Teleportation is a widely adopted locomotion technique in virtual reality (VR), with reorientation commonly integrated into commercial systems to reduce physical turning and improve navigational control. However, research on reorientation-enabled teleportation has received limited attention and still lacks clear design guidance, resulting in longer interaction times, greater complexity, and higher mental workload. To bridge this gap, we revisit reorientation-enabled teleportation through bare-hand interaction, a natural and device-free modality that is now increasingly supported and widely adopted in mainstream VR systems. Accordingly, four gesture-based techniques, Concurrent Wrist Rotation (C-WR), Decoupled Wrist Rotation (D-WR), Joystick Wrist Rotation (J-WR), and Direction Drawing (DD), were introduced and evaluated in a controlled user study with 24 participants. Results show that C-WR was fastest but error-prone, D-WR improved accuracy at the cost of time, J-WR supported precise orientation but was demanding and slow, while DD offered balanced performance and was most preferred overall. From these findings, four design implications were distilled, emphasizing efficiency, intuitive mappings, and support for diverse user preferences. Finally, two example extensions illustrate how these techniques may extend to broader VR applications.

High latency is often introduced due to limited computational capabilities and high hardware demands. It has proven to significantly impair user performance in target selection, a fundamental interaction task. Existing research has established that latency negatively impacts selection times and success rates in 2D interactive systems; however, the underlying behavioral mechanisms remain unclear. This article investigates the effects of latency on selection times, success rates, and endpoint distributions in Virtual Reality (VR) with controller-based raycasting and bare-hand direct touch-the two most common selection methods. Our results from a user study (N = 31) revealed distinct patterns between the two methods, leading to two novel mathematical models that account for latency, target width, and movement amplitude. These two models were validated via a new dataset collected from a second user study (N = 16) and were demonstrated to outperform the existing models. Our findings provide actionable recommendations to mitigate the negative impacts of latency and improve user experience in VR interface design.

Cursors and how they are presented significantly influence user experience in both VR and non-VR environments by shaping how users interact with and perceive interfaces. In traditional interfaces, cursors serve as a fundamental component for translating human movement into digital interactions, enhancing interaction accuracy, efficiency, and experience. The design and visibility of cursors can affect users' ability to locate interactive elements and understand system feedback. In VR, cursor manipulation is more complex than in non-VR environments, as it can be controlled through hand, head, and gaze movements. With the arrival of the Apple Vision Pro, the use of gaze-controlled non-visible cursors has gained some prominence. However, there has been limited exploration of the effect of this type of cursor. This work presents a comprehensive study of the effects of cursor visibility (visible versus invisible) in gaze-based interactions within VR environments. Through two user studies, we investigate how cursor visibility impacts user performance and experience across different confirmation mechanisms and tasks. The first study focuses on selection tasks, examining the influence of target width, movement amplitude, and three common confirmation methods (air tap, blinking, and dwell). The second study explores pursuit tasks, analyzing cursor effects under varying movement speeds. Our findings reveal that cursor visibility significantly affects both objective performance metrics and subjective user preferences, but these effects vary depending on the confirmation mechanism used and task type. We propose eight design implications based on our empirical results to guide the future development of gaze-based interfaces in VR. These insights highlight the importance of tailoring cursor metaphors to specific interaction tasks and provide practical guidance for researchers and developers in optimizing VR user interfaces.

Weight perception is essential for delivering compelling and realistic object interaction in VR systems. While existing pseudo-haptic techniques have enabled users to perceive virtual object weight without physical actuation, their application has primarily been limited to near-field, direct hand interaction. As VR systems continue to advance in fidelity and versatility, interactions with objects beyond arm's reach are becoming increasingly common. However, how weight perception can be introduced into such remote interactions, and whether it can enhance user immersion and experience, remains underexplored. To bridge this gap, we present RayFlex, a pseudo-haptic technique that conveys object weight in raycasting-based interaction through visual displacement and dynamic ray deformation. The technique was evaluated in two user studies, which examined its effectiveness in supporting weight discrimination and its impact on user experience across different interaction contexts. Results indicate that RayFlex leads to significant improvements in perceived realism, presence, and satisfaction, while maintaining usability. From the results, we derived two implications and an application guideline that can help design future VR systems.

Target selection is a fundamental interaction task in virtual reality (VR) systems, particularly for older adults who face unique challenges due to age-related declines in motor and cognitive abilities. While controller-based raycasting is widely used for its accuracy and efficiency, the design of selection feedback remains an open question, particularly in enhancing usability and accessibility for aging populations. In this study, we propose seven feedback techniques, including three uni-modal (visual, audio, haptic) and four multimodal (visual-audio, visual-haptic, audio-haptic, visual-audio-haptic) approaches. To evaluate these techniques, we conducted two user studies focusing on selection tasks in controlled and realistic scenarios. Our results indicate that visual-based feedback, particularly expansion techniques, significantly improves selection accuracy and user experience. Moreover, multimodal feedback does not always yield better performance; rather, a combination of visual and haptic feedback provides the most effective balance between usability and cognitive load. Based on our findings, we derive six design implications to guide the development of VR selection feedback tailored to older adults. This work contributes to the understanding of optimal selection feedback mechanisms, promoting more inclusive and accessible VR interactions for aging users.

This paper explores finger pressure as a continuous implicit input modality to enhance interaction precision in virtual reality (VR). While motion controllers are widely adopted, their limitations in delicate operations remain a critical challenge. We investigate whether finger pressure signals from conventional VR controllers could offer advantages over traditional kinematic metrics for precision interaction.Through empirical studies, we demonstrate a robust relationship between pressure dynamics and task precision requirements, leading to a lightweight sigmoid-based model that leverages detected pressure to infer desired control granularity. In a comparative evaluation of video-scrubbing tasks, our adaptive method outperforms static sensitivity baselines in both task performance and subjective preference, without elevating cognitive load. Further validation via a VR sketching application demonstrates that our technique maintains task performance while reducing mental demand compared to manual control. Our findings reveal the untapped potential of pressure-based input to bridge coarse and fine-grained VR interactions, offering a path toward more versatile and intuitive input systems.

Proximity-based feedback provides users with real-time guidance as they approach an interaction goal. This type of feedback is particularly useful for tasks that require guidance during the interaction process, such as selecting moving targets. This work explores proximity-based feedback types and modalities to improve the selection of moving targets in VR by leveraging three feedback types that combine visual, auditory, and haptic modalities. We evaluated the performance of these mechanisms through two user studies, analyzing both objective data (e.g., selection time, error rate) and subjective data (e.g., user experience, preferences) to explore the characteristics of feedback types across different modalities and to examine the roles of various modalities within multimodal combinations. Our findings suggest optimal selection mechanisms for developers and should be tailored to different goals: achieving user precision, enabling quick movement to a target, considering task duration, and enhancing entertainment value. We also discuss applications that correspond to these different perspectives.

Target selection is one of the most fundamental tasks in VR interaction systems. Prediction heuristics can provide users with a smoother interaction experience in this process. Our work aims to predict the ray landing pose for hand-based raycasting selection in Virtual Reality (VR) using a Long Short-Term Memory (LSTM)-based neural network with time-series data input of speed and distance over time from three different pose channels: hand, Head-Mounted Display (HMD), and eye. We first conducted a study to collect motion data from these three input channels and analyzed these movement behaviors. Additionally, we evaluated which combination of input modalities yields the optimal result. A second study validates raycasting across a continuous range of distances, angles, and target sizes. On average, our technique’s predictions were within 4.6° of the true landing Pose when 50% of the way through the movement. We compared our LSTM neural network model to a kinematic information model and further validated its generalizability in two ways: by training the model on one user’s data and testing on other users (cross-user) and by training on a group of users and testing on entirely new users (unseen users). Compared to the baseline and a previous kinematic method, our model increased prediction accuracy by a factor of 3.5 and 1.9, re spectively, when 40% of the way through the movement.

Recent advances in eye-tracking technology have positioned gaze as an efficient and intuitive input method for Virtual Reality (VR), offering a natural and immersive user experience. As a result, gaze input is now leveraged for fundamental interaction tasks such as selection, manipulation, crossing, and steering. Although several studies have modeled user steering performance across various path characteristics and input methods, our understanding of gaze-based steering in VR remains limited. This gap persists because the unique qualities of eye movements-involving rapid, continuous motions-and the variability in eye-tracking make findings from other input modalities nontransferable to a gaze-based context, underscoring the need for a dedicated investigation into gaze-based steering behaviors and performance. To bridge this gap, we present two user studies to explore and model gaze-based steering. In the first one, user behavior data are collected across various path characteristics and eye-tracking conditions. Based on this data, we propose four refined models that extend the classic Steering Law to predict users' movement time in gaze-based steering tasks, explicitly incorporating the impact of tracking quality. The best-performing model achieves an adjusted R2 of 0.956, corresponding to a 16% improvement in movement time prediction. This model also yields a substantial reduction in AIC (from 1550 to 1132) and BIC (from 1555 to 1142), highlighting improved model quality and better balance between goodness of fit and model complexity. Finally, data from a second study with varied settings, such as a different eye-tracking sampling rate, illustrate the strong robustness and predictability of our models. Finally, we present scenarios and applications that demonstrate how our models can be used to design enhanced gaze-based interactions in VR systems.

Prior work has shown that frame rate significantly influences user behavior in fast-response tasks in 2D and 3D contexts. However, its impact on a steering task, which involves navigating an object along a path from the start to the end, remains relatively unexplored, especially in the context of virtual reality (VR). This task is considered a typical non-fast-response activity, as it does not demand rapid reactions within a limited time frame. Our work aims to understand and model users' steering behavior and predict movement time with different task complexities and frame rates in VR environments. We first conducted a user study to collect user behavior in a steering task with four factors: frame rate, path length, width, and radius of curvature. Based on the results, we then quantified the effects of frame rate and built two predictive models. Our models exhibited the best fit ($r^{2}> 0.957$r2>0.957) and over 17% improvement in prediction accuracy for movement time compared to existing models. Our models' robustness was further validated by applying them to predict steering performance with different VR tasks and frame rates. The two models keep the best predictability for both movement time and speed.

Piloting requires users to control and navigate the aircraft within a designated pathway, with a controller that utilizes two joysticks to control the aircraft. This task is representative of various daily and gaming scenarios, such as controlling the aircraft to capture the photo or navigating an object in a game from the start position to the end via a trajectory. In this work, we explore a model (based on the Steering Law) that predicts the piloting time required in spatial-constrained environments. Thus, two user studies are conducted to help us understand the relationship between path complexity (curvature) and spatial constraints (width and height). According to the results, we propose a model that can achieve $52.6 \%$ and $60.6 \%$ improvement in R-square and the Akaike Information Criterion (AIC), respectively. Next, an additional study was conducted to further verify the performance and efficiency of our proposed model with the change of movement direction and orientation. Our model and experimental results can benefit both game and interface designers of applications that require controlling moving objects along specific trajectories in virtual reality environments.

Object selection is essential in virtual reality (VR) head-mounted displays (HMDs). Prior work mainly focuses on enhancing and evaluating techniques for selecting a single object in VR, leaving a gap in the techniques for multi-object selection, a more complex but common selection scenario. To enable multi-object selection, the interaction technique should support group selection in addition to the default pointing selection mode for acquiring a single target. This composite interaction could be particularly challenging when using freehand gestural input. In this work, we present an empirical comparison of six freehand techniques, which are comprised of three mode-switching gestures (Finger Segment, Multi-Finger, and Wrist Orientation) and two group selection techniques (Cone-casting Selection and Crossing Selection) derived from prior work. Our results demonstrate the performance, user experience, and preference of each technique. The findings derive three design implications that can guide the design of freehand techniques for multi-object selection in VR HMDs.

Alphanumeric and special characters are essential during text entry. Text entry in virtual reality (VR) is usually performed on a virtual Qwerty keyboard to minimize the need to learn new layouts. As such, entering capitals, symbols, and numbers in VR is often a direct migration from a physical/touchscreen Qwerty keyboard-that is, using the mode-switching keys to switch between different types of characters and symbols. However, there are inherent differences between a keyboard in VR and a physical/touchscreen keyboard, and as such, a direct adaptation of mode-switching via switch keys may not be suitable for VR. The high flexibility afforded by VR opens up more possibilities for entering alphanumeric and special characters using the Qwerty layout. In this work, we designed two controller-based raycasting text entry methods for alphanumeric and special characters input (Layer-ButtonSwitch and Key-ButtonSwitch) and compared them with two other methods (Standard Qwerty Keyboard and Layer-PointSwitch) that were derived from physical and soft Qwerty keyboards. We explored the performance and user preference of these four methods via two user studies (one short-term and one prolonged use), where participants were instructed to input text containing alphanumeric and special characters. Our results show that Layer-ButtonSwitch led to the highest statistically significant performance, followed by Key-ButtonSwitch and Standard Qwerty Keyboard, while Layer-PointSwitch had the slowest speed. With continuous practice, participants' performance using Key-ButtonSwitch reached that of Layer-ButtonSwitch. Further, the results show that the key-level layout used in Key-ButtonSwitch led users to parallel mode switching and character input operations because this layout showed all characters on one layer. We distill three recommendations from the results that can help guide the design of text entry techniques for alphanumeric and special characters in VR.

Users' perceived image quality of virtual reality head-mounted displays (VR HMDs) is determined by multiple factors, including the HMD's structure, optical system, display and render resolution, and users' visual acuity (VA). Existing metrics such as pixels per degree (PPD) have limitations that prevent accurate comparison of different VR HMDs. One of the main limitations is that not all VR HMD manufacturers released the official PPD or details of their HMDs' optical systems. Without these details, developers and users cannot know the precise PPD or calculate it for a given HMD. The other issue is that the visual clarity varies with the VR environment. Our work has identified a gap in having a feasible metric that can measure the visual clarity of VR HMDs. To address this gap, we present an end-to-end and user-centric visual clarity metric, omnidirectional virtual visual acuity (OVVA), for VR HMDs. OVVA extends the physical visual acuity chart into a virtual format to measure the virtual visual acuity of an HMD's central focal area and its degradation in its noncentral area. OVVA provides a new perspective to measure visual clarity and can serve as an intuitive and accurate reference for VR applications sensitive to visual accuracy. Our results show that OVVA is a simple yet effective metric for comparing VR HMDs and environments.

Steering is a fundamental task in interactive Virtual Reality (VR) systems. Prior work has demonstrated that movement direction can significantly influence user behavior in the steering task, and different interactive environments (VEs) can lead to various behavioral patterns, such as tablets and PCs. However, its impact on VR environments remains unexplored. Given the widespread use of steering tasks in VEs, including menu adjustment and object manipulation, this work seeks to understand and model the directional effect with a focus on barehand interaction, which is typical in VEs. This paper presents the results of two studies. The first study was conducted to collect behavioral data with four categories: movement time, average movement speed, success rate, and reenter times. According to the results, we examined the effect of movement direction and built the SθModel. We then empirically evaluated the model through the data collected from the first study. The results proved that our proposed model achieved the best performance across all the metrics (r2 > 0.95), with more than 15% improvement over the original Steering Law in terms of prediction accuracy. Next, we further validated the SθModel by another study with the change of device and steering direction. Consistent with previous assessments, the model continues to exhibit optimal performance in both predicting movement time and speed. Finally, based on the results, we formulated design recommendations for steering tasks in VEs to enhance user experience and interaction efficiency.

Target selection is a fundamental task in interactive Augmented Reality (AR) systems. Predicting the intended target of selection in such systems can provide users with a smooth, low-friction interaction experience. Our work aims to predict gaze-based target selection in AR headsets with eye and head endpoint distributions, which describe the probability distribution of eye and head 3D orientation when a user triggers a selection input. We first conducted a user study to collect users’ eye and head behavior in a gaze-based pointing selection task with two confirmation mechanisms (air tap and blinking). Based on the study results, we then built two models: a unimodal model using only eye endpoints and a multimodal model using both eye and head endpoints. Results from a second user study showed that the pointing accuracy is improved by approximately 32% after integrating our models into gaze-based selection techniques.

Region selection is a fundamental task in interactive systems. In 2D user interfaces, users typically use a rectangle selection tool to formulate a region using a mouse or touchpad. Region selection in 3D spaces, especially in Augmented Reality (AR) Head-Mounted Displays (HMDs) is different and challenging because users need to select an intended region via freehand mid-air gestures or eye-based actions that are touchless interactions. In this work, we aim to fill in the gap in the design of region selection techniques in AR HMDs. We first analyzed and discretized the interaction procedure of region selection and explored design possibilities for each step. We then developed four techniques for region selection in AR HMDs, which leveraged users' hand and gaze for unimodal or multimodal interaction. The techniques were evaluated via a user study with a controlled region selection task. The findings led to three design recommendations and two proof-of-concept application examples.