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.
@article{arxiv.2506.00394,
title = {Sequence-Based Identification of First-Person Camera Wearers in Third-Person Views},
author = {Ziwei Zhao and Xizi Wang and Yuchen Wang and Feng Cheng and David Crandall},
journal= {arXiv preprint arXiv:2506.00394},
year = {2025}
}