Redirected walking (RDW) subtly adjusts the user's visual perspective on head-mounted displays during natural walking to reduce forced resets, thus enlarging the size of the virtual environment that can be explored beyond that of the physical environment. Alignment-based RDW controllers aim to minimize spatial discrepancies by optimizing the alignment between the user's physical and virtual environments. We introduce a novel alignment-based method that dynamically calculates mapping functions between physical and virtual geometries to enhance the algorithm's awareness of the RDW environments. To achieve this, we first construct an abstract model defining a mapping function between physical and virtual geometries and establish feasibility constraints in differential form. We then concretize this mapping, optimize it, and develop a practical implementation for dynamic geometric mapping in RDW. Our approach distinguishes itself by determining dense spatial mappings around the user, rather than aligning environments according to limited metrics. Through extensive testing, our algorithm has proven to markedly decrease reset incidents in natural walking, surpassing existing RDW controllers. The introduction of dynamic geometric mapping provides a fresh perspective, contributing significant insights and advancing the field.

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.

The growing adoption of social virtual reality (VR) platforms underscores the importance of safeguarding personal VR space to maintain user privacy and security. Teleportation, a prevalent instantaneous locomotion method in VR, facilitates user engagement but can also inadvertently intrude upon personal VR space, thereby raising privacy concerns. This paper introduces three innovative negotiated teleportation techniques designed to secure user-to-user teleportation and protect personal space privacy, all under a unified small-group development framework. We have designed and evaluated three types of negotiated teleportation techniques: Sector technique for directional control, Distance technique for minimum social distance control, and Area technique for defining circular permissible teleportation areas. These techniques foster a collaborative approach to selecting teleportation points that respect personal space. To evaluate the efficacy of these techniques, we conducted a user study with 20 participants who performed social tasks within a virtual campus environment. The findings demonstrate that our techniques significantly enhance privacy protection and alleviate anxiety associated with unwanted proximity in social VR.

Traveling between scenes has become a major requirement for navigation in numerous virtual reality (VR) social platforms and game applications, allowing users to efficiently explore multiple virtual environments (VEs). To facilitate scene transition, prevalent techniques such as instant teleportation and virtual portals have been extensively adopted. However, these techniques exhibit limitations when there is a need for frequent travel between separate VEs, particularly within indoor environments, resulting in low efficiency. In this article, we first analyze the design rationale for a novel navigation method supporting efficient travel between virtual indoor scenes. Based on the analysis, we introduce the SceneFusion technique that fuses separate virtual rooms into an integrated environment. SceneFusion enables users to perceive rich visual information from both rooms simultaneously, achieving high visual continuity and spatial awareness. While existing teleportation techniques passively transport users, SceneFusion allows users to actively access the fused environment using short-range locomotion techniques. User experiments confirmed that SceneFusion outperforms instant teleportation and virtual portal techniques in terms of efficiency, workload, and preference for both single-user exploration and multi-user collaboration tasks in separate VEs. Thus, SceneFusion presents an effective solution for seamless traveling between virtual indoor scenes.

Time-anchored on-screen comments, as known as bullet comments, are a popular feature for online video streaming. Bullet comments reflect audiences’ feelings and opinions at specific video timings, which have been shown to be beneficial to video content understanding and social connection level. In this paper, we for the first time investigate the problem of bullet comment display and insertion for 360° video via head-mounted display and controller. We design four bullet comment display methods and evaluate their effects on 360° video experiences. We further propose two controller-based methods for bullet comment insertion. Combining the display and insertion methods, the user can experience 360° videos with bullet comments, and interactively post new ones by selecting among existing comments. User study results revealed how the factors of display and insertion methods affect 360° video experience. With the experiment findings, we also discuss useful design insights for 360° video bullet comments.

Various redirected walking (RDW) techniques have been proposed, which unwittingly manipulate the mapping from the user’s physical locomotion to motions of the virtual camera. Thereby, RDW techniques guide users on physical paths with the goal to keep them inside a limited tracking area, whereas users perceive the illusion of being able to walk infinitely in the virtual environment. However, the inconsistency between the user’s virtual and physical location hinders passive haptic feedback when the user interacts with virtual objects, which are represented by physical props in the real environment.In this paper, we present a novel reinforcement learning approach towards RDW with passive haptics. With a novel dense reward function, our method learns to jointly consider physical boundary avoidance and consistency of user-object positioning between virtual and physical spaces. The weights of reward and penalty terms in the reward function are dynamically adjusted to adaptively balance term impacts during the walking process. Experimental results demonstrate the advantages of our technique in comparison to previous approaches. Finally, the code of our technique is provided as an open-source solution.

Redirected walking (RDW) is a locomotion technique that guides users on virtual paths, which might vary from the paths they physically walk in the real world. Thereby, RDW enables users to explore a virtual space that is larger than the physical counterpart with near-natural walking experiences. Several approaches have been proposed and developed; each using individual platforms and evaluated on a custom dataset, making it challenging to compare between methods. However, there are seldom public toolkits and recognized benchmarks in this field. In this paper, we introduce OpenRDW, an open-source library and benchmark for developing, deploying and evaluating a variety of methods for walking path redirection. The OpenRDW library provides application program interfaces to access the attributes of scenes, to customize the RDW controllers, to simulate and visualize the navigation process, to export multiple formats of the results, and to evaluate RDW techniques. It also supports the deployment of multi-user real walking, as well as reinforcement learning-based models exported from TensorFlow or PyTorch. The OpenRDW benchmark includes multiple testing conditions, such as walking in size varied tracking spaces or shape varied tracking spaces with obstacles, multiple user walking, etc. On the other hand, procedurally generated paths and walking paths collected from user experiments are provided for a comprehensive evaluation. It also contains several classic and state-of-the-art RDW techniques, which include the above mentioned functionalities.

Redirected jumping (RDJ) is a locomotion technique that allows users to explore a virtual space that is larger than the available physical space by imperceptibly manipulating users' virtual viewpoints according to different gains. In previous redirected jumping work, different types of gains were imposed separately, without considering the possible interaction effects of horizontal and vertical gains on the jumping distance perception. To figure out how humans perceive distance manipulation when more than one gain is used, in this paper, we explored joint horizontal and vertical gains that manipulate horizontal and vertical distances at the same time during two-legged takeoff jumping in the virtual space. We estimated and analyzed horizontal and vertical detection thresholds by conducting a user study, fitting the data to two-dimensional psychometric functions, and visualizing the fitted 3D plots. We provided quantitative insights into the effects of joint gains on detection thresholds, where the imperceptible range for one gain can be affected by the variation of the other gain. Finally, we designed redirected jumping-based games as applications with joint horizontal and vertical gains and demonstrated the effectiveness of the redirected jumping technique.

Redirected jumping (RDJ) allows users to explore virtual environments (VEs) naturally by scaling a small real-world jump to a larger virtual jump with virtual camera motion manipulation, thereby addressing the problem of limited physical space in VR applications. Previous RDJ studies have mainly focused on detection threshold estimation. However, the effect VE or selfrepresentation (SR) has on the perception or performance of RDJs remains unclear. In this paper, we report experiments to measure the perception (detection thresholds for gains, presence, embodiment, intrinsic motivation, and cybersickness) and physical performance (heart rate intensity, preparation time, and actual jumping distance) of redirected forward jumping under six different combinations of VE (low and high visual richness) and SRs (invisible, shoes, and human-like). Our results indicated that the detection threshold ranges for horizontal translation gains were significantly smaller in the VE with high rather than low visual richness. When different SRs were applied, our results did not suggest significant differences in detection thresholds, but it did report longer actual jumping distances in the invisible body case compared with the other two SRs. In the high visual richness VE, the preparation time for jumping with a human-like avatar was significantly longer than that with other SRs. Finally, some correlations were found between perception and physical performance measures. All these findings suggest that both VE and SRs influence users' perception and performance in RDJ and must be considered when designing locomotion techniques.

Despite the increasing number of head-mounted displays, many 360° VR videos are still being viewed by users on existing 2D displays. To this end, a subset of the 360° video content is often shown inside a manually or semi-automatically selected normal-field-of-view (NFoV) window. However, during the playback, simply watching an NFoV video can easily miss concurrent off-screen content. We present Transitioning360, a tool for 360° video navigation and playback on 2D displays by transitioning between multiple NFoV views that track potentially interesting targets or events. Our method computes virtual NFoV camera paths considering content awareness and diversity in an offline preprocess. During playback, the user can watch any NFoV view corresponding to a precomputed camera path. Moreover, our interface shows other candidate views, providing a sense of concurrent events. At any time, the user can transition to other candidate views for fast navigation and exploration. Experimental results including a user study demonstrate that the viewing experience using our method is more enjoyable and convenient than previous methods.

Virtual reality (VR) offers an artificial, computer generated simulation of a real life environment. It originated in the 1960s and has evolved to provide increasing immersion, interactivity, imagination, and intelligence. Because deep learning systems are able to represent and compose information at various levels in a deep hierarchical fashion, they can build very powerful models which leverage large quantities of visual media data. Intelligence of VR methods and applications has been significantly boosted by the recent developments in deep learning techniques. VR content creation and exploration relates to image and video analysis, synthesis and editing, so deep learning methods such as fully convolutional networks and general adversarial networks are widely employed, designed specifically to handle panoramic images and video and virtual 3D scenes. This article surveys recent research that uses such deep learning methods for VR content creation and exploration. It considers the problems involved, and discusses possible future directions in this active and emerging research area.