We propose a multi-user Mixed Reality (MR) telepresence system that allows users to interact by seamlessly visualizing remote environments and avatars overlaid onto their local physical space. Building on prior shared-space approaches, our method first aligns overlapping rooms to maximize a shared space –a common area containing matched real and virtual objects where all users can interact. Uniquely, our system extends beyond this shared space by visualizing non-shared spaces, the remaining part of each room, allowing users to inhabit these distinct areas. To address the issue of overlap between non-shared spaces, we dynamically adjust their visibility based on user proximity, using a Voronoi diagram to prioritize subspaces closer to each user. Visualizing the surrounding space of each user conveys spatial context, helping others interpret their behavior within their environment. Visibility is updated in real time as users move, maintaining a coherent sense of spatial awareness. Through a user study, we demonstrate that our system enhances enjoyment, spatial understanding, and presence compared to shared-space-only approaches. Quantitative results further show that our dynamic visibility modulation improves both personal space preservation and space accessibility relative to static methods. Overall, our system provides users with a seamless, dynamically connected, and shared multi-room environment. We provide an system overview and demo video of our work in the supplementary material.

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.