论文标题
RIT-EYES：渲染近眼图像用于吸引人的应用程序
RIT-Eyes: Rendering of near-eye images for eye-tracking applications
论文作者
论文摘要
用于基于视频的眼睛跟踪的深度神经网络已经证明了对嘈杂的环境，杂散反射和低分辨率的韧性。但是，要训练这些网络，需要大量手动注释的图像。为了减轻手动标记的繁琐过程，使用计算机图形渲染来自动在各种条件下生成大量注释的眼睛图像。在这项工作中，我们引入了一个合成的眼睛图像生成平台，该平台通过添加诸如主动变形虹膜，非球形角膜，视网膜逆转反射，凝视协调的眼睛盖的眼睛变形和眨眼等功能来改善以前的工作。为了展示我们平台的实用性，我们渲染图像反映了两个公开可用数据集（Nvgaze and Opens）中固有的凝视分布。我们还报告了对渲染图像训练并在原始数据集上进行了测试的两个语义分割体系结构（SEGNET和RITNET）的性能。
Deep neural networks for video-based eye tracking have demonstrated resilience to noisy environments, stray reflections, and low resolution. However, to train these networks, a large number of manually annotated images are required. To alleviate the cumbersome process of manual labeling, computer graphics rendering is employed to automatically generate a large corpus of annotated eye images under various conditions. In this work, we introduce a synthetic eye image generation platform that improves upon previous work by adding features such as an active deformable iris, an aspherical cornea, retinal retro-reflection, gaze-coordinated eye-lid deformations, and blinks. To demonstrate the utility of our platform, we render images reflecting the represented gaze distributions inherent in two publicly available datasets, NVGaze and OpenEDS. We also report on the performance of two semantic segmentation architectures (SegNet and RITnet) trained on rendered images and tested on the original datasets.
