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"doi": "10.1109/ISWC.2004.32",
"title": "My Own Private Kiosk: Privacy-Preserving Public Displays",
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"abstract": "Ubiquitous, high-resolution, large public displays offer an attractive complement to wearable displays. Unfortunately, the inherently public nature of these public displays makes them unsuitable for displaying sensitive information. We present EyeGuide, a wearable system that allows the user to obtain information quickly from a public display without sacrificing privacy. To this end, EyeGuide employs a lightweight head-worn eye-tracker for hands-free object selection and an earphone for private communication. Our system supports public displays that are dynamic (e.g., a large plasma screen) and static (e.g., a large printed map). In our printed map scenario, EyeGuide whispers verbal directions via earphone to a user, based on where they are looking on the map. Using a technique we call \"gaze steering,\" the system guides the user's eye position to specific locations. In our dynamic public display scenarios, EyeGuide presents documents (e.g., maps) that contain sensitive data in a way that preserves privacy.",
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"content": "Ubiquitous, high-resolution, large public displays offer an attractive complement to wearable displays. Unfortunately, the inherently public nature of these public displays makes them unsuitable for displaying sensitive information. We present EyeGuide, a wearable system that allows the user to obtain information quickly from a public display without sacrificing privacy. To this end, EyeGuide employs a lightweight head-worn eye-tracker for hands-free object selection and an earphone for private communication. Our system supports public displays that are dynamic (e.g., a large plasma screen) and static (e.g., a large printed map). In our printed map scenario, EyeGuide whispers verbal directions via earphone to a user, based on where they are looking on the map. Using a technique we call \"gaze steering,\" the system guides the user's eye position to specific locations. In our dynamic public display scenarios, EyeGuide presents documents (e.g., maps) that contain sensitive data in a way that preserves privacy.",
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"fullName": "G?bor Blask?",
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"affiliation": "Rochester Institute of Technology",
"fullName": "Jason Babcock",
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"affiliation": "Columbia University",
"fullName": "Steven Feiner",
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"proceeding": {
"id": "1CJbEwHHqEg",
"title": "2022 IEEE on Conference Virtual Reality and 3D User Interfaces (VR)",
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"groupId": "1000791",
"volume": "0",
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"year": "2022",
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"article": {
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"doi": "10.1109/VR51125.2022.00059",
"title": "Real-Time Gaze Tracking with Event-Driven Eye Segmentation",
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"abstract": "Gaze tracking is increasingly becoming an essential component in Augmented and Virtual Reality. Modern gaze tracking algorithms are heavyweight; they operate at most 5 Hz on mobile processors despite that near-eye cameras comfortably operate at a real-time rate (> 30 Hz). This paper presents a real-time eye tracking algorithm that, on average, operates at 30 Hz on a mobile processor, achieves 0.1°–0.5° gaze accuracies, all the while requiring only 30K parameters, one to two orders of magnitude smaller than state-of-the-art eye tracking algorithms. The crux of our algorithm is an Auto ROI mode, which continuously predicts the Regions of Interest (ROIs) of near-eye images and judiciously processes only the ROIs for gaze estimation. To that end, we introduce a novel, lightweight ROI prediction algorithm by emulating an event camera. We discuss how a software emulation of events enables accurate ROI prediction without requiring special hardware. The code of our paper is available at https://github.com/horizon-research/edgaze.",
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"content": "Gaze tracking is increasingly becoming an essential component in Augmented and Virtual Reality. Modern gaze tracking algorithms are heavyweight; they operate at most 5 Hz on mobile processors despite that near-eye cameras comfortably operate at a real-time rate (> 30 Hz). This paper presents a real-time eye tracking algorithm that, on average, operates at 30 Hz on a mobile processor, achieves 0.1°–0.5° gaze accuracies, all the while requiring only 30K parameters, one to two orders of magnitude smaller than state-of-the-art eye tracking algorithms. The crux of our algorithm is an Auto ROI mode, which continuously predicts the Regions of Interest (ROIs) of near-eye images and judiciously processes only the ROIs for gaze estimation. To that end, we introduce a novel, lightweight ROI prediction algorithm by emulating an event camera. We discuss how a software emulation of events enables accurate ROI prediction without requiring special hardware. The code of our paper is available at https://github.com/horizon-research/edgaze.",
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"affiliation": "University of Rochester",
"fullName": "Yu Feng",
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"affiliation": "University of Rochester",
"fullName": "Nathan Goulding-Hotta",
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"affiliation": "University of Rochester",
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"id": "1GU6OXhOl4k",
"title": "2022 12th International Congress on Advanced Applied Informatics (IIAI-AAI)",
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"doi": "10.1109/IIAIAAI55812.2022.00042",
"title": "Development and evaluation of car training system using VR and eye tracking technology",
"normalizedTitle": "Development and evaluation of car training system using VR and eye tracking technology",
"abstract": "When practicing car driving in a real world, there are risks like traffic accidents. With the development of virtual reality technology, we can create environments in which people can practice without being exposed to certain dangers like traffic accidents that potentially occur in real environments. So far, many researches regarding car driving in VR have been developed, but little attention has been paid to analyzing and visualizing eye gaze data collected in VR education systems. Analyzing and visualizing the collected eye gaze data is potential to grasp information of whether learners overlooked objects such as traffic signals and signs. In this study, we developed a driving simulator in VR and collected eye tracking data from five university students while they were practicing driving using eye tracking technology. Also, we have developed a system that allows learners to reflect on their own driving using the collected their eye gaze data. We compared the changes in gaze between those who received feedback to improve their driving skills and those who did not. The results showed that the feedback was effective because it reduced the number of oversights while driving.",
"abstracts": [
{
"abstractType": "Regular",
"content": "When practicing car driving in a real world, there are risks like traffic accidents. With the development of virtual reality technology, we can create environments in which people can practice without being exposed to certain dangers like traffic accidents that potentially occur in real environments. So far, many researches regarding car driving in VR have been developed, but little attention has been paid to analyzing and visualizing eye gaze data collected in VR education systems. Analyzing and visualizing the collected eye gaze data is potential to grasp information of whether learners overlooked objects such as traffic signals and signs. In this study, we developed a driving simulator in VR and collected eye tracking data from five university students while they were practicing driving using eye tracking technology. Also, we have developed a system that allows learners to reflect on their own driving using the collected their eye gaze data. We compared the changes in gaze between those who received feedback to improve their driving skills and those who did not. The results showed that the feedback was effective because it reduced the number of oversights while driving.",
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"affiliation": "Hiroshima City University,Graduate School of Information Sciences and Technology,Department of Intelligent Engineering,Hiroshima,Japan",
"fullName": "Tetsuhiro Ito",
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{
"affiliation": "Hiroshima City University,Graduate School of Information Sciences and Technology,Department of Intelligent Engineering,Hiroshima,Japan",
"fullName": "Kousuke Mouri",
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"affiliation": "Hiroshima City University,Graduate School of Information Sciences and Technology,Department of Intelligent Engineering,Hiroshima,Japan",
"fullName": "Masaru Okamoto",
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{
"affiliation": "Hiroshima City University,Graduate School of Information Sciences and Technology,Department of Intelligent Engineering,Hiroshima,Japan",
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"content": "Teaching in optical systems design is usually performed on an optical bench. While experimentation plays an important role in education, experiments involving expensive or dangerous components are usually limited to short, heavily supervised sessions. Computer simulations, on the other hand, offer high accessibility, but suffer from reduced realism and tangibility when presented on a 2D screen. For this reason, we present the virtual optical bench, an application that lets users explore spherical lens layouts in virtual reality (VR). We implemented a numerically accurate simulation of optical systems using Nvidia OptiX, as well as a prototypical VR application, which we then evaluated in an expert review with 6 optics experts. Based on their feedback, we re-implemented our VR application in Unreal Engine 4. The re-implementation has since been actively used for teaching optical layouts, where we performed a qualitative evaluation with 18 students. We show that our virtual optical bench achieves good usability and is perceived to enhance the understanding of course contents.",
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"affiliation": "Visual Computing Institute, RWTH Aachen University",
"fullName": "Martin Bellgardt",
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"affiliation": "Visual Computing Institute, RWTH Aachen University",
"fullName": "Sebastian Pape",
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"surname": "Pape",
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{
"affiliation": "Visual Computing Institute, RWTH Aachen University",
"fullName": "David Gilbert",
"givenName": "David",
"surname": "Gilbert",
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{
"affiliation": "RWTH Aachen University,Chair for Technology of Optical Systems",
"fullName": "Marcel Prochnau",
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"surname": "Prochnau",
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{
"affiliation": "RWTH Aachen University,Chair for Technology of Optical Systems",
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"surname": "König",
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{
"affiliation": "Visual Computing Institute, RWTH Aachen University",
"fullName": "Torsten W. Kuhlen",
"givenName": "Torsten W.",
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"abstract": "Common VR headsets require lenses that increase the field of view and allow the user to focus the display. In most systems, thick lenses are used that generate strong pincushion distortions. To account for this, the content is warped by a corresponding barrel distortion before displaying, resulting in undistorted images for the viewer. This approach assumes that the eye is exactly positioned, typically on the optical axis of the lens. However, in real systems the eye's location deviates from this optimal position even at rest; moreover, the pupil - and thus the optical center of the eye - moves by several millimeters when the user looks around. Thus, eye movement results in additional distortion, which is ignored in current VR headsets. Also in literature on head-mounted displays, the effect is most often not considered, or at least badly documented. The contribution of this paper are experiments that emphasize the importance of this mostly ignored effect. To this end, we have built a simple setup with a camera at variable eye positions in a standard VR headset, that allows us to directly measure the variation of distortion during eye movement. Our experiments show that distortion varies by several dozens of pixels within the full range of eye movements, which emphasizes that the effect is definitely significant. We also demonstrate how in a headset with built-in eye tracker, the knowledge of the eye position can be used to achieve a view-dependent lens correction with only minimal additional effort at run-time.",
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"content": "Common VR headsets require lenses that increase the field of view and allow the user to focus the display. In most systems, thick lenses are used that generate strong pincushion distortions. To account for this, the content is warped by a corresponding barrel distortion before displaying, resulting in undistorted images for the viewer. This approach assumes that the eye is exactly positioned, typically on the optical axis of the lens. However, in real systems the eye's location deviates from this optimal position even at rest; moreover, the pupil - and thus the optical center of the eye - moves by several millimeters when the user looks around. Thus, eye movement results in additional distortion, which is ignored in current VR headsets. Also in literature on head-mounted displays, the effect is most often not considered, or at least badly documented. The contribution of this paper are experiments that emphasize the importance of this mostly ignored effect. To this end, we have built a simple setup with a camera at variable eye positions in a standard VR headset, that allows us to directly measure the variation of distortion during eye movement. Our experiments show that distortion varies by several dozens of pixels within the full range of eye movements, which emphasizes that the effect is definitely significant. We also demonstrate how in a headset with built-in eye tracker, the knowledge of the eye position can be used to achieve a view-dependent lens correction with only minimal additional effort at run-time.",
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"affiliation": "University of Erlangen-Nuremberg",
"fullName": "Jonathan Martschinke",
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"affiliation": "University of Erlangen-Nuremberg",
"fullName": "Jana Martschinke",
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"affiliation": "University of Erlangen-Nuremberg",
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"affiliation": "University of Erlangen-Nuremberg",
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"title": "2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)",
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"doi": "10.1109/ISMAR50242.2020.00027",
"title": "Optical distortions in VR bias the perceived slant of moving surfaces",
"normalizedTitle": "Optical distortions in VR bias the perceived slant of moving surfaces",
"abstract": "The magnifying optics of virtual reality (VR) head-mounted displays (HMD) often cause undesirable pincushion distortion in the displayed imagery. Eccentrically increasing magnification radially displaces image-points away from the optical axis, causing straight lines to curve outwards. This, in turn, should affect the 3D perception of surface shape by warping binocular and monocular depth cues. Previous research has shown that distortion-induced biases in perceived slant do occur in static images. However, most use cases in VR involve moving images. Here we evaluate the impact of motion on biases in perceived slant. An HMD was used to present flat, textured surfaces that varied in slant and were either stationary, or translated laterally by the observer. In separate studies we varied the degree of distortion and evaluated the impact on perceived slant at several locations along the surface. We found that, irrespective of whether the surface was moving or stationary, distortion introduced significant bias into local slant estimates. The pattern of results is consistent with the surface appearing to be concave (as if viewing the inside surface of a bowl), as predicted from the warping of binocular and monocular cues. Importantly, the intermediate distortion level produced the same, but weaker, pattern of biases seen in the fully-distorted condition. When an appropriate level of pre-warping was applied, slant perception was veridical. Overall, our results highlight the importance of sufficiently correcting for optical distortions in VR HMDs to enable veridical perception of surface attitude.",
"abstracts": [
{
"abstractType": "Regular",
"content": "The magnifying optics of virtual reality (VR) head-mounted displays (HMD) often cause undesirable pincushion distortion in the displayed imagery. Eccentrically increasing magnification radially displaces image-points away from the optical axis, causing straight lines to curve outwards. This, in turn, should affect the 3D perception of surface shape by warping binocular and monocular depth cues. Previous research has shown that distortion-induced biases in perceived slant do occur in static images. However, most use cases in VR involve moving images. Here we evaluate the impact of motion on biases in perceived slant. An HMD was used to present flat, textured surfaces that varied in slant and were either stationary, or translated laterally by the observer. In separate studies we varied the degree of distortion and evaluated the impact on perceived slant at several locations along the surface. We found that, irrespective of whether the surface was moving or stationary, distortion introduced significant bias into local slant estimates. The pattern of results is consistent with the surface appearing to be concave (as if viewing the inside surface of a bowl), as predicted from the warping of binocular and monocular cues. Importantly, the intermediate distortion level produced the same, but weaker, pattern of biases seen in the fully-distorted condition. When an appropriate level of pre-warping was applied, slant perception was veridical. Overall, our results highlight the importance of sufficiently correcting for optical distortions in VR HMDs to enable veridical perception of surface attitude.",
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"normalizedAbstract": "The magnifying optics of virtual reality (VR) head-mounted displays (HMD) often cause undesirable pincushion distortion in the displayed imagery. Eccentrically increasing magnification radially displaces image-points away from the optical axis, causing straight lines to curve outwards. This, in turn, should affect the 3D perception of surface shape by warping binocular and monocular depth cues. Previous research has shown that distortion-induced biases in perceived slant do occur in static images. However, most use cases in VR involve moving images. Here we evaluate the impact of motion on biases in perceived slant. An HMD was used to present flat, textured surfaces that varied in slant and were either stationary, or translated laterally by the observer. In separate studies we varied the degree of distortion and evaluated the impact on perceived slant at several locations along the surface. We found that, irrespective of whether the surface was moving or stationary, distortion introduced significant bias into local slant estimates. The pattern of results is consistent with the surface appearing to be concave (as if viewing the inside surface of a bowl), as predicted from the warping of binocular and monocular cues. Importantly, the intermediate distortion level produced the same, but weaker, pattern of biases seen in the fully-distorted condition. When an appropriate level of pre-warping was applied, slant perception was veridical. Overall, our results highlight the importance of sufficiently correcting for optical distortions in VR HMDs to enable veridical perception of surface attitude.",
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"Magnifying Optics",
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"VR HMD",
"Surface Attitude",
"Visualization",
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"Optical Distortion",
"Resists",
"Distortion",
"Predistortion",
"Lenses",
"Human Centered Computing",
"Virtual Reality",
"Human Centered Computing",
"Empirical Studies In HCI",
"Computing Methodologies",
"Perception",
"Computing Methodologies",
"Virtual Reality"
],
"authors": [
{
"affiliation": "York University,Centre for Vision Research,Department of Psychology,Toronto,Ontario,Canada",
"fullName": "Jonathan Tong",
"givenName": "Jonathan",
"surname": "Tong",
"__typename": "ArticleAuthorType"
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"abstract": "Virtual reality (VR) is gaining interest as a platform for sports skills training. VR allows for information manipulation and feedback that would be difficult in reality. This is particularly useful in open skill sports where players must adjust their behavior in response to environmental factors. Auditory feedback (sonification) is constructive for sports training in VR. However, this has not been well studied in open skill-specific situations due to the difficulty of accounting for environmental factors in reality. This study focuses on a serve return, an important scene in tennis. It investigates the effects of sonification on the forehand return stroke in VR by comparing score displays and trajectory visualizations. We designed the sonification based on the difference between experienced and novice players' movements in VR. We then conducted a between-subjects experiment to investigate the effect of the sonification (N=20). The results showed that the system with sonification effectively improved the timing of hip movement for preparing a slow serve return compared to the system without sonification.",
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"abstract": "As a depth sensing approach, whilst stereo vision provides a good compromise between accuracy and cost, a key limitation is the limited field of view of the conventional cameras that are used within most stereo configurations. By contrast, the use of spherical cameras within a stereo configuration offers omnidirectional stereo sensing. However, despite the presence of significant image distortion in spherical camera images, only very limited attempts have been made to study and quantify omnidirectional stereo depth accuracy.In this paper we construct such an omnidirectional stereo system that is capable of real-time 360° disparity map reconstruction as the basis for such a study. We first investigate the accuracy of using a standard spherical camera model for calibration combined with a longitude-latitude projection for omnidirectional stereo, and show that the depth error increases significantly as the angle from the camera optical axis approaches the limits of the camera field of view.In contrast, we then consider an alternative calibration approach via the use of perspective undistortion with a conventional pinhole camera model allowing omnidirectional cameras to be mapped to a conventional rectilinear stereo formulation. We find that conversely this proposed approach exhibits improved depth accuracy at large angles from the camera optical axis when compared to omnidirectional stereo depth based on a spherical camera model calibration.",
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"affiliation": "University of Konstanz,Germany",
"fullName": "Matthias Albrecht",
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"abstract": "Finger gesture input is emerged as an increasingly popular means of human-computer interactions. In this paper, we propose iPand, an acoustic sensing system that enables finger gesture input on the skin, which is more convenient, user-friendly and always accessible. Unlike previous works, which implement gesture input with dedicated devices, our system exploits passive acoustic sensing to identify the gestures, e.g. swipe left, swipe right, pinch and spread. The insight of our system is that specific gesture emits unique friction sound, which can be captured by the microphone embedded in wearable devices. We capture these acoustic signals and extract the features by using bandpass filters and short-time Fourier Transform. The offline convolutional neural network is adopted to recognize the gestures. iPand is implemented and evaluated using COTS smartphones and smartwatches. Experiment results show that iPand can achieve the recognition accuracy of 89%, 83% and 78% in three daily scenarios (i.e., library, lab and cafe), respectively. Particularly, our system supports multi-touch function where 2-4 fingers are enabled for more efficient and expressive gesture input, and its average accuracy for individual finger gesture reaches up to 83% within 12 gestures.",
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"abstract": "Text input is an important part of the data annotation process, where text is used to capture ideas and comments. For text entry in immersive virtual environments, for which standard keyboards usually do not work, various approaches have been proposed. While these solutions have mostly proven effective, there still remain certain shortcomings making further investigations worthwhile. Motivated by recent research, we propose the speech-based multimodal text entry system SWIFTER, which strives for simplicity while maintaining good performance. In an initial user study, we compared our approach to smartphone-based text entry within a CAVE-like virtual environment. Results indicate that SWIFTER reaches an average input rate of 23.6 words per minute and is positively received by users in terms of user experience.",
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"abstract": "As touchscreen mobile devices grow in popularity, it is inevitable that software developers will eventually want to write code on them. However, writing code on a soft (or virtual) keyboard is cumbersome due to the device size and lack of tactile feedback. We present a soft syntax-directed keyboard extension to the QWERTY keyboard for Java program input on touchscreen devices and evaluate this keyboard with Java programmers. Our results indicate that a programmer using the keyboard extension can input a Java program with fewer errors and using fewer keystrokes per character than when using a standard soft keyboard alone. In addition, programmers maintain an overall typing speed in words per minute that is equivalent to that on the standard soft keyboard alone. The keyboard extension was shown to be mentally, physically, and temporally less demanding than the standard soft keyboard alone when inputting a Java program.",
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"abstract": "In this study, we explore the physiological effect of cybersickness using heart rate variability (HRV) and examine the relationship between the physiological and subjective measurement of cybersickness when navigating a virtual environment (VE). To achieve these objectives, we conducted two experiments. In experiment 1, four types of forwarding translation method (teleportation + embodied control [EC], steering + EC, teleportation + instrumental control [IC], and steering + IC) were compared, while six types of rotation (yaw, pitch, roll, yaw + pitch, yaw + roll, and pitch + roll) were examined in experiment 2. The HRV analysis for experiment 1 revealed that the steering conditions yielded significantly higher low-frequency (LF) power (ms<sup>2</sup>) than did the teleportation condition. In addition, the simulator sickness questionnaire (SSQ) results showed that the steering conditions produced stronger symptoms of cybersickness than did the teleportation conditions. For experiment 2, the yaw + roll and pitch + roll conditions had greater LF power (ms<sup>2</sup>) than did the other four conditions. However, the SSQ indicated that the level of cybersickness was higher for the roll, yaw + roll, and pitch + roll conditions than for the other rotation conditions. The participants completed the four and six conditions over a limited period across two experimental days. However, the overall HRV and SSQ results for both experiments indicated no explicit additive effects of sickness symptoms. Based on these outcomes, this study verified the relationship between cybersickness, HRV, and SSQ during navigation in a VE.",
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"content": "In this study, we explore the physiological effect of cybersickness using heart rate variability (HRV) and examine the relationship between the physiological and subjective measurement of cybersickness when navigating a virtual environment (VE). To achieve these objectives, we conducted two experiments. In experiment 1, four types of forwarding translation method (teleportation + embodied control [EC], steering + EC, teleportation + instrumental control [IC], and steering + IC) were compared, while six types of rotation (yaw, pitch, roll, yaw + pitch, yaw + roll, and pitch + roll) were examined in experiment 2. The HRV analysis for experiment 1 revealed that the steering conditions yielded significantly higher low-frequency (LF) power (ms<sup>2</sup>) than did the teleportation condition. In addition, the simulator sickness questionnaire (SSQ) results showed that the steering conditions produced stronger symptoms of cybersickness than did the teleportation conditions. For experiment 2, the yaw + roll and pitch + roll conditions had greater LF power (ms<sup>2</sup>) than did the other four conditions. However, the SSQ indicated that the level of cybersickness was higher for the roll, yaw + roll, and pitch + roll conditions than for the other rotation conditions. The participants completed the four and six conditions over a limited period across two experimental days. However, the overall HRV and SSQ results for both experiments indicated no explicit additive effects of sickness symptoms. Based on these outcomes, this study verified the relationship between cybersickness, HRV, and SSQ during navigation in a VE.",
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"abstract": "Cybersickness can be characterized by nausea, vertigo, headache, eye strain, and other discomforts when using virtual reality (VR) systems. The previously reported machine learning (ML) and deep learning (DL) algorithms for detecting (classification) and predicting (regression) VR cybersickness use black-box models; thus, they lack explainability. Moreover, VR sensors generate a massive amount of data, resulting in complex and large models. Therefore, having inherent explainability in cybersickness detection models can significantly improve the model’s trustworthiness and provide insight into why and how the ML/DL model amved at a specific decision. To address this issue, we present three explainable machine learning (xML) models to detect and predict cybersickness: 1) explainable boosting machine (EBM), 2) decision tree (DT), and 3) logistic regression (LR). We evaluate xML-based models with publicly available physiological and gameplay datasets for cybersickness. The results show that the EBM can detect cybersickness with an accuracy of 99.75% and 94.10% for the physiological and gameplay datasets, respectively. On the other hand, while predicting the cybersickness, EBM resulted in a Root Mean Square Error (RMSE) of 0.071 for the physiological dataset and 0.27 for the gameplay dataset. Furthermore, the EBM-based global explanation reveals exposure length, rotation, and acceleration as key features causing cybersickness in the gameplay dataset. In contrast, galvanic skin responses and heart rate are most significant in the physiological dataset. Our results also suggest that EBM-based local explanation can identify cybersickness-causing factors for individual samples. We believe the proposed xML-based cybersickness detection method can help future researchers understand, analyze, and design simpler cybersickness detection and reduction models.",
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"content": "Cybersickness can be characterized by nausea, vertigo, headache, eye strain, and other discomforts when using virtual reality (VR) systems. The previously reported machine learning (ML) and deep learning (DL) algorithms for detecting (classification) and predicting (regression) VR cybersickness use black-box models; thus, they lack explainability. Moreover, VR sensors generate a massive amount of data, resulting in complex and large models. Therefore, having inherent explainability in cybersickness detection models can significantly improve the model’s trustworthiness and provide insight into why and how the ML/DL model amved at a specific decision. To address this issue, we present three explainable machine learning (xML) models to detect and predict cybersickness: 1) explainable boosting machine (EBM), 2) decision tree (DT), and 3) logistic regression (LR). We evaluate xML-based models with publicly available physiological and gameplay datasets for cybersickness. The results show that the EBM can detect cybersickness with an accuracy of 99.75% and 94.10% for the physiological and gameplay datasets, respectively. On the other hand, while predicting the cybersickness, EBM resulted in a Root Mean Square Error (RMSE) of 0.071 for the physiological dataset and 0.27 for the gameplay dataset. Furthermore, the EBM-based global explanation reveals exposure length, rotation, and acceleration as key features causing cybersickness in the gameplay dataset. In contrast, galvanic skin responses and heart rate are most significant in the physiological dataset. Our results also suggest that EBM-based local explanation can identify cybersickness-causing factors for individual samples. We believe the proposed xML-based cybersickness detection method can help future researchers understand, analyze, and design simpler cybersickness detection and reduction models.",
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"normalizedAbstract": "Cybersickness can be characterized by nausea, vertigo, headache, eye strain, and other discomforts when using virtual reality (VR) systems. The previously reported machine learning (ML) and deep learning (DL) algorithms for detecting (classification) and predicting (regression) VR cybersickness use black-box models; thus, they lack explainability. Moreover, VR sensors generate a massive amount of data, resulting in complex and large models. Therefore, having inherent explainability in cybersickness detection models can significantly improve the model’s trustworthiness and provide insight into why and how the ML/DL model amved at a specific decision. To address this issue, we present three explainable machine learning (xML) models to detect and predict cybersickness: 1) explainable boosting machine (EBM), 2) decision tree (DT), and 3) logistic regression (LR). We evaluate xML-based models with publicly available physiological and gameplay datasets for cybersickness. The results show that the EBM can detect cybersickness with an accuracy of 99.75% and 94.10% for the physiological and gameplay datasets, respectively. On the other hand, while predicting the cybersickness, EBM resulted in a Root Mean Square Error (RMSE) of 0.071 for the physiological dataset and 0.27 for the gameplay dataset. Furthermore, the EBM-based global explanation reveals exposure length, rotation, and acceleration as key features causing cybersickness in the gameplay dataset. In contrast, galvanic skin responses and heart rate are most significant in the physiological dataset. Our results also suggest that EBM-based local explanation can identify cybersickness-causing factors for individual samples. We believe the proposed xML-based cybersickness detection method can help future researchers understand, analyze, and design simpler cybersickness detection and reduction models.",
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"fullName": "Ripan Kumar Kundu",
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"affiliation": "Northeastern University",
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"abstract": "Cybersickness is one of the primary challenges to the usability and acceptability of virtual reality (VR). Cybersickness can cause motion sickness-like discomforts, including disorientation, headache, nausea, and fatigue, both during and after the VR immersion. Prior research suggested a significant correlation between physiological signals and cybersickness severity, as measured by the simulator sickness questionnaire (SSQ). However, SSQ may not be suitable for automatic detection of cybersickness severity during immersion, as it is usually reported before and after the immersion. In this study, we introduced an automated approach for the detection and prediction of cybersickness severity from the user's physiological signals. We collected heart rate, breathing rate, heart rate variability, and galvanic skin response data from 31 healthy participants while immersed in a VR roller coaster simulation. We found a significant difference in the participants' physiological signals during their cybersickness state compared to their resting baseline. We compared a support vector machine classifier and three deep neural classifiers for cybersickness severity detection and prediction in two minutes' future, given the previous two minutes of physiological signals. Our proposed simplified convolutional long short-term memory classifier achieved an accuracy of 97.44% for detecting current cybersickness severity and 87.38% for predicting future cybersickness severity from the physiological signals.",
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"content": "Cybersickness is one of the primary challenges to the usability and acceptability of virtual reality (VR). Cybersickness can cause motion sickness-like discomforts, including disorientation, headache, nausea, and fatigue, both during and after the VR immersion. Prior research suggested a significant correlation between physiological signals and cybersickness severity, as measured by the simulator sickness questionnaire (SSQ). However, SSQ may not be suitable for automatic detection of cybersickness severity during immersion, as it is usually reported before and after the immersion. In this study, we introduced an automated approach for the detection and prediction of cybersickness severity from the user's physiological signals. We collected heart rate, breathing rate, heart rate variability, and galvanic skin response data from 31 healthy participants while immersed in a VR roller coaster simulation. We found a significant difference in the participants' physiological signals during their cybersickness state compared to their resting baseline. We compared a support vector machine classifier and three deep neural classifiers for cybersickness severity detection and prediction in two minutes' future, given the previous two minutes of physiological signals. Our proposed simplified convolutional long short-term memory classifier achieved an accuracy of 97.44% for detecting current cybersickness severity and 87.38% for predicting future cybersickness severity from the physiological signals.",
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"abstract": "With the development of consumer virtual reality (VR), people have increasing opportunities to experience cybersickness (CS) -- a kind of visually induced motion sickness (MS). In view of the importance of CS mitigation (CSM), this paper reviews the methods of electrostimulation-based CSM (e-CSM), broadly categorised as either “VR-centric” or “Human-centric”. “VR-centric” refers to approaches where knowledge regarding the visual motion being experienced in VR directly affects how the neurostimulation is delivered, whereas “Human-centric” approaches focus on the inhibition or enhancement of human functions per se without knowledge of the experienced visual motion. We DIFFERENT E-found that 1) most e-CSM approaches are based on visual-vestibular sensory conflict theory -- one of the generally-accepted aetiologies of MS, 2) the majority of eCSM approaches are vestibular system-centric, either stimulating it to compensate for the mismatched vestibular sensory responses, or inhibiting it to make an artificial and temporary dysfunction in vestibular sensory organs or cortical areas, 3) Vestibular sensory organbased solutions are able to mitigate CS with immediate effect, while the real-time effect of vestibular cortical areas-based methods remains unclear, due to limited public data, 4) Based on subjective assessment, VRcentric approaches could relieve all three kinds of symptoms (nausea, oculomotor, and disorientation), which appears superior to the human-centric ones that could only alleviate one of the symptom types or just have an overall relief effect. Finally, we propose promising future research directions in the development of e-CSM.",
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"content": "With the development of consumer virtual reality (VR), people have increasing opportunities to experience cybersickness (CS) -- a kind of visually induced motion sickness (MS). In view of the importance of CS mitigation (CSM), this paper reviews the methods of electrostimulation-based CSM (e-CSM), broadly categorised as either “VR-centric” or “Human-centric”. “VR-centric” refers to approaches where knowledge regarding the visual motion being experienced in VR directly affects how the neurostimulation is delivered, whereas “Human-centric” approaches focus on the inhibition or enhancement of human functions per se without knowledge of the experienced visual motion. We DIFFERENT E-found that 1) most e-CSM approaches are based on visual-vestibular sensory conflict theory -- one of the generally-accepted aetiologies of MS, 2) the majority of eCSM approaches are vestibular system-centric, either stimulating it to compensate for the mismatched vestibular sensory responses, or inhibiting it to make an artificial and temporary dysfunction in vestibular sensory organs or cortical areas, 3) Vestibular sensory organbased solutions are able to mitigate CS with immediate effect, while the real-time effect of vestibular cortical areas-based methods remains unclear, due to limited public data, 4) Based on subjective assessment, VRcentric approaches could relieve all three kinds of symptoms (nausea, oculomotor, and disorientation), which appears superior to the human-centric ones that could only alleviate one of the symptom types or just have an overall relief effect. Finally, we propose promising future research directions in the development of e-CSM.",
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"normalizedAbstract": "Vascular interventions are minimally invasive surgical procedures in which a physician navigates a catheter through a patient's vasculature to a desired destination in the patient's body. Since perception of relevant patient anatomy is limited in procedures of this sort, virtual reality and augmented reality systems have been developed to assist in 3D navigation. These systems often require user interaction, yet both of the physician's hands may already be busy performing the procedure. To address this need, we demonstrate hands-free interaction techniques that use voice and head tracking to allow the physician to interact with 3D virtual content on a head-worn display while making both hands available intraoperatively. Our approach supports rotation and scaling of 3D anatomical models that appear to reside in the surrounding environment through small head rotations using first-order control, and rigid body transformation of those models using zero-order control. This allows the physician to easily manipulate a model while it stays close to the center of their field of view.",
"fno": "08446259",
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"Biomedical Equipment",
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"Computer Graphics",
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"Three Dimensional Displays",
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"Surgery",
"Cameras",
"Augmented Reality",
"Hands Free Interaction",
"Augmented Reality",
"Vascular Interventions",
"Head Tracking",
"Head Worn Display"
],
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"affiliation": "Columbia University, USA",
"fullName": "Alon Grinshpoon",
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"affiliation": "Columbia University, USA",
"fullName": "Shirin Sadri",
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{
"affiliation": "Columbia University, USA",
"fullName": "Gabrielle J. Loeb",
"givenName": "Gabrielle J.",
"surname": "Loeb",
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"affiliation": "Columbia University, USA",
"fullName": "Carmine Elvezio",
"givenName": "Carmine",
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{
"affiliation": "Columbia University, USA",
"fullName": "Steven K. Feiner",
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"abstract": "Continuous virtual rotation is likely one of the biggest contributors to cybersickness, while simultaneously being necessary for many VR scenarios where the user, for instance, is sitting in a bus, at an office desk, or on a couch and therefore, limited in physical body rotation. A possible solution is discrete virtual rotation, such as already broadly accepted in translational movements (teleportation). In this work, we want to help increase the knowledge about discrete virtual rotations. We classify existing work and systematically investigate the two dimensionstarget(rotation)acquisition(selectionvs. directional)and body-based (yes vs. no) regarding their impact on the performance in a naive and a primed rotational search task, spatial orientation, and usability. We do find the novel virtual rotation selection most successful in both search tasks and no difference in the factor bodybased on spatial orientation.",
"abstracts": [
{
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"content": "Continuous virtual rotation is likely one of the biggest contributors to cybersickness, while simultaneously being necessary for many VR scenarios where the user, for instance, is sitting in a bus, at an office desk, or on a couch and therefore, limited in physical body rotation. A possible solution is discrete virtual rotation, such as already broadly accepted in translational movements (teleportation). In this work, we want to help increase the knowledge about discrete virtual rotations. We classify existing work and systematically investigate the two dimensionstarget(rotation)acquisition(selectionvs. directional)and body-based (yes vs. no) regarding their impact on the performance in a naive and a primed rotational search task, spatial orientation, and usability. We do find the novel virtual rotation selection most successful in both search tasks and no difference in the factor bodybased on spatial orientation.",
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"fno": "961700a693",
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"Cybersickness",
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"affiliation": "University of Trier,Germany",
"fullName": "Daniel Zielasko",
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"title": "Tell Me Where To Go: Voice-Controlled Hands-Free Locomotion for Virtual Reality Systems",
"normalizedTitle": "Tell Me Where To Go: Voice-Controlled Hands-Free Locomotion for Virtual Reality Systems",
"abstract": "As locomotion is an important factor in improving Virtual Reality (VR) immersion and usability, research in this area has been and continues to be a crucial aspect for the success of VR applications. In recent years, a variety of techniques have been developed and evaluated, ranging from abstract control, vehicle, and teleportation techniques to more realistic techniques such as motion, gestures, and gaze. However, when it comes to hands-free scenarios, for example to increase the overall accessibility of an application or in medical scenarios under sterile conditions, most of the announced techniques cannot be applied. This is where the use of speech as an intuitive means of navigation comes in handy. As systems become more capable of understanding and producing speech, voice interfaces become a valuable alternative for input on all types of devices. This takes the quality of hands-free interaction to a new level. However, intuitive user-assisted speech interaction is difficult to realize due to semantic ambiguities in natural language utterances as well as the high real-time requirements of these systems. In this paper, we investigate steering-based locomotion and selection-based locomotion using three speech-based, hands-free methods and compare them with leaning as an established alternative. Our results show that landmark-based locomotion is a convenient, fast, and intuitive way to move between locations in a VR scene. Furthermore, we show that in scenarios where landmarks are not available, number grid-based navigation is a successful solution. Based on this, we conclude that speech is a suitable alternative in hands-free scenar-ios, and exciting ideas are emerging for future work focused on developing hands-free ad hoc navigation systems for scenes where landmarks do not exist or are difficult to articulate or recognize.",
"abstracts": [
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"content": "As locomotion is an important factor in improving Virtual Reality (VR) immersion and usability, research in this area has been and continues to be a crucial aspect for the success of VR applications. In recent years, a variety of techniques have been developed and evaluated, ranging from abstract control, vehicle, and teleportation techniques to more realistic techniques such as motion, gestures, and gaze. However, when it comes to hands-free scenarios, for example to increase the overall accessibility of an application or in medical scenarios under sterile conditions, most of the announced techniques cannot be applied. This is where the use of speech as an intuitive means of navigation comes in handy. As systems become more capable of understanding and producing speech, voice interfaces become a valuable alternative for input on all types of devices. This takes the quality of hands-free interaction to a new level. However, intuitive user-assisted speech interaction is difficult to realize due to semantic ambiguities in natural language utterances as well as the high real-time requirements of these systems. In this paper, we investigate steering-based locomotion and selection-based locomotion using three speech-based, hands-free methods and compare them with leaning as an established alternative. Our results show that landmark-based locomotion is a convenient, fast, and intuitive way to move between locations in a VR scene. Furthermore, we show that in scenarios where landmarks are not available, number grid-based navigation is a successful solution. Based on this, we conclude that speech is a suitable alternative in hands-free scenar-ios, and exciting ideas are emerging for future work focused on developing hands-free ad hoc navigation systems for scenes where landmarks do not exist or are difficult to articulate or recognize.",
"__typename": "ArticleAbstractType"
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"normalizedAbstract": "As locomotion is an important factor in improving Virtual Reality (VR) immersion and usability, research in this area has been and continues to be a crucial aspect for the success of VR applications. In recent years, a variety of techniques have been developed and evaluated, ranging from abstract control, vehicle, and teleportation techniques to more realistic techniques such as motion, gestures, and gaze. However, when it comes to hands-free scenarios, for example to increase the overall accessibility of an application or in medical scenarios under sterile conditions, most of the announced techniques cannot be applied. This is where the use of speech as an intuitive means of navigation comes in handy. As systems become more capable of understanding and producing speech, voice interfaces become a valuable alternative for input on all types of devices. This takes the quality of hands-free interaction to a new level. However, intuitive user-assisted speech interaction is difficult to realize due to semantic ambiguities in natural language utterances as well as the high real-time requirements of these systems. In this paper, we investigate steering-based locomotion and selection-based locomotion using three speech-based, hands-free methods and compare them with leaning as an established alternative. Our results show that landmark-based locomotion is a convenient, fast, and intuitive way to move between locations in a VR scene. Furthermore, we show that in scenarios where landmarks are not available, number grid-based navigation is a successful solution. Based on this, we conclude that speech is a suitable alternative in hands-free scenar-ios, and exciting ideas are emerging for future work focused on developing hands-free ad hoc navigation systems for scenes where landmarks do not exist or are difficult to articulate or recognize.",
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"affiliation": "University of Jena",
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"affiliation": "University Hospital Cologne",
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"abstract": "To ensure 360-degree video's continuous playback and reduce the bandwidth waste, predicting user's future fixation is indispensable. However, existing methods concentrate either on user's motion information or content information. None of them consider users watching behaviors' inconsistency which embodies user's attention distribution more explicitly. So in this paper, we exploit Cross-Users Behaviors for viewport prediction in 360-degree video adaptive streaming, namely CUB360, trying to concurrently consider user's personalized information and cross-users behaviors information to predict future viewport. Besides, we use a QoE-driven framework to optimize existing video streaming approaches and propose a general algorithm aiming at solving the NP problem at a low complexity. Extensive experimental results over real datasets demonstrate that compared with traditional adaptive streaming method, our proposal can significantly boost the prediction accuracy by 20.2% absolutely and 48.1 % relatively. Besides, the mean quality can get 30.28% gain while quality variance can be reduced by 29.89%.",
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"abstract": "Volumetric video (VV) recently emerges as a new form of video application providing a photorealistic immersive 3D viewing experience with 6 degree-of-freedom (DoF), which empowers many applications such as VR, AR, and Metaverse. A key problem therein is how to stream the enormous size VV through the network with limited bandwidth. Existing works mostly focused on predicting the viewport for a tiling-based adaptive VV streaming, which however only has quite a limited effect on resource saving. We argue that the content repeatability in the viewport can be further leveraged, and for the first time, propose a client-side cache-assisted strategy that aims to buffer the repeatedly appearing VV tiles in the near future so as to reduce the redundant VV content transmission. The key challenges exist in three aspects, including (1) feature extraction and mining in 6 DoF VV context, (2) accurate long-term viewing pattern estimation and (3) optimal caching scheduling with limited capacity. In this paper, we propose CaV3, an integrated cache-assisted viewport adaptive VV streaming framework to address the challenges. CaV3 employs a Long-short term Sequential prediction model (LSTSP) that achieves accurate short-term, mid-term and long-term viewing pattern prediction with a multi-modal fusion model by capturing the viewer's behavior inertia, current attention, and subjective intention. Besides, CaV3 also contains a contextual MAB-based caching adaptation algorithm (CCA) to fully utilize the viewing pattern and solve the optimal caching problem with a proved upper bound regret. Compared to existing VV datasets only containing single or co-located objects, we for the first time collect a comprehensive dataset with sufficient practical unbounded 360° scenes. The extensive evaluation of the dataset confirms the superiority of CaV3, which outperforms the SOTA algorithm by 15.6%-43% in viewport prediction and 13%-40% in system utility.",
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"abstract": "Live virtual reality streaming (a.k.a., 360-degree video streaming) is gaining popularity recently with its rapid growth in the consumer market. However, the huge bandwidth required by delivering the 360-degree frames becomes the bottleneck, keeping this application from a wider range of deployment. Research efforts have been carried out to solve the bandwidth problem by predicting the user's viewport of interest and selectively streaming a part of the whole frame. However, currently most of the viewport prediction approaches cannot address the unique challenges in the live streaming scenario, where there is no historical user or video traces to build the prediction model. In this paper, we explore the opportunity of leveraging convolutional neural network (CNN) to predict the user's viewport in live streaming by modifying the workflow of the CNN application and the training/testing process. The evaluation results reveal that the CNN-based method could achieve a high prediction accuracy with low bandwidth usage and low timing overhead.",
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"content": "Live virtual reality streaming (a.k.a., 360-degree video streaming) is gaining popularity recently with its rapid growth in the consumer market. However, the huge bandwidth required by delivering the 360-degree frames becomes the bottleneck, keeping this application from a wider range of deployment. Research efforts have been carried out to solve the bandwidth problem by predicting the user's viewport of interest and selectively streaming a part of the whole frame. However, currently most of the viewport prediction approaches cannot address the unique challenges in the live streaming scenario, where there is no historical user or video traces to build the prediction model. In this paper, we explore the opportunity of leveraging convolutional neural network (CNN) to predict the user's viewport in live streaming by modifying the workflow of the CNN application and the training/testing process. The evaluation results reveal that the CNN-based method could achieve a high prediction accuracy with low bandwidth usage and low timing overhead.",
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"abstract": "Live virtual reality (VR) streaming has become a popular and trending video application in the consumer market providing users with 360-degree, immersive viewing experiences. To provide premium quality of experience, VR streaming faces unique challenges due to the significantly increased bandwidth consumption. To address the bandwidth challenge, VR video viewport prediction has been proposed as a viable solution, which predicts and streams only the user’s viewport of interest with high quality to the VR device. However, most of the existing viewport prediction approaches target only the video-on-demand (VOD) use cases, requiring offline processing of the historical video and/or user data that are not available in the live streaming scenario. In this work, we develop a novel viewport prediction approach for live VR streaming, which only requires video content and user data in the current viewing session. To address the challenges of insufficient training data and real-time processing, we propose a live VR-specific deep learning mechanism, namely LiveDeep, to create the online viewport prediction model and conduct real-time inference. LiveDeep employs a hybrid approach to address the unique challenges in live VR streaming, involving (1) an alternate online data collection, labeling, training, and inference schedule with controlled feedback loop to accommodate for the sparse training data; and (2) a mixture of hybrid neural network models to accommodate for the inaccuracy caused by a single model. We evaluate LiveDeep using 48 users and 14 VR videos of various types obtained from a public VR user head movement dataset. The results indicate around 90% prediction accuracy, around 40% bandwidth savings, and premium processing time, which meets the bandwidth and real-time requirements of live VR streaming.",
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"content": "Live virtual reality (VR) streaming has become a popular and trending video application in the consumer market providing users with 360-degree, immersive viewing experiences. To provide premium quality of experience, VR streaming faces unique challenges due to the significantly increased bandwidth consumption. To address the bandwidth challenge, VR video viewport prediction has been proposed as a viable solution, which predicts and streams only the user’s viewport of interest with high quality to the VR device. However, most of the existing viewport prediction approaches target only the video-on-demand (VOD) use cases, requiring offline processing of the historical video and/or user data that are not available in the live streaming scenario. In this work, we develop a novel viewport prediction approach for live VR streaming, which only requires video content and user data in the current viewing session. To address the challenges of insufficient training data and real-time processing, we propose a live VR-specific deep learning mechanism, namely LiveDeep, to create the online viewport prediction model and conduct real-time inference. LiveDeep employs a hybrid approach to address the unique challenges in live VR streaming, involving (1) an alternate online data collection, labeling, training, and inference schedule with controlled feedback loop to accommodate for the sparse training data; and (2) a mixture of hybrid neural network models to accommodate for the inaccuracy caused by a single model. We evaluate LiveDeep using 48 users and 14 VR videos of various types obtained from a public VR user head movement dataset. The results indicate around 90% prediction accuracy, around 40% bandwidth savings, and premium processing time, which meets the bandwidth and real-time requirements of live VR streaming.",
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"abstract": "With a growing emphasis on human identification, iris recognition as a biometric identification has recently received increasing attention. The first step in iris recognition is segmentation. In this paper a new segmentation approach is offered which does not use any information of color or texture as the segmentation cues. Instead, we use random sample consensus method to fit an ellipse or a circle to the edge information of iris boundary. The presented approach is robust against the iris texture variations and other trouble makers like eyelid and specularity effect in pupil area. The extracted curves in this method are more conformable with iris boundaries than the curves obtained by other conventional methods.",
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"content": "Restore damaged face images is an important issue for ancient murals conservation and research. Since the structure and style of facial image varies with identity, dynasties, location and artists, it is a time consuming and challenging job for researchers to achieve objective and precise restoration. In this paper, we propose a novel facial image completion method based on a semantic learning framework. By few user interactions, our approach can fill missing regions with a reconstructed face which is based on candidate images with high semantic similarity. Since most of the ancient murals contain deteriorations, to archive visual pleasant result, we adopt a PCA based region blending method to produce a seamless completion. Moreover, for the criteria of mural restoration are evaluated in the view of both scientific and art, our completion process can provide the researcher an opportunity to preview a diverse set of the final results virtually before they actually start their job. The real applications in Dunhuang mural show the efficiency and effectiveness of our approach. Furthermore, these experiments suggested the potential applications for restoration facial image in ancient line drawing.",
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