MagicEyes: A Large Scale Eye Gaze Estimation Dataset for Mixed Reality

PubDate: Mar 2020

Teams: Magic Leap

Writers: Zhengyang Wu, Srivignesh Rajendran, Tarrence van As, Joelle Zimmermann, Vijay Badrinarayanan, Andrew Rabinovich

PDF: MagicEyes: A Large Scale Eye Gaze Estimation Dataset for Mixed Reality

Abstract

With the emergence of Virtual and Mixed Reality (XR) devices, eye tracking has received significant attention in the computer vision community. Eye gaze estimation is a crucial component in XR – enabling energy efficient rendering, multi-focal displays, and effective interaction with content. In head-mounted XR devices, the eyes are imaged off-axis to avoid blocking the field of view. This leads to increased challenges in inferring eye related quantities and simultaneously provides an opportunity to develop accurate and robust learning based approaches. To this end, we present MagicEyes, the first large scale eye dataset collected using real MR devices with comprehensive ground truth labeling. MagicEyes includes 587 subjects with 80,000 images of human-labeled ground truth and over 800,000 images with gaze target labels. We evaluate several state-of-the-art methods on MagicEyes and also propose a new multi-task EyeNet model designed for detecting the cornea, glints and pupil along with eye segmentation in a single forward pass.