Abstract
Eye gaze can provide rich information on human psychological activities, and has garnered significant attention in the field of Human-Robot Interaction (HRI). However, existing gaze estimation methods merely predict either the gaze direction or the Point-of-Gaze (PoG) on the screen, failing to provide sufficient information for a comprehensive six Degree-of-Freedom (DoF) gaze analysis in 3D space. Moreover, the variations of eye shape and structure among individuals also impede the generalization capability of these methods. In this study, we propose MAGE, a Multi-task Architecture for Gaze Estimation with an efficient calibration module, to predict the 6-DoF gaze information that is applicable for the real-word HRI. Our basic model encodes both the directional and positional features from facial images, and predicts gaze results with dedicated information flow and multiple decoders. To reduce the impact of individual variations, we propose a novel calibration module, namely Easy-Calibration, to fine-tune the basic model with subject-specific data, which is efficient to implement without the need of a screen. Experimental results demonstrate that our method achieves state-of-the-art performance on the public MPIIFaceGaze, EYEDIAP, and our built IMRGaze datasets.
Abstract (translated)
目光追踪可以提供丰富的人类心理活动信息，在人机交互（HRI）领域引起了极大的关注。然而，现有的目光估计方法只能预测目光方向或屏幕上的目光点（PoG），无法为三维空间中的六自由度（DoF）全面的目光分析提供足够信息。此外，个体间眼睛形状和结构的差异也阻碍了这些方法的泛化能力。在这项研究中，我们提出了MAGE，一种带有高效校准模块的多任务架构，用于预测适用于真实世界人机交互的六自由度目光信息。我们的基本模型从面部图像编码方向和位置特征，并通过专用的信息流和多个解码器来预测目光结果。为了减少个体差异的影响，我们提出了一种新颖的校准模块，即Easy-Calibration，该模块利用特定于对象的数据对基础模型进行微调，并且可以在无需屏幕的情况下高效实现。实验结果显示，我们的方法在公开的MPIIFaceGaze、EYEDIAP和我们构建的IMRGaze数据集上均取得了最先进的性能。
URL
https://arxiv.org/abs/2505.16384
