The Eye in Extended Reality: A Survey on Gaze Interaction and Eye Tracking in Head-worn Extended Reality

Plopski, Alexander; Hirzle, Teresa; Norouzi, Nahal; Qian, Long; Bruder, Gerd; Langlotz, Tobias

doi:10.1145/3491207

Cited by 82 publications

(24 citation statements)

References 260 publications

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“…Driving, which is commonly considered as a goal-directed activity [7,8], is taken as the sensorimotor task in our psychophysical experiments. Due to its repeatable usability, high safety and good performance, (head-worn) virtual reality technique has become a popular paradigm to study gaze shifts and sensorimotor tasks [9,[44][45][46]. Therefore, our study is done based on head-worn virtual reality.…”

Section: Experiments Virtual Reality Environment and Taskmentioning

confidence: 99%

Transfer Entropy Based Causality from Head Motion to Eye Movement for Visual Scanning in Virtual Driving

Zhang

Peng

et al. 2022

Preprint

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Visual scanning is achieved by eye movement control for visual information acquisition and cognitive processing, which plays a critical role in undertaking common sensorimotor tasks such as driving. The specific coordination of the head and eyes, with head motions temporally preceding eye movements, is an important human behavior to make a key contribution to goal-directed visual scanning and sensorimotor driving. In this paper, we put forward a proposal of philosophy that this specific coordination of the head and eyes essentially indicates a unidirectional causality from head motion to eye movement. We propose to investigate transfer entropy for defining a quantitative measure of this unidirectional head-eye causality. A normalized version of the proposed causality measure is introduced for taking a role as an assessment proxy of driving. The plain transfer entropy-based definition has shown its statistical significance as the measure of causality and, the normalized version has demonstrated its good effectiveness for the evaluation of driving performance, with the verification in virtual reality-based psychophysical studies. This paper successfully suggests that the quantitative exploitation of causality based on the specific coordination of the head and eyes offers an effective approach to behaviometrics of visual scanning and sensorimotor activity.

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Section: Experiments Virtual Reality Environment and Taskmentioning

confidence: 99%

Transfer Entropy Based Causality from Head Motion to Eye Movement for Visual Scanning in Virtual Driving

Zhang

Peng

et al. 2022

Preprint

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“…With the advent of widespread and affordable consumer VR hardware, more and more head-mounted displays (HMDs) are now starting to include eye tracking technology out of the box. Besides the growing use in behavioral research, eye tracking in VR can enable a variety of different use cases (Duchowski, 2002(Duchowski, , 2017Plopski et al, 2022). To highlight just some examples: Approaches such as foveated rendering allow higher visual fidelity and reduce rendering demands and power consumption for VR graphics (Albert et al, 2017;Patney et al, 2016), gaze-based pointing and target selection can be utilized to create intuitive and multimodal methods of interaction (Jacob & Stellmach, 2016;Majaranta & Bulling, 2014;Plopski et al, 2022;Tanriverdi & Jacob, 2000), and knowledge about a user's current gaze direction can enable novel ways to experience and imperceptibly manipulate a virtual environment (e.g., Langbehn et al, 2018;Marwecki et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Besides the growing use in behavioral research, eye tracking in VR can enable a variety of different use cases (Duchowski, 2002(Duchowski, , 2017Plopski et al, 2022). To highlight just some examples: Approaches such as foveated rendering allow higher visual fidelity and reduce rendering demands and power consumption for VR graphics (Albert et al, 2017;Patney et al, 2016), gaze-based pointing and target selection can be utilized to create intuitive and multimodal methods of interaction (Jacob & Stellmach, 2016;Majaranta & Bulling, 2014;Plopski et al, 2022;Tanriverdi & Jacob, 2000), and knowledge about a user's current gaze direction can enable novel ways to experience and imperceptibly manipulate a virtual environment (e.g., Langbehn et al, 2018;Marwecki et al, 2019). All of these applications have different requirements relating to the quality of eye tracking data, such as high spatial accuracy and precision of the estimated gaze position in the case of gaze selection and interaction (Feit et al, 2017;Orquin & Holmqvist, 2018;Schuetz et al, 2019Schuetz et al, , 2020, or very low latency between performing an eye movement and the corresponding change in a visual scene for foveated rendering (Albert et al, 2017;Stein et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Eye tracking in virtual reality: Vive pro eye spatial accuracy, precision, and calibration reliability

Schütz

Fiehler

2022

JEMR

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A growing number of virtual reality devices now include eye tracking technology, which can facilitate oculomotor and cognitive research in VR and enable use cases like foveated rendering. These applications require different tracking performance, often measured as spatial accuracy and precision. While manufacturers report data quality estimates for their devices, these typically represent ideal performance and may not reflect real-world data quality. Additionally, it is unclear how accuracy and precision change across sessions within the same participant or between devices, and how performance is influenced by vision correction. Here, we measured spatial accuracy and precision of the Vive Pro Eye built-in eye tracker across a range of 30 visual degrees horizontally and vertically. Participants completed ten measurement sessions over multiple days, allowing to evaluate calibration reliability. Accuracy and precision were highest for central gaze and decreased with greater eccentricity in both axes. Calibration was successful in all participants, including those wearing contacts or glasses, but glasses yielded significantly lower performance. We further found differences in accuracy (but not precision) between two Vive Pro Eye headsets, and estimated participants’ inter-pupillary distance. Our metrics suggest high calibration reliability and can serve as a baseline for expected eye tracking performance in VR experiments.

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“…This technology is being studied in fields such as education, engineering, gaming, and medicine [ 1 , 2 , 3 , 4 , 5 ]. Research has been conducted to identify or train reality-based users’ behaviors by applying it to automobiles, wheelchairs, and augmented reality [ 6 , 7 , 8 ]. However, research on eye-gaze direction-tracking is not limited to recognition and the analysis of physical changes in eye movement.…”

Section: Introductionmentioning

confidence: 99%

“…However, research on eye-gaze direction-tracking is not limited to recognition and the analysis of physical changes in eye movement. Recently, the scope of eye-gaze direction-tracking studies was expanded to the understanding of human psychology, behavior, emotions, and intentions [ 8 , 9 ]. Thus, eye-gaze direction-tracking studies commonly require the stability, accuracy, and precision of eye-gaze direction detection, as well as fast response speed.…”

Section: Introductionmentioning

confidence: 99%

Improving Performance of the Human Pupil Orbit Model (HPOM) Estimation Method for Eye-Gaze Tracking

Lee

Jeong

Kim

et al. 2022

Sensors

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Eye-gaze direction-tracking technology is used in fields such as medicine, education, engineering, and gaming. Stability, accuracy, and precision of eye-gaze direction-tracking are demanded with simultaneous upgrades in response speed. In this study, a method is proposed to improve the speed with decreases in the system load and precision in the human pupil orbit model (HPOM) estimation method. The new method was proposed based on the phenomenon that the minor axis of the elliptical-deformed pupil always pointed toward the rotational center presented in various eye-gaze direction detection studies and HPOM estimation methods. Simulation experimental results confirmed that the speed was improved by at least 74 times by consuming less than 7 ms compared to the HPOM estimation. The accuracy of the eye’s ocular rotational center point showed a maximum error of approximately 0.2 pixels on the x-axis and approximately 8 pixels on the y-axis. The precision of the proposed method was 0.0 pixels when the number of estimation samples (ES) was 7 or less, which showed results consistent with those of the HPOM estimation studies. However, the proposed method was judged to work conservatively against the allowable angle error (AAE), considering that the experiment was conducted under the worst conditions and the cost used to estimate the final model. Therefore, the proposed method could estimate HPOM with high accuracy and precision through AAE adjustment according to system performance and the usage environment.

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The Eye in Extended Reality: A Survey on Gaze Interaction and Eye Tracking in Head-worn Extended Reality

Cited by 82 publications

References 260 publications

Transfer Entropy Based Causality from Head Motion to Eye Movement for Visual Scanning in Virtual Driving

Transfer Entropy Based Causality from Head Motion to Eye Movement for Visual Scanning in Virtual Driving

Eye tracking in virtual reality: Vive pro eye spatial accuracy, precision, and calibration reliability

Improving Performance of the Human Pupil Orbit Model (HPOM) Estimation Method for Eye-Gaze Tracking

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