This study presents a novel framework for 3D gaze tracking tailored for mixed-reality settings, aimed at enhancing joint attention and collaborative efforts in team-based scenarios. Conventional gaze tracking, often limited by monocular cameras and traditional eye-tracking apparatus, struggles with simultaneous data synchronization and analysis from multiple participants in group contexts. Our proposed framework leverages state-of-the-art computer vision and machine learning techniques to overcome these obstacles, enabling precise 3D gaze estimation without dependence on specialized hardware or complex data fusion. Utilizing facial recognition and deep learning, the framework achieves real-time, tracking of gaze patterns across several individuals, addressing common depth estimation errors, and ensuring spatial and identity consistency within the dataset. Empirical results demonstrate the accuracy and reliability of our method in group environments. This provides mechanisms for significant advances in behavior and interaction analysis in educational and professional training applications in dynamic and unstructured environments.

本研究提出一种新颖的针对混合现实环境的三维凝视追踪框架，旨在增强团队协作和共同注意力，通过利用计算机视觉和机器学习技术，实现精确的三维凝视估计，无需依赖专用硬件或复杂的数据融合，在组环境中追踪凝视模式，解决常见深度估计误差，保证数据集的空间和身份的一致性，实证结果展示了我们方法在群组环境中的准确性和可靠性，为在动态和非结构化环境中的教育和专业培训应用中的行为和互动分析提供了推动机制。

混合现实环境中研究协作交互的3D凝视跟踪