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.

中文翻译：

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MagicEyes：用于混合现实的大规模眼睛注视估计数据集

随着虚拟和混合现实 (XR) 设备的出现，眼动追踪在计算机视觉社区中受到了极大的关注。眼睛注视估计是 XR 中的一个关键组件——实现节能渲染、多焦点显示以及与内容的有效交互。在头戴式 XR 设备中，眼睛离轴成像以避免阻挡视野。这导致在推断与眼睛相关的数量方面面临更多挑战，同时为开发基于学习的准确而稳健的方法提供了机会。为此，我们展示了 MagicEyes，这是第一个使用真实 MR 设备收集的大规模眼睛数据集，具有全面的地面实况标记。MagicEyes 包括价值 587 美元的主题和价值 80,000 美元的人工标记地面真相图像和超过 800,000 美元的带有注视目标标签的图像。