Egocentric vision is an emerging topic, which has
demonstrated great potential in assistive healthcare scenarios,
ranging from human-centric behavior analysis to personal
social assistance. Within this field, due to the heterogeneity of
visual perception from first-person views, egocentric pose estimation is one of the most significant prerequisites for enabling
various downstream applications. However, existing methods
for egocentric pose estimation mainly focus on predicting the
pose represented in the camera coordinates from a single image,
which ignores the latent cues in the temporal domain and results
in less accuracy. In this paper, we propose Ego+X, an egocentric
vision based system for 3D canonical pose estimation and
human-centric social interaction characterization. Our system is
composed of two head-mounted egocentric cameras, where one
is faced downwards and the other looks outwards. By leveraging
the global context provided by visual SLAM, we first propose
Ego-Glo for spatial-accurate and temporal-consistent egocentric
3D pose estimation in the canonical coordinate system. With the
help of an egocentric camera looking outwards, we then propose
Ego-Soc by extending Ego-Glo to various social interaction
tasks, e.g., object detection and human-human interaction.
Quantitative and qualitative experiments have been conducted
to demonstrate the effectiveness of our proposed Ego+X.