标题： 基于序列的第一人称相机佩戴者在第三人称视图中的识别 显示英文标题

标题： Sequence-Based Identification of First-Person Camera Wearers in Third-Person Views

摘要： 第一人称视角摄像机的日益普及引发了人们对研究共享环境中多摄像机交互的兴趣。尽管像Ego4D和Ego-Exo4D这样的大规模数据集推动了第一人称视觉研究的发展，但多个摄像机佩戴者之间的交互仍然未被充分探索——这是沉浸式学习和协作机器人等应用中的一个关键空白。为了解决这个问题，我们推出了TF2025，这是一个扩展的数据集，包含同步的第一人称和第三人称视图。此外，我们还介绍了一种基于序列的方法，用于识别第三人称视频中的一人称佩戴者，结合运动线索和人物再识别技术。 显示英文摘要

摘要： The increasing popularity of egocentric cameras has generated growing interest in studying multi-camera interactions in shared environments. Although large-scale datasets such as Ego4D and Ego-Exo4D have propelled egocentric vision research, interactions between multiple camera wearers remain underexplored-a key gap for applications like immersive learning and collaborative robotics. To bridge this, we present TF2025, an expanded dataset with synchronized first- and third-person views. In addition, we introduce a sequence-based method to identify first-person wearers in third-person footage, combining motion cues and person re-identification.