Translation gain is a key Redirected Walking (RDW) technique in Virtual Reality (VR) that enables users to navigate virtual environments (VEs) larger than the available physical space. The technique was originally developed to scale users' walking distance in the VE and is typically applied continuously, regardless of the user's motion state. We introduce Gait-Synced Translation Gain (GSTG), a novel approach that adapts translation gain by synchronizing it with the user's gait cycle. GSTG leverages the single-limb support phase of walking-when users are less stable and thus less sensitive to external disturbances-to apply higher levels of gain. This approach allows greater manipulation while preserving natural walking sensations and avoiding additional cybersickness. A user study comparing GSTG with continuous translation gain demonstrates significant improvements in perceived naturalness and comfort. Our results highlight the potential of gait-synchronized gain to enhance immersion, offering new possibilities for more realistic and comfortable VR locomotion.

We present a novel 360° panoramic video conferencing system that dynamically synchronizes virtual backgrounds with real-time camera motion, addressing the limitations of static backgrounds in conventional systems. By integrating robust human segmentation, monocular visual odometry (VO), and virtual environment rendering, our method achieves seamless alignment between foreground participants and immersive 3D virtual scenes. Unlike prior approaches that suffer from foreground-background desynchronization during camera rotations or user movements, our framework estimates camera rotation in 3-DoF using a hybrid pipeline combining feature-based patch tracking and pose smoothing, while ignoring translation artifacts to maintain stability. This work bridges the gap between computational efficiency and MR-driven telepresence, offering a practical solution for next-generation virtual collaboration.

Age significantly influences human motor patterns, yet existing virtual reality (VR) systems lack dynamic modelling of these variations. This paper introduces AgeStyle, a versatile framework that integrates age-guided style selection with motion style transfer to convert user-uploaded videos into interactive 3D motion models. Utilizing 2D joint detection and 3D pose estimation, AgeStyle constructs motion representations enhanced by a CLIP-driven Cross-Attention module, capturing the distinct traits of different age groups—child flexibility, adult efficiency, and elderly stability. Our system enables real-time switching between motion styles and perspectives through voice commands, offering an immersive exploration of age-related movements. Quantitative experiments on the XIA dataset demonstrate AgeStyle’s competitive performance in both content preservation and style consistency, achieving average CC and SC++ scores of 7.4 and 14.8, respectively. AgeStyle represents a meaningful advancement in VR character design, with broad potential applications in education, healthcare, rehabilitation, and interactive entertainment. For the demo, please refer to https://youtu.be/eo7Shy0Ukps . The source code of AgeStyle is available at https://github.com/codeozzz/ageStyle .

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.

The rise of Virtual Reality (VR) sports has been driven by evolving work patterns, limited access to physical exercise spaces, and a growing focus on health and wellness. Beyond the enjoyment and convenience offered by VR exercise, we aim to enhance users' athletic performance through this medium. Prior research on the Proteus Effect in VR sports has demonstrated the potential of customized, stronger, or younger avatars to improve exercise outcomes. However, such effects are limited for users who already perceive themselves as strong and youthful. To address this, we propose a more general approach: representing perceived avatar capability through facial expressions and vocal cues to influence user performance. In this study, we examined how manipulating the perceived capability level of virtual avatars (high, neutral, low) during dumbbell lateral raises in a VR gym affected exercise performance. Results indicated that avatars with high-capability expressions significantly enhanced performance and motivation compared to low-capability avatars. These findings underscore the promise of using facial and auditory cues to represent perceived capability, offering new directions for designing emotionally intelligent fitness applications.

As technology advances, user demand for immersive and authentic information presentation rises. Traditional 2D displays and interactions fail to meet modern standards, while virtual reality (VR) is gaining attention for its immersive experience. However, using a controller for VR movement can cause dizziness due to mismatched visual and vestibular cues, impacting the VR experience. This paper analyzes the main causes of VR-induced vertigo and develops improved handheld controller movement strategies. These strategies adjust the user’s pitch angle and field of view in real time or map the user’s real-world head acceleration to the virtual character. By intelligently adjusting the controller-to-VR display mapping, these methods reduce vertigo. In addition, this paper also verified the actual effects of these designs through a series of experiments, and conducted detailed data analysis on the degree of user vertigo. The experimental results showed that using a specific improved handheld controller movement design can significantly improve the user’s comfort in the VR environment, effectively reducing the occurrence of vertigo and discomfort.

Redirected Walking (RDW) is an important intermediary layer in virtual reality (VR) interaction systems. It addresses the issues of spatial restrictions in VR exploration by imperceptibly remapping the virtual environment’s movement to the physical environment, enhancing the user’s immersive experience. In VR, jumping is also a noteworthy motion besides walking. However, existing redirected walking (RDW) algorithms typically focus on reducing collisions between users and obstacles during walking but overlook the safety when users perform significant actions such as jumping. This oversight can pose serious risks to users during VR exploration, especially when there are physical obstacles or boundaries near the virtual locations that require user jumping. We propose SafeRDW, the first RDW algorithm that takes the user’s jumping safety into consideration. The proposed method considers both walking and jumping actions in the virtual environment, reducing physical resets and redirecting users to safer locations when a jump is required in the virtual space, ensuring user safety. Simulation experiments and user study results both show that our method not only reduces the number of resets, but also significantly ensures user safety when they reach the jumping points in the virtual scene.

Rotation gain is a subtle manipulation technique commonly employed in Redirected Walking (RDW) methods due to its superior capability to alter a user’s virtual trajectory. Previous studies have reported that the imperceptible ranges of rotation gains are influenced by various factors, resulting in different detection threshold values, which may alter RDW performance. In this study, we focus on the effects of scene visual characteristics on the rotation gain and rotation gain thresholds (RGTs), which have been less explored in this area. In our experiments, we focus on three visual characteristics: visual density, spatial size, and realism. Each characteristic is tested at two different levels, resulting in a design of eight distinct VR scenes. Through extensive statistical analysis, we find that spatial size may influence user perception of rotation gain in different virtual environments (VEs), though the effect appears to be small. No significant results of sensitivity differences were found for visual density and realism. We show that the short-term temporal effect is another predominant factor influencing user perception of rotation gain, even when users experience different visual stimuli in VEs, such as different scene visual characteristic settings in our study. This result indicates that users’ adaptation effects on rotation gain can occur in as short a time as overnight intervals, rather than over weeks.

Redirected walking (RDW) facilitates user navigation within expansive virtual spaces despite the constraints of limited physical spaces. It employs discrepancies between human visual-proprioceptive sensations, known as gains, to enable the remapping of virtual and physical environments. In this paper, we explore how to apply rotation gain while the user is walking. We propose to apply a rotation gain to let the user rotate by a different angle when reciprocating from a previous head rotation, to achieve the aim of steering the user to a desired direction. To apply the gains imperceptibly based on such a Bidirectional Rotation gain Difference (BiRD), we conduct both measurement and verification experiments on the detection thresholds of the rotation gain for reciprocating head rotations during walking. Unlike previous rotation gains which are measured when users are turning around in place (standing or sitting), BiRD is measured during users' walking. Our study offers a critical assessment of the acceptable range of rotational mapping differences for different rotational orientations across the user's walking experience, contributing to an effective tool for redirecting users in virtual environments.

Virtual Reality (VR) offers an immersive 3D digital environment, but enabling natural walking sensations without the constraints of physical space remains a technological challenge. Previous VR locomotion methods, including game controller, teleportation, treadmills, walking-in-place, and redirected walking (RDW), have made strides towards overcoming this challenge. However, these methods also face limitations such as possible unnaturalness, additional hardware requirements, or motion sickness risks. This paper introduces "Spatial Contraction (SC)", an innovative VR locomotion method inspired by the phenomenon of Lorentz contraction in Special Relativity. Similar to the Lorentz contraction, our SC contracts the virtual space along the user's velocity direction in response to velocity variation. The virtual space contracts more when the user's speed is high, whereas minimal or no contraction happens at low speeds. We provide a virtual space transformation method for spatial contraction and optimize the user experience in smoothness and stability. Through SC, VR users can effectively traverse a longer virtual distance with a shorter physical walking. Different from locomotion gains, the spatial contraction effect is observable by the user and aligns with their intentions, so there is no inconsistency between the user's proprioception and visual perception. SC is a general locomotion method that has no special requirements for VR scenes. The experimental results of our live user studies in various virtual scenarios demonstrate that SC has a significant effect in reducing both the number of resets and the physical walking distance users need to cover. Furthermore, experiments have also demonstrated that SC has the potential for integration with existing locomotion techniques such as RDW.

With the recent rise of Metaverse, online multiplayer VR applications are becoming increasingly prevalent worldwide. However, as multiple users are located in different physical environments, different reset frequencies and timings can lead to serious fairness issues for online collaborative/competitive VR applications. For the fairness of online VR apps/games, an ideal online RDW strategy must make the locomotion opportunities of different users equal, regardless of different physical environment layouts. The existing RDW methods lack the scheme to coordinate multiple users in different PEs, and thus have the issue of triggering too many resets for all the users under the locomotion fairness constraint. We propose a novel multi-user RDW method that is able to significantly reduce the overall reset number and give users a better immersive experience by providing a fair exploration. Our key idea is to first find out the "bottleneck" user that may cause all users to be reset and estimate the time to reset given the users' next targets, and then redirect all the users to favorable poses during that maximized bottleneck time to ensure the subsequent resets can be postponed as much as possible. More particularly, we develop methods to estimate the time of possibly encountering obstacles and the reachable area for a specific pose to enable the prediction of the next reset caused by any user. Our experiments and user study found that our method outperforms existing RDW methods in online VR applications.

With redirected walking (RDW) technology, people can explore large virtual worlds in smaller physical spaces. RDW controls the trajectory of the user's walking in the physical space through subtle adjustments, so as to minimize the collision between the user and the physical space. Previous predictive algorithms place constraints on the user's path according to the spatial layouts of the virtual environment and work well when applicable, while reactive algorithms are more general for scenarios involving free exploration or uncon-strained movements. However, even in relatively free environments, we can predict the user's walking to a certain extent by analyzing the user's historical walking data, which can help the decision-making of reactive algorithms. This paper proposes a novel RDW method that improves the effect of real-time unrestricted RDW by analyzing and utilizing the user's historical walking data. In this method, the physical space is discretized by considering the user's location and orientation in the physical space. Using the weighted directed graph obtained from the user's historical walking data, we dynamically update the scores of different reachable poses in the physical space during the user's walking. We rank the scores and choose the optimal target position and orientation to guide the user to the best pose. Since simulation experiments have been shown to be effective in many previous RDW studies, we also provide a method to simulate user walking trajectories and generate a dataset. Experiments show that our method outperforms multiple state-of-the-art methods in various environments of different sizes and spatial layouts.

Redirected walking (RDW) aims to reduce the collisions in the physical space for VR applications. However, most of the previous RDW methods do not consider future possibilities of collisions after imperceptibly redirecting users. In this paper, we combine the subtle RDW methods and reset strategy in our method design and propose a novel solution for RDW that can make better use of physical space and trigger fewer resets. The key idea of our method is to discretize the representation of possible user positions and orientations by a series of standard poses and rate them based on the possibilities of hitting obstacles of their reachable poses. A transfer path algorithm is proposed to measure the accessibility among standard poses and is used to support the calculation of the scores of standard poses. Using our method, the user can be redirected imperceptibly to the optimal pose with the best score among all the reachable poses from the user’s current pose during walking. Experiments demonstrate that our method outperforms state-of-the-art methods in various environment sizes and obstacle layouts.

Rapidly developing Redirected Walking (ROW) technologies have enabled VR applications to immerse users in large virtual environments (VE) while actually walking in relatively small physical environments (PE). When an unavoidable collision emerges in a PE, the ROW controller suspends the user's immersive experience and resets the user to a new direction in PE. Existing ROW methods mainly aim to reduce the number of resets. However, from the perspective of the user experience, when users are about to reach a point of interest (POI) in a VE, reset interruptions are more likely to have an impact on user experience. In this paper, we propose a new ROW method, aiming to keep resets occurring at a longer distance from the virtual target, as well as to reduce the number of resets. Simulation experiments and real user studies demonstrate that our method outperforms state-of-the-art ROW methods in the number of resets and dramatically increases the distance between the reset locations and the virtual targets.

In a conventional optical motion capture (MoCap) workflow, two processes are needed to turn captured raw marker sequences into correct skeletal animation sequences. Firstly, various tracking errors present in the markers must be fixed ( cleaning or refining ). Secondly, an agent skeletal mesh must be prepared for the actor/actress, and used to determine skeleton information from the markers ( re-targeting or solving ). The whole process, normally referred to as solving MoCap data, is extremely time-consuming, labor-intensive, and usually the most costly part of animation production. Hence, there is a great demand for automated tools in industry. In this work, we present MoCap-Solver, a production-ready neural solver for optical MoCap data. It can directly produce skeleton sequences and clean marker sequences from raw MoCap markers, without any tedious manual operations. To achieve this goal, our key idea is to make use of neural encoders concerning three key intrinsic components: the template skeleton, marker configuration and motion, and to learn to predict these latent vectors from imperfect marker sequences containing noise and errors. By decoding these components from latent vectors, sequences of clean markers and skeletons can be directly recovered. Moreover, we also provide a novel normalization strategy based on learning a pose-dependent marker reliability function, which greatly improves system robustness. Experimental results demonstrate that our algorithm consistently outperforms the state-of-the-art on both synthetic and real-world datasets.

Abstract Image‐to‐image translation has been widely studied. Since real‐world images can often be described by multiple attributes, it is useful to manipulate them at the same time. However, most methods focus on transforming between two domains, and when they chain multiple single attribute transform networks together, the results are affected by the order of chaining, and the performance drops with the out‐of‐domain issue for intermediate results. Existing multi‐domain transfer methods mostly manipulate multiple attributes by adding a list of attribute labels to the network feature, but they also suffer from interference of different attributes, and perform worse when multiple attributes are manipulated. We propose a novel approach to multi‐attribute image‐to‐image translation using several parallel latent transform networks, where multiple attributes are manipulated in parallel and simultaneously, which eliminates both issues. To avoid the interference of different attributes, we introduce a novel soft independence constraint for the changes caused by different attributes. Extensive experiments show that our method outperforms state‐of‐the‐art methods.

Compositing is one of the most important editing operations for images and videos. The process of improving the realism of composite results is often called harmonization. Previous approaches for harmonization mainly focus on images. In this paper, we take one step further to attack the problem of video harmonization. Specifically, we train a convolutional neural network in an adversarial way, exploiting a pixel-wise disharmony discriminator to achieve more realistic harmonized results and introducing a temporal loss to increase temporal consistency between consecutive harmonized frames. Thanks to the pixel-wise disharmony discriminator, we are also able to relieve the need of input foreground masks. Since existing video datasets which have ground-truth foreground masks and optical flows are not sufficiently large, we propose a simple yet efficient method to build up a synthetic dataset supporting supervised training of the proposed adversarial network. The experiments show that training on our synthetic dataset generalizes well to the real-world composite dataset. In addition, our method successfully incorporates temporal consistency during training and achieves more harmonious visual results than previous methods.

Abstract Video stabilization is necessary for many hand‐held shot videos. In the past decades, although various video stabilization methods were proposed based on the smoothing of 2D, 2.5D or 3D camera paths, hardly have there been any deep learning methods to solve this problem. Instead of explicitly estimating and smoothing the camera path, we present a novel online deep learning framework to learn the stabilization transformation for each unsteady frame, given historical steady frames. Our network is composed of a generative network with spatial transformer networks embedded in different layers, and generates a stable frame for the incoming unstable frame by computing an appropriate affine transformation. We also introduce an adversarial network to determine the stability of apiece of video. The network is trained directly using the pair of steady and unsteady videos. Experiments show that our method can produce similar results as traditional methods, moreover, it is capable of handling challenging unsteady video of low quality, where traditional methods fail, such as video with heavy noise or multiple exposures. Our method runs in real time, which is much faster than traditional methods.