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Eye tracking has already made its way to current commercial wearable display devices, and is becoming increasingly important for virtual and augmented reality applications. However, the existing model-based eye tracking solutions are not capable of conducting very accurate gaze angle measurements, and may not be sufficient to solve challenging display problems such as pupil steering or eyebox expansion. In this paper, we argue that accurate detection and localization of pupil in 3D space is a necessary intermediate step in model-based eye tracking. Existing methods and datasets either ignore evaluating the accuracy of 3D pupil localization or evaluate it only on synthetic data. To this end, we capture the first 3D pupilgaze-measurement dataset using a high precision setup with head stabilization and release it as the first benchmark dataset to evaluate both 3D pupil localization and gaze tracking methods. Furthermore, we utilize an advanced eye model to replace the commonly used oversimplified eye model. Leveraging the eye model, we propose a novel 3D pupil localization method with a deep learning-based corneal refraction correction. We demonstrate that our method outperforms the state-of-the-art works by reducing the 3D pupil localization error by 47.5% and the gaze estimation error by 18.7%. Our dataset and codes can be found here: link. 

Recently, image-to-image translation (I2I) has met with great success in computer vision, but few works have paid attention to the geometric changes that occur during translation. The geometric changes are necessary to reduce the geometric gap between domains at the cost of breaking correspondence between translated images and original ground truth. We propose a novel geometry-aware semi-supervised method to preserve this correspondence while still allowing geometric changes. The proposed method takes a synthetic image-mask pair as input and produces a corresponding real pair. We also utilize an objective function to ensure consistent geometric movement of the image and mask through the translation. Extensive experiments illustrate that our method yields a 11.23% higher mean Intersection-Over-Union than the current methods on the downstream eye segmentation task. The generated image has a 15.9% decrease in Frechet Inception Distance indicating higher image quality. 

We present a personalized, comprehensive eye-tracking solution based on tracking higher-order Purkinje images, suited explicitly for eyeglasses-style AR and VR displays. Existing eye-tracking systems for near-eye applications are typically designed to work for an on-axis configuration and rely on pupil center and corneal reflections (PCCR) to estimate gaze with an accuracy of only about 0.5°to 1°. These are often expensive, bulky in form factor, and fail to estimate monocular accommodation, which is crucial for focus adjustment within the AR glasses. Our system independently measures the binocular vergence and monocular accommodation using higher-order Purkinje reflections from the eye, extending the PCCR based methods. We demonstrate that these reflections are sensitive to both gaze rotation and lens accommodation and model the Purkinje images’ behavior in simulation. We also design and fabricate a user-customized eye tracker using cheap off-the-shelf cameras and LEDs. We use an end-to-end convolutional neural network (CNN) for calibrating the eye tracker for the individual user, allowing for robust and simultaneous estimation of vergence and accommodation. Experimental results show that our solution, specifically catering to individual users, outperforms state-of-the-art methods for vergence and depth estimation, achieving an accuracy of 0.3782°and 1.108 cm respectively.