SynthesEyes Dataset

Images of the eye are key in several computer vision problems, such as shape registration and gaze estimation. Recent large-scale supervised methods for these problems require time-consuming data collection and manual annotation, which can be unreliable. We propose synthesizing perfectly labelled photo-realistic training data in a fraction of the time. We used computer graphics techniques to build a collection of dynamic eye-region models from head scan geometry. These were randomly posed to synthesize close-up eye images for a wide range of head poses, gaze directions, and illumination conditions. Finally, we demonstrate the benefits of our synthesized training data (SynthesEyes) by out-performing state-of-the-art methods for eye-shape registration as well as cross-dataset appearance-based gaze estimation in the wild.

眼部图像在诸多计算机视觉任务中均为关键支撑数据，例如形状配准与视线估计。当前针对此类任务的大规模监督学习方案，往往需要耗费大量时间进行数据采集与人工标注，且标注结果的可靠性难以保障。为此，我们提出可在极短时间内合成带有精准标注的照片级真实感训练数据的方法。我们借助计算机图形学技术，基于头部扫描几何模型构建了多组动态眼部区域模型，并通过随机调整姿态，针对多样化的头部姿态、视线方向与光照条件，合成了大量近景眼部图像。最终，我们通过实验验证了所合成训练数据集（SynthesEyes）的应用优势：其在眼部形状配准任务上的性能超越了当前最优方法，同时在野外场景下基于外观的跨数据集视线估计任务中也取得了更优结果。

搜集汇总

背景与挑战

背景概述

SynthesEyes Dataset是一个用于计算机视觉研究的合成数据集，包含11,382张合成的近距离眼睛图像及其相关数据，适用于眼形注册和视线估计等任务。该数据集通过计算机图形技术生成，覆盖了多种头部姿势、视线方向和光照条件，为研究提供了高质量的标注数据。