In social virtual reality (VR), maintaining appropriate interpersonal distance is essential for user comfort and privacy. However, most existing locomotion methods provide limited support for respecting personal space, leaving users vulnerable to unintentional or socially inappropriate intrusions. To address this issue, we propose potential field-guided teleportation, a proactive locomotion framework consisting of two method implementations that dynamically adjust teleportation targets based on real-time interpersonal proximity, preventing entry into others' personal spaces without explicit user intervention. We evaluate our technique through two user studies: a preliminary study exploring energy-based constraint parameters, followed by a comparative study against conventional and negotiated teleportation methods. Experiments were conducted in socially interactive VR scenarios populated with simulated users exhibiting human-like behaviors. Results demonstrate that our methods reduce perceived social anxiety while maintaining locomotion efficiency and usability. This work presents a socially-aware locomotion strategy that balances personal space protection with effective and socially appropriate movement in shared virtual environments.

Shared virtual environments are becoming essential platforms for collaborative interaction and immersive entertainment, enabling users to be co-located and engage in activities together. The presence of surrounding virtual humans forms an environmental crowd, serving as a component of ambient stimuli in these environments. However, it remains unclear how crowd size affects users under different cognitive and motor demands. This study investigates the influence of crowd size on user performance, experience and social presence across three fundamental VR tasks: Spatial Locomotion, Memory Search, and Motor Coordination. We conducted a controlled within-subjects experiment, manipulating each task's crowd size at Small, Medium, and Large levels. Our results show that crowd size significantly impacts user performance, experience, and social presence, but these effects are task-dependent. While Medium size can enhance performance, Large size in cognitively demanding tasks may induce attentional blindness and diminish sensitivity to social cues. Task functionality further shapes how users perceive and respond to virtual crowds. Additionally, users' preferences for crowd size varied across different tasks, and most participants expressed a desire for control over the number of visible avatars. These findings provide novel insights into human crowd perception mechanisms, revealing cross-task perceptual variations that pave the way for further exploring crowd perception in shared virtual environments.

Geographically dispersed users often rely on virtual avatars as intermediaries to facilitate interactive communication and collaboration. However, existing methods for augmented reality (AR) telepresence applications exhibit limitations, including restricted movement within confined sub-areas, lack of smooth transitions, and the necessity for manually establishing object mapping between dissimilar environments. We present a novel interactive AR framework for virtual avatar locomotion adaption while preserving semantic coherence across dissimilar indoor scenes. Initially, we conduct a preliminary user study to identify key attributes influencing preferred avatar movement. These attributes are quantified as features, and a dataset of user annotations on avatar movements is created. Based on the user interaction and scene configurations, we employ a deep reinforcement learning neural network to guide the avatar to the ideal position while maximizing semantic coherence. We validate our proposed framework through simulations and user studies by implementing an AR-based 3D telepresence prototype, demonstrating the efficacy of our framework in conveying user intentions across dissimilar environments, enabling natural and immersive 3D telepresence interactions.

The locomotion and interaction of multi-user groups are critical components of social virtual reality (VR), where users collaboratively navigate shared spaces defined by their relationships. As metaverse and social VR platforms evolve, safeguarding group spatial integrity becomes paramount. While teleportation-widely adopted for efficient navigation-enhances communication, it risks unintended intrusion into group spaces, compromising privacy and security. This paper presents two novel negotiated user-to-group teleportation techniques, paired with dynamic zone computation methods, to address these challenges. Our approach enables guest users to join groups through spatially aware teleportation, mediated by real-time negotiation. The negotiation interaction process is designed to facilitate users to negotiate teleportation locations efficiently and smoothly. To validate our techniques, we conducted a user study with 36 participants in a VR art museum environment, where they performed collaborative social-tour tasks. The findings demonstrate that our techniques significantly enhance group privacy protection, effectively support user-to-group negotiation of teleportation joining requirements, and alleviate anxiety associated with unwanted proximity within social VR groups.