Real-Time Translation of Upper-Body Gestures to Virtual Avatars in Dissimilar Telepresence Environments

In mixed reality (MR) avatar-mediated telepresence, avatar movement must be adjusted to convey the user's intent in a dissimilar space. This paper presents a novel neural network-based framework designed for translating upper-body gestures, which adjusts virtual avatar movements in dissimilar environments to accurately reflect the user's intended gestures in real-time. Our framework translates a wide range of upper-body gestures, including eye gaze, deictic gestures, free-form gestures, and the transitions between them. A key feature of our framework is its ability to generate natural upper-body gestures for users of different sizes, irrespective of handedness and eye dominance, even though the training is based on data from a single person. Unlike previous methods that require paired motion between users and avatars for training, our framework uses an unpaired approach, significantly reducing training time and allowing for generating a wider variety of motion types. These advantages were made possible by designing two separate networks: the Motion Progression Network, which interprets sparse tracking signals from the user to determine motion progression, and the Upper-body Gesture Network, which autoregressively generates the avatar's pose based on these progressions. We demonstrate the effectiveness of our framework through quantitative comparisons with state-of-the-art methods, qualitative animation results, and a user evaluation in MR telepresence scenarios.