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

Abstract

We present TEyeD, the world's largest unified public data set of eye images taken with head-mounted devices. TEyeD was acquired with seven different head-mounted eye trackers. Among them, two eye trackers were integrated into virtual reality (VR) or augmented reality (AR) devices. The images in TEyeD were obtained from various tasks, including car rides, simulator rides, outdoor sports activities, and daily indoor activities. The data set includes 2D&3D landmarks, semantic segmentation, 3D eyeball annotation and the gaze vector and eye movement types for all images. Landmarks and semantic segmentation are provided for the pupil, iris and eyelids. Video lengths vary from a few minutes to several hours. With more than 20 million carefully annotated images, TEyeD provides a unique, coherent resource and a valuable foundation for advancing research in the field of computer vision, eye tracking and gaze estimation in modern VR and AR applications. Data and code at https://unitc-my.sharepoint.com/:f:/g/personal/iitfu01_cloud_uni-tuebingen_de/EvrNPdtigFVHtCMeFKSyLlUBepOcbX0nEkamweeZa0s9SQ?e=fWEvPp.

Real-Time Gaze Tracking with Event-Driven Eye Segmentation

“…TEyeD Another dataset we used in our evaluation is the TEyeD dataset, which has manually annotated segmentation and gaze ground truth [32]. We select 2 sequences from the dataset.…”

On-Sensor Binarized Fully Convolutional Neural Network with A Pixel Processor Array

“…Considering the light weight and low-power consumption of the PPA chip and the increasing popularity of virtual and augmented reality (VR/AR) based on the eye movement, it is promising to mount the PPA chip to a wearable device such as glasses in the near future, hence we explore the pupil detection with the proposed binarized FCN based on the public dataset of eye images: TEyeD [16]. Fig.…”

EyeNeRF: A Hybrid Representation for Photorealistic Synthesis, Animation and Relighting of Human Eyes

“…While there are several publicly available eye image datasets, they are unfortunately not directly suited for our purposes of view, gaze, and illumination synthesis. The majority of these datasets are tailored for the task of gaze-tracking [Fuhl et al 2021;Fusek 2018;Kim et al 2019;Tonsen et al 2017;Wood et al 2015;Wu et al 2020;] while others cater to different problems such as pupil detection [Tonsen et al 2016], eye closure detection [Song et al 2014] or eyelash segmentation [Xiao et al 2021]. While there are datasets that aim at modeling high-quality eyes and periocular region [Bérard et al 2019[Bérard et al , 2014, these are not suited for relighting purposes.…”