In this paper, we combine the strengths of distance-based and classification-based approaches for the task of identifying extended reality users by their movements. For this we present an embedding-based approach that leverages deep metric learning. We train the model on a dataset of users playing the VR game ``Half-Life: Alyx'' and conduct multiple experiments and analyses using a state of the art classification-based model as baseline. The results show that the embedding-based method 1) is able to identify new users from non-specific movements using only a few minutes of enrollment data, 2) can enroll new users within seconds, while retraining the baseline approach takes almost a day, 3) is more reliable than the baseline approach when only little enrollment data is available, 4) can be used to identify new users from another dataset recorded with different VR devices. Altogether, our solution is a foundation for easily extensible XR user identification systems, applicable to a wide range of user motions. It also paves the way for production-ready models that could be used by XR practitioners without the requirements of expertise, hardware, or data for training deep learning models.

翻译：本文结合基于距离和基于分类方法的优势，通过用户运动特征实现扩展现实（XR）身份识别。为此，我们提出一种基于深度度量学习的嵌入方法。我们使用玩家在VR游戏《半条命：Alyx》中的运动数据集训练模型，并以当前最优的分类模型为基线进行多项实验与分析。结果表明：该嵌入方法能够1）仅凭数分钟注册数据即可从非特定运动模式中识别新用户；2）可在数秒内完成新用户注册（而基线方法重新训练需近一天时间）；3）在注册数据不足时比基线方法更可靠；4）可识别其他VR设备记录的不同数据集中新用户。总体而言，我们的解决方案为易于扩展的XR用户身份识别系统奠定了基础，可适用于广泛用户运动模式，并为生产级模型铺平道路——XR从业者无需深度学习模型训练所需的专业知识、硬件或数据即可使用。