We introduce a new gaze tracker for Head Mounted Displays (HMDs). We modify two off-the-shelf HMDs to be gaze-aware using Light Emitting Diodes (LEDs). Our key contribution is to exploit the sensing capability of LEDs to create low-power gaze tracker for virtual reality (VR) applications. This yields a simple approach using minimal hardware to achieve good accuracy and low latency using light-weight supervised Gaussian Process Regression (GPR) running on a mobile device. With our hardware, we show that Minkowski distance measure based GPR implementation outperforms the commonly used radial basis function-based support vector regression (SVR) without the need to precisely determine free parameters. We show that our gaze estimation method does not require complex dimension reduction techniques, feature extraction, or distortion corrections due to off-axis optical paths. We demonstrate two complete HMD prototypes with a sample eye-tracked application, and report on a series of subjective tests using our prototypes.
Max Planck Institute for Informatics（马克斯·普朗克信息学研究所）
3D gaze information is important for scene-centric attention analysis but accurate estimation and analysis of 3D gaze in real-world environments remains challenging. We present a novel 3D gaze estimation method for monocular head-mounted eye trackers. In contrast to previous work, our method does not aim to infer 3D eyeball poses but directly maps 2D pupil positions to 3D gaze directions in scene camera coordinate space. We first provide a detailed discussion of the 3D gaze estimation task and summarize different methods, including our own. We then evaluate the performance of different 3D gaze…
A head-mounted display (HMD) is a portable and interactive display device. With the development of 5G technology, it may become a general-purpose computing platform in the future. Human-computer interaction (HCI) technology for HMDs has also been of significant interest in recent years. In addition to tracking gestures and speech, tracking human eyes as a means of interaction is highly effective. In this paper, we propose two UnityEyes-based convolutional neural network models, UEGazeNet and UEGazeNet*, which can be used for input images with low resolution and high resolution, respectively. T…
University of Science and Technology of China（中国科学技术大学）；Carnegie Mellon University（卡内基梅隆大学）
In recent years, head-mounted near-eye display devices have become the key hardware foundation for virtual reality and augmented reality. Thus head-mounted gaze tracking technology has received attention as an essential part of human-computer interaction. However, unavoidable slippage of head-mounted devices (HMD) often results higher gaze tracking errors and hinders the practical usage of HMD. To tackle this problem, we propose a slippage-robust gaze tracking for near-eye display method based on the aspheric eyeball model and accurately compute the eyeball optical axis and rotation center. We…
UC San Diego（加州大学圣地亚哥分校）；Microsoft Research（微软研究院）
We present a novel, automatic eye gaze tracking scheme inspired by smooth pursuit eye motion while playing mobile games or watching virtual reality contents. Our algorithm continuously calibrates an eye tracking system for a head mounted display. This eliminates the need for an explicit calibration step and automatically compensates for small movements of the headset with respect to the head. The algorithm finds correspondences between corneal motion and screen space motion, and uses these to generate Gaussian Process Regression models. A combination of those models provides a continuous mappi…
The study of human gaze behavior in natural contexts requires algorithms for gaze estimation that are robust to a wide range of imaging conditions. However, algorithms often fail to identify features such as the iris and pupil centroid in the presence of reflective artifacts and occlusions. Previous work has shown that convolutional networks excel at extracting gaze features despite the presence of such artifacts. However, these networks often perform poorly on data unseen during training. This work follows the intuition that jointly training a convolutional network with multiple datasets lear…
University of California Berkeley（加州大学伯克利分校）；Stanford University（斯坦福大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）
The cameras in modern gaze-tracking systems suffer from fundamental bandwidth and power limitations, constraining data acquisition speed to 300 Hz realistically. This obstructs the use of mobile eye trackers to perform, e.g., low latency predictive rendering, or to study quick and subtle eye motions like microsaccades using head-mounted devices in the wild. Here, we propose a hybrid frame-event-based near-eye gaze tracking system offering update rates beyond 10,000 Hz with an accuracy that matches that of high-end desktop-mounted commercial trackers when evaluated in the same conditions. Our s…
Head-mounted eye trackers promise convenient access to reliable gaze data in unconstrained environments. Due to several limitations, however, often they can only partially deliver on this promise. Among those are the following: (i) the necessity of performing a device setup and calibration prior to every use of the eye tracker, (ii) a lack of robustness of gaze-estimation results against perturbations, such as outdoor lighting conditions and unavoidable slippage of the eye tracker on the head of the subject, and (iii) behavioral distortion resulting from social awkwardness, due to the unnatura…
Toddlers' Active Gaze Behavior Supports Self-Supervised Object Learning
Frankfurt Institute for Advanced Studies（弗赖堡高级研究所）；Xidian-FIAS International Joint Research Center（西电-弗赖堡国际联合研究中心）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
Eye gaze estimation and simultaneous semantic understanding of a user through eye images is a crucial component in Virtual and Mixed Reality; enabling energy efficient rendering, multi-focal displays and effective interaction with 3D content. In head-mounted VR/MR devices the eyes are imaged off-axis to avoid blocking the user's gaze, this view-point makes drawing eye related inferences very challenging. In this work, we present EyeNet, the first single deep neural network which solves multiple heterogeneous tasks related to eye gaze estimation and semantic user understanding for an off-axis c…
Incorporating the physical environment is essential for a complete understanding of human behavior in unconstrained every-day tasks. This is especially important in ego-centric tasks where obtaining 3 dimensional information is both limiting and challenging with the current 2D video analysis methods proving insufficient. Here we demonstrate a proof-of-concept system which provides real-time 3D mapping and semantic labeling of the local environment from an ego-centric RGB-D video-stream with 3D gaze point estimation from head mounted eye tracking glasses. We augment existing work in Semantic Si…
TEyeD: Over 20 million real-world eye images with Pupil, Eyelid, and Iris 2D and 3D Segmentations, 2D and 3D Landmarks, 3D Eyeball, Gaze Vector, and Eye Movement Types
Visible Light Positioning With Lamé Curve LEDs: A Generic Approach for Camera Pose Estimation
Beijing Key Laboratory of Network System Architecture and Convergence, School of Information and Communication Engineering, Beijing University of Posts and Telecommunications（北京网络系统架构与融合重点实验室，信息与通信工程学院，北京邮电大学）；Institute of Information Engineering, Chinese Academy of Sciences（信息工程研究所，中国科学院）；College of Physics and Electronic Engineering, Shanxi University（物理与电子工程学院，山西大学）；School of Electronic Information and Artificial Intelligence, West Anhui University（电子信息与人工智能学院，皖西学院）
Fraunhofer Institute for Computer Graphics Research IGD（弗劳恩霍夫计算机图形研究所（IGD））；TU Darmstadt（达姆施塔特工业大学）；The Norwegian Colour and Visual Computing Laboratory, NTNU（挪威色彩与视觉计算实验室，挪威科技大学（NTNU））；Norwegian Biometrics Laboratory, NTNU（挪威生物识别实验室，挪威科技大学（NTNU））；Mathematical and Applied Visual Computing（数学与应用视觉计算（TU Darmstadt））；Mathematical and Applied Visual Computing, TU Darmstadt（数学与应用视觉计算，达姆施塔特工业大学）
Augmented and virtual reality is being deployed in different fields of applications. Such applications might involve accessing or processing critical and sensitive information, which requires strict and continuous access control. Given that Head-Mounted Displays (HMD) developed for such applications commonly contains internal cameras for gaze tracking purposes, we evaluate the suitability of such setup for verifying the users through iris recognition. In this work, we first evaluate a set of iris recognition algorithms suitable for HMD devices by investigating three well-established handcrafte…
ETH Zürich（苏黎世联邦理工学院）；Microsoft（微软）；Meta Reality Labs Research（Meta现实实验室）
Understanding social interactions from egocentric views is crucial for many applications, ranging from assistive robotics to AR/VR. Key to reasoning about interactions is to understand the body pose and motion of the interaction partner from the egocentric view. However, research in this area is severely hindered by the lack of datasets. Existing datasets are limited in terms of either size, capture/annotation modalities, ground-truth quality, or interaction diversity. We fill this gap by proposing EgoBody, a novel large-scale dataset for human pose, shape and motion estimation from egocentric…
