Various machine learning approaches have proven to be useful for user verification and identification based on motion data in eXtended Reality (XR). However, their real-world application still faces significant challenges concerning versatility, i.e., in terms of extensibility and generalization capability. This article presents a solution that is both extensible to new users without expensive retraining, and that generalizes well across different sessions, devices, and user tasks. To this end, we developed a similarity-learning model and pretrained it on the "Who Is Alyx?" dataset. This dataset features a wide array of tasks and hence motions from users playing the VR game "Half-Life: Alyx". In contrast to previous works, we used a dedicated set of users for model validation and final evaluation. Furthermore, we extended this evaluation using an independent dataset that features completely different users, tasks, and three different XR devices. In comparison with a traditional classification-learning baseline, our model shows superior performance, especially in scenarios with limited enrollment data. The pretraining process allows immediate deployment in a diverse range of XR applications while maintaining high versatility. Looking ahead, our approach paves the way for easy integration of pretrained motion-based identification models in production XR systems.

翻译：各类机器学习方法已被证明可基于扩展现实（XR）中的运动数据进行用户验证与识别。然而，这些方法在实际应用中仍面临通用性方面的显著挑战，即扩展性与泛化能力。本文提出一种解决方案，既能通过避免昂贵重训练实现新用户扩展，又能在不同会话、设备及用户任务间保持良好泛化。为此，我们开发了相似性学习模型，并在"Who Is Alyx?"数据集上进行预训练。该数据集包含用户游玩VR游戏《半衰期：爱莉克斯》时的多种任务及对应运动数据。与以往研究不同，我们采用专用用户集进行模型验证与最终评估，并利用独立数据集（涵盖完全不同用户、任务及三种不同XR设备）扩展评估范围。与传统分类学习基线相比，我们的模型展现出更优性能，尤其在注册数据有限场景下表现突出。预训练过程使其能即时部署于各类XR应用，同时保持高通用性。展望未来，该方法为生产级XR系统便捷集成预训练运动识别模型铺平了道路。