使用预训练相似度学习的扩展现实中通用用户识别
Versatile User Identification in Extended Reality using Pretrained Similarity-Learning
AI总结：
本文提出在“Who Is Alyx?”数据集上预训练相似度学习模型，以实现XR中无需重训、可跨会话设备任务泛化的用户识别，并在独立多设备数据集上验证其优于分类基线。
摘要：
各种机器学习方法已被证明可用于基于扩展现实（XR）中运动数据的用户验证和识别。然而，其在现实世界中的应用在通用性方面仍面临重大挑战，即在可扩展性和泛化能力方面。本文提出了一种解决方案，既可在无需昂贵重新训练的情况下扩展至新用户，又能在不同会话、设备和用户任务之间良好泛化。为此，我们开发了一个相似度学习模型，并在“Who Is Alyx?”数据集上进行了预训练。该数据集包含玩VR游戏《Half-Life: Alyx》的用户所执行的大量任务及相应运动。与以往工作不同，我们使用专门的一组用户进行模型验证和最终评估。此外，我们利用一个独立数据集扩展了评估，该数据集具有完全不同的用户、任务以及三种不同的XR设备。与传统的分类学习基线相比，我们的模型表现出更优的性能，尤其是在注册数据有限的场景中。预训练过程允许在保持高通用性的同时，立即部署到各种XR应用中。展望未来，我们的方法为在生产级XR系统中轻松集成预训练的基于运动的识别模型铺平了道路。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
