkrome.github.io/CAGE.html.