In virtual reality, users are typically represented by and interact through a virtual body. Research frequently manipulates different features of these bodies. We analyze 208 studies to identify what aspects of virtual bodies are manipulated, how these manipulations affect interaction and other outcomes, and why they are assumed to do so. Based on the analysis, we propose a design space comprising seven types of visual manipulations: appearance, size, morphology, viewpoint, transfer, remapping, and control. We also synthesize findings on their effects—ranging from task performance to physiological responses and social outcomes—and examine the theories used to explain them, such as embodiment, Proteus effect, and presence. The design space helps researchers identify key variables and their interconnections in design and empirical research of virtual bodies. The synthesis further reveals unexplored causal connections and highlights theories that may account for observed effects.

VR interaction techniques define mappings between physical movements and virtual outcomes. While some mappings are learned through the adaptation of existing movement strategies, others require acquiring entirely new control policies. Drawing on motor learning theory, we introduce a design space that organizes these mappings into three families and provides a basis for reasoning about how the mappings are learned. To examine learning within individual families and their compositions, we start with two simple hand-based mappings, mirror reversal and cross-hand control, and their combination, allowing us to probe the design space in a controlled experiment with 96 participants. The mappings differ in initial difficulty, but participants achieve comparable overall learning. In the combined condition, prior exposure produces mapping specific start-up advantages. We discuss how this design space can support the analysis and design of VR mapping techniques.

A VR body can move faster than its user, making actions like reaching more efficient. We propose a VR body that not only moves faster during reaching, but also starts moving before the user has decided which target to reach for. However, it is unclear whether such a VR body would speed up choices, since moving towards a wrong target might cause confusion, or influence users’ choices. To explore these questions, we built a faster VR body prototype, focusing on an accelerated hand and arm, in a choice task. Thirty-four participants viewed random-dot displays to judge the overall motion direction and indicated their choice by pressing the corresponding button. Task difficulty was varied to influence choice uncertainty. Results showed that choice time decreased when the virtual body was 0.1 seconds ahead of the physical body, but increased when it was 0.3 seconds ahead. Users’ choice distributions showed no significant differences.

Tendon vibration can create movement illusions: vibrating the biceps tendon induces an illusion of extending the arm, while vibrating the triceps tendon induces an illusion of flexing the arm. However, it is unclear how to create and integrate such illusions shown in neuroscience to interaction techniques in virtual reality (VR). We first design a motor setup for tendon vibration. Study 1 validates that the setup induces movement illusions which on average create a 5.26 cm offset in active arm movements. Study 2 shows that tendon vibration improves the detection thresholds of visual motion gains often used in VR interaction techniques by 0.22. A model we developed in Study 2 predicts the effects of tendon vibration and is used in a biomechanical simulation to demonstrate the detection thresholds across typical reaching tasks in VR.

Presence appears an important concept for virtual reality (VR): It is frequently measured with questionnaires, and theory and methods about it have been discussed in numerous works. Yet, it is unclear how to actually work with this concept: Why is presence important to measure, how to choose an appropriate questionnaire, and what to conclude about it based on findings? To answer these questions, we review how the concept is put to work in 288 VR papers from 2023 measuring presence with questionnaires. Our findings include that measuring presence is often motivated by another construct, such as user experience; the reasons for choosing a specific questionnaire are often weak or not reported at all; and high presence values are frequently used simply to validate an interaction technique. We propose recommendations for working with presence and formulate questions to direct future research.

Designing efficient selection techniques for graphical user interfaces (GUIs) is fundamental in HCI research.We derive selection techniques based on the multiple process model, a theory that details the motor control processes during goal-directed movements.Specifically, we deduce three theoretical assumptions on how control processes of pre-planning, impulse control, and limb-target control could influence selection movements when adjusting GUI elements, including visual feedback, cursor position, and target position.Corresponding to our assumptions, we develop three techniques that hide the cursor when a target is highlighted, snap the cursor when selection begins, and expand clustered objects during selection movements.After that, we pre-register the assumptions and research methodology and evaluate the techniques in three crowdsourcing-based pointing studies.Our results show that all techniques improved the selection efficiency compared to established baselines.We further discuss the design implications and reflect on how we derived techniques from theory.

Virtual reality (VR) techniques can modify how physical body movements are mapped to the virtual body. However, it is unclear how users learn such mappings and, therefore, how the learning process may impede interaction. To understand and quantify the learning of the techniques, we design new metrics explicitly for VR interactions based on the motor learning literature. We evaluate the metrics in three object selection and manipulation tasks, employing linear-translational and nonlinear-rotational gains and finger-to-arm mapping. The study shows that the metrics demonstrate known characteristics of motor learning similar to task completion time, typically with faster initial learning followed by more gradual improvements over time. More importantly, the metrics capture learning behaviors that task completion time does not. We discuss how the metrics can provide new insights into how users adapt to movement mappings and how they can help analyze and improve such techniques.

Object selection has been explored extensively in the VR research literature. However, the research is typically conducted in constrained experimental setups. It remains unclear whether the designed selection techniques fit the prevalent practical uses and whether the experimental tasks represent important challenges in real applications. To identify and help bridge these gaps, we surveyed current consumer VR applications, containing 206 popular VR game and 3D modeling applications. We extracted 1300+ selection scenarios based on video analyses of these applications and derived a taxonomy to understand common patterns on where and how selections occur. Our findings reveal significant gaps in selection tasks and techniques between research and consumer applications. We also present an interactive visualization tool to help researchers explore the VR object selection scenarios. Finally, we discuss how our work can help researchers and developers evaluate techniques in meaningful tasks and drive the design of techniques.

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.

Temporal target selection requires users to wait and trigger the selection input within a bounded time window, with a selection cursor that is expected to be delayed. This task conceptualizes, for example, a variety of game scenarios such as determining the timing of shooting a projectile towards a moving object. In this work, we explore models that predict “when” users typically perform a selection (i.e., user selection distribution) and their selection error rates in such tasks. We hypothesize that users react to temporal factors including “distance”, “width”, and “delay” as how they treat the corresponding variables in spatial target selection. The derived models are evaluated in a controlled experiment and an MTurk-based online study. Our research contributes new knowledge on user behavior in temporal target selection tasks and its potential connection with its spatial correspondence. Our models and conclusions can benefit both users and designers of relevant interactive applications.

Non-invasive inspection and imaging techniques are used to acquire non-visible information embedded in samples. Typical applications include medical imaging, defect evaluation, and electronics testing. However, existing methods have specific limitations, including safety risks (e.g., X-ray), equipment costs (e.g., optical tomography), personnel training (e.g., ultrasonography), and material constraints (e.g., terahertz spectroscopy). Such constraints make these approaches impractical for everyday scenarios. In this article, we present a method that is low-cost and practical for non-invasive inspection in everyday settings. Our prototype incorporates a miniaturized near-infrared spectroscopy scanner driven by a computer-controlled 2D-plotter. Our work presents a method to optimize content embedding, as well as a wavelength selection algorithm to extract content without human supervision. We show that our method can successfully extract occluded text through a paper stack of up to 16 pages. In addition, we present a deep-learning-based image enhancement model that can further improve the image quality and simultaneously decompose overlapping content. Finally, we demonstrate how our method can be generalized to different inks and other layered materials beyond paper. Our approach enables a wide range of content embedding applications, including chipless information embedding, physical secret sharing, 3D print evaluations, and steganography.

Mirror reflections offer an intuitive and realistic Mixed Reality (MR) experience comparable to other MR interfaces. Their high visual fidelity, and the sensorimotor contingency from the reflected moving body, make the mirror an ideal instrument for MR movement guidance. The translucent two-way mirror display enables users to follow a virtual humanoid instructor’s movement accurately by visually matching it with their reflections. In this work, we conduct the first formal evaluation of movement acquisition performance with simple motor tasks, using visual guidance from an MR mirror and a humanoid virtual instructor. Our results of performance and subjective ratings indicate that, comparing with simulated virtual mirror and with traditional screen-based movement guidance, the real MR mirror yields better acquisition performance and stronger sense of embodiment with the reflection, for upper-body movement. But the benefits diminish with larger-range head movements. We provide design guidelines for future mirror movement guidance interfaces and MR mirror experiences at large.

On-body interfaces, which leverage the human body’s surface as an input or output platform, can provide new opportunities for designing VR interaction. However, it remains unclear how on-body interfaces can best support current VR systems that mainly rely on mid-air interaction. We propose BodyOn, a collection of six design patterns that leverage combined on-body and mid-air interfaces to achieve more effective 3D interaction. Specifically, a user may use thumb-on-finger gestures, finger-on-arm gestures, or on-body displays with mid-air input, including hand movement and orientation, to complete an interaction task. To test our design concepts, we implemented example interaction techniques based on BodyOn that can assist users in various 3D interaction tasks. We further conducted an expert evaluation using the techniques as probes to elicit immediate design issues that emerge from the novel combination of on-body and midair interaction. We provide insights that can inspire and inform the design of future 3D user interfaces.

Intelligent suggestion techniques can enable low-friction selection-based input within virtual or augmented reality (VR/AR) systems. Such techniques leverage probability estimates from a target prediction model to provide users with an easy-to-use method to select the most probable target in an environment. For example, a system could highlight the predicted target and enable a user to select it with a simple click. However, as the probability estimates can be made at any time, it is unclear when an intelligent suggestion should be presented. Earlier suggestions could save a user time and effort but be less accurate. Later suggestions, on the other hand, could be more accurate but save less time and effort. This paper thus proposes a computational framework that can be used to determine the optimal timing of intelligent suggestions based on user-centric costs and benefits. A series of studies demonstrated the value of the framework for minimizing task completion time and maximizing suggestion usage and showed that it was both theoretically and empirically effective at determining the optimal timing for intelligent suggestions.

Smart speakers have become exceedingly popular and entered many people's homes due to their ability to engage users with natural conversations. Researchers have also looked into using smart speakers as an interface to collect self-reported health data through conversations. Responding to surveys prompted by smart speakers requires users to listen to questions and answer in voice without any visual stimuli. Compared to traditional web-based surveys, where users can see questions and answers visually, voice surveys may be more cognitively challenging. Therefore, to collect reliable survey data, it is important to understand what types of questions are suitable to be administered by smart speakers. We selected five common survey questionnaires and deployed them as voice surveys and web surveys in a within-subject study. Our 24 participants answered questions using voice and web questionnaires in one session. They then repeated the same study session after 1 week to provide a "retest'' response. Our results suggest that voice surveys have comparable reliability to web surveys. We find that, when using 5-point or 7-point scales, voice surveys take about twice as long as web surveys. Based on objective measurements, such as response agreement and test-retest reliability, and subjective evaluations of user experience, we recommend that researchers consider adopting the binary scale and 5-point numerical scales for voice surveys on smart speakers.

We investigate the use of a miniaturized Near-Infrared Spectroscopy (NIRS) device in an assisted decision-making task. We consider the real-world scenario of determining whether food contains gluten, and we investigate how end-users interact with our NIRS detection device to ultimately make this judgment. In particular, we explore the effects of different nutrition labels and representations of confidence on participants’ perception and trust. Our results show that participants tend to be conservative in their judgment and are willing to trust the device in the absence of understandable label information. We further identify strategies to increase user trust in the system. Our work contributes to the growing body of knowledge on how NIRS can be mass-appropriated for everyday sensing tasks, and how to enhance the trustworthiness of assisted decision-making systems.

This paper investigates integration, coordination, and transition strategies of gaze and hand input for 3D object manipulation in VR. Specifically, this work aims to understand whether incorporating gaze input can benefit VR object manipulation tasks, and how it should be combined with hand input for improved usability and efficiency. We designed four gaze-supported techniques that leverage different combination strategies for object manipulation and evaluated them in two user studies. Overall, we show that gaze did not offer significant performance benefits for transforming objects in the primary working space, where all objects were located in front of the user and within the arm-reach distance, but can be useful for a larger environment with distant targets. We further offer insights regarding combination strategies of gaze and hand input, and derive implications that can help guide the design of future VR systems that incorporate gaze input for 3D object manipulation.

Text entry is an important and frequent task in interactive devices including augmented reality head-mounted displays (AR HMDs). In current AR HMDs, there are still two main open challenges to overcome for efficient and usable text entry: arm fatigue due to mid-air input and visual occlusion because of their small see-through displays. To address these challenges, we present iText, a technique for AR HMDs that is hands-free and is based on an imaginary (invisible) keyboard. We first show that it is feasible and practical to use an imaginary keyboard on AR HMDs. Then, we evaluated its performance and usability with three hands-free selection mechanisms: eye blinks (E-Type), dwell (D-Type), and swipe gestures (G-Type). Our results show that users could achieve an average text entry speed of 11.95, 9.03 and 9.84 words per minutes (WPM) with E-Type, D-Type, and G-Type, respectively. Given that iText with E-Type outperformed the other two selection mechanisms in text entry rate and subjective feedback, we ran a third, 5-day study. Our results show that iText with E-Type can achieve an average text entry rate of 13.76 WPM with a mean word error rate of 1.5%. In short, iText can enable efficient eyes-free text entry and can be useful for various application scenarios in AR HMDs.

Text entry is a common activity in virtual reality (VR) systems. There is a limited number of available hands-free techniques, which allow users to carry out text entry when users’ hands are busy such as holding items or hand-based devices are not available. The most used hands-free text entry technique is DwellType, where a user selects a letter by dwelling over it for a specific period. However, its performance is limited due to the fixed dwell time for each character selection. In this paper, we explore two other hands-free text entry mechanisms in VR: BlinkType and NeckType, which leverage users’ eye blinks and neck’s forward and backward movements to select letters. With a user study, we compare the performance of the two techniques with DwellType. Results show that users can achieve an average text entry rate of 13.47, 11.18 and 11.65 words per minute with BlinkType, NeckType, and DwellType, respectively. Users’ subjective feedback shows BlinkType as the preferred technique for text entry in VR.

While text tends to lead a rather static life on paper and screens, virtual reality (VR) allows readers to interact with it in novel ways: the reading surface is no longer confined to a 2D plane. We conducted two user studies, in which we assessed text rendered on different surface shapes in VR and their effects on legibility and the reading experience. Comparing differently curved surfaces, these studies disclose the impact of warp angles and view box widths on reading comfort, speed, and distraction. Our results suggest that text should be warped around the horizontal rather than the vertical axis, and we provide recommendations for the extent of warp and view box width. In a proof-of-concept application, we used everyday 3D objects as text canvases and studied them through an information-seeking task. The studies’ implications inform VR interfaces and, more generally, the rendering of text on 3D objects.

Selection studies are prevalent and indispensable for VR research. However, due to the tedious and repetitive nature of many such experiments, participants can become disengaged during the study, which is likely to impact the results and conclusions. In this work, we investigate participant disengagement in VR selection experiments and how this issue affects the outcomes. Moreover, we evaluate the usefulness of four engagement strategies to keep participants engaged during VR selection studies and investigate how they impact user performance when compared to a baseline condition with no engagement strategy. Based on our findings, we distill several design recommendations that can be useful for future VR selection studies or user tests in other domains that employ similar repetitive features.

This research explores the design and evaluation of visualization techniques of targets that reside outside of users' view and/or are occluded by other elements within a virtual reality environment (VE). We first compare four techniques (3DWedge, 3DArrow, 3DMinimap, and Radar) that use different types of visual elements to provide direction and distance information of targets. To give structure to their evaluation, we also develop a framework of four tasks (one for direction and three for distance) and their assessment criteria. The results of the first study show that 3DWedge is the best-performing and most usable technique. However, all techniques, including 3DWedge, have poor performance in dense scenarios with a large number of targets. To improve support in dense scenarios, a fifth technique, 3DWedge+, is developed by using 3DWedge as its foundation and including additional visual elements that are derived from the other three techniques which are shown to be useful. A second study is conducted to evaluate the performance of 3DWedge+ in relation to the other techniques. The results show that both 3DWedge and 3DWedge+ are significantly better in distinguishing user-to-target distance and that 3DWedge+ is particularly suitable for dense scenarios. Based on these results, we provide a set of recommendations for the design of visualization techniques of off-screen and occluded targets in 3D VE.

The presence of fully-occluded targets is common within virtual environments, ranging from a virtual object located behind a wall to a datapoint of interest hidden in a complex visualization. However, efficient input techniques for locating and selecting these targets are mostly underexplored in virtual reality (VR) systems. In this paper, we developed an initial set of seven techniques techniques for fully-occluded target selection in VR. We then evaluated their performance in a user study and derived a set of design implications for simple and more complex tasks from our results. Based on these insights, we refined the most promising techniques and conducted a second, more comprehensive user study. Our results show how factors, such as occlusion layers, target depths, object densities, and the estimation of target locations, can affect technique performance. Our findings from both studies and distilled recommendations can inform the design of future VR systems that offer selections for fully-occluded targets.

Reaching towards out-of-sight objects during walking is a common task in daily life, however the same task can be challenging when wearing immersive Head-Mounted Displays (HMD). In this paper, we investigate the effects of spatial reference frame, walking path curvature, and target placement relative to the body on user performance of manually acquiring out-of-sight targets located around their bodies, as they walk in a spatial-mapping Mixed Reality (MR) environment wearing an immersive HMD. We found that walking and increased path curvature negatively affected the overall spatial accuracy of the performance, and that the performance benefited more from using the torso as the reference frame than the head. We also found that targets placed at maximum reaching distance yielded less error in angular rotation and depth of the reaching arm. We discuss our findings with regard to human walking kinesthetics and the sensory integration in the peripersonal space during locomotion in immersive MR. We provide design guidelines for future immersive MR experience featuring spatial mapping and full-body motion tracking to provide better embodied experience.

We present DMove, directional motion-based interaction for Augmented Reality (AR) Head-Mounted Displays (HMDs) that is both hands- and device-free. It uses directional walk-ing as a way to interact with virtual objects. To use DMove, a user needs to perform directional motions such as mov-ing one foot forward or backward. In this research, we first investigate the recognition accuracy of the motion direc-tions of our method and the social acceptance of this type of interactions together with users' comfort rating for each direction. We then optimize its design and conduct a sec-ond study to compare DMove in task performance and user preferences (workload, motion sickness, user experience), with two approaches-Hand interaction (Meta 2-like) and Head+Hand interaction (HoloLens-like) for menu selection tasks. Based on the results of these two studies, we provide a set of guidelines for DMove and further demonstrate two applications that utilize directional motions.

Head-based interactions are very handy for virtual reality (VR) head-worn display (HWD) systems. A useful head-based interaction technique could help users to interact with VR environments in a hands-free manner (i.e., without the need of a hand-held de-vice). Moreover, it can sometimes be seamlessly integrated with other input modalities to provide richer interaction possibilities. This paper explores the potential of a new approach that we call DepthMove to allow interactions that are based on head motions along the depth dimension. With DepthMove, a user can interact with a VR system proactively by moving the head perpendicular to the VR HWD forward or backward. We use two user studies to investigate, model, and optimize DepthMove by taking into con-sideration user performance, subjective response, and social ac-ceptability. The results allow us to determine the optimal and comfortable DepthMove range. We also distill recommendations that can be used to guide the design interfaces that use DepthMove for efficient and accurate interaction in VR HWD systems. A third study is conducted to demonstrate the usefulness of DepthMove relative to other techniques in four application scenarios.

Text entry is a common activity in virtual reality (VR) systems. However, there is a limited number of approaches available for mobile VR systems, where it might be inconvenient for users to carry an input device. We propose a novel hands-free text entry technique we call DepthText which leverages the acceleration sensing abilities of built-in IMU sensors of mobile VR systems. Users are able to enter text by moving their head forward. The results of a 5-day study indicate that users can achieve an average of 10.76 words per minute (wpm) on the last day with low errors. This performance is comparable to the dwell-based technique which is the most common way of entering text that is hands-free. One advantage of DepthText over the dwell-based technique is that users can have more control of the pace of selecting characters, rather than being pushed by a pre-set dwell time.

Hands-free interactions are very handy for virtual reality (VR) head-worn display (HWD) systems because they allow users to interact with VR environments without the need for a hand-held device. This paper explores the potential of a new approach that we call DepthMove to allow hands-free interactions that are based on head motions towards the depth dimension. With DepthMove, a user can interact in a VR system proactively by moving towards the depth dimension with an HWD. We present the concept and implementation of DepthMove in VR HWD systems and demonstrate its potential applications. We further discuss the advantages and limitations of using DepthMove.

Understanding the endpoint distribution of pointing selection tasks can reveal the underlying patterns on how users tend to acquire a target, which is one of the most essential and pervasive tasks in interactive systems. It could further aid designers to create new graphical user interfaces and interaction techniques that are optimized for accuracy, efficiency, and ease of use. Previous research has explored the modeling of endpoint distribution outside of virtual reality (VR) systems that have shown to be useful in predicting selection accuracy and guide the design of new interactive techniques. This work aims at developing an endpoint distribution of selection tasks for VR systems which has resulted in EDModel , a novel model that can be used to predict endpoint distribution of pointing selection tasks in VR environments. The development of EDModel is based on two users studies that have explored how factors such as target size, movement amplitude, and target depth affect the endpoint distribution. The model is built from the collected data and its generalizability is subsequently tested in complex scenarios with more relaxed conditions. Three applications of EDModel inspired by previous research are evaluated to show the broad applicability and usefulness of the model: correcting the bias in Fitts's law, predicting selection accuracy, and enhancing pointing selection techniques. Overall, EDModel can achieve high prediction accuracy and can be adapted to different types of applications in VR.

In this paper, we present a case for text entry using a circular keyboard layout for mobile head-mounted displays (HMDs) that is dwell-free and does not require users to hold a dedicated input device for letter selection. To support the case, we have implemented RingText whose design is based on a circular layout with two concentric circles. The outer circle is subdivided into regions containing letters. Selection is made by using a virtual cursor controlled by the user's head movements-entering a letter region triggers a selection and moving back into the inner circle resets the selection. The design of RingText follows an iterative process, where we initially conduct one first study to investigate the optimal number of letters per region, inner circle size, and alphabet starting location. We then optimize its design by selecting the most suitable features from the first study: one letter per region, narrowing the trigger area to lower error rates, and creating candidate regions that incorporate two suggested words to appear next to the current letter region (close to the cursor) using a dynamic (rather than fixed) approach. Our second study compares text entry performance of RingText with four other hands-free techniques and the results show that RingText outperforms them. Finally, we run a third study lasting four consecutive days with 10 participants (5 novice users and 5 expert users) doing two daily sessions and the results show that RingText is quite efficient and yields a low error rate. At the end of the eighth session, the novice users can achieve a text entry speed of 11.30 WPM after 60 minutes of training while the expert (more experienced) users can reach an average text entry speed of 13.24 WPM after 90 minutes of training.

Presents corrections to author information for the paper, “RingText: Dwell-Free and Hands-Free Text Entry for Mobile Head-Mounted Displays Using Head Motions,” (Xu, W., et al), IEEE Trans. Vis. Comput. Graph., vol. 25, no. 5, pp. 1991–2001, May 2019.

Interactive visualizations are external cognitive artifacts aimed at supporting users' exploratory and sense-making activities. In recent years, there has been an explosion of commercial virtual reality (VR) head-mounted displays (HMD). These VR devices are meant to offer high levels of engagement and improve users' analytical exploration of the displayed content. However, given their rapid market introduction, the possible influences and usefulness that VR could bring in terms of supporting users' exploration with interactive visualizations remain largely underexplored. We attempt to fill this gap and provide results of an empirical study of an interactive visualization tool that we have developed for a VR HMD system. This tool is aimed at facilitating exploratory and analytical reasoning activities with 3D shapes and their transformational processes. Overall, the results show that the tool is supportive of users' exploratory and analytical activities based on the significant improvement in their post-experiment test scores (when compared to their pre-experiment ones) and their engagement level measured via a user engagement questionnaire and participants' comments. The results shed a positive light on the use of visualizations in VR environments and can inform the design of these tools of domains beyond 3D transformational geometry.

We present PizzaText, a circular keyboard layout technique for text entry in virtual reality (VR) environments that uses the dual thumbsticks of a hand-held game controller. Text entry is a common activity in VR environments but remains challenging with existing techniques and keyboard layouts that is largely based on QWERTY. Our technique makes text entry simple, easy, and efficient, even for novice users. The technique uses a hand-held controller because it is still an important input device for users to interact with VR environments. To allow rapid search of characters, PizzaText divides a circle into slices and each slice contains 4 characters. To enable fast selection, the user uses the right thumbstick for traversing the slices, and the left thumbstick for choosing the letters. The design of PizzaText is based on three criteria: efficiency, learnability, and ease-of-use. In our first study, six potential layouts are considered and evaluated. The results lead to a design with 7 slices and 4 letters per slice. The final design is evaluated in a five-day study with 10 participants. The results show that novice users can achieve an average of 8.59 Words per Minute (WPM), while expert users are able to reach 15.85 WPM, with just two hours of training.