  We present UnrealEgo, i.e., a new large-scale naturalistic dataset for
egocentric 3D human pose estimation. UnrealEgo is based on an advanced concept
of eyeglasses equipped with two fisheye cameras that can be used in
unconstrained environments. We design their virtual prototype and attach them
to 3D human models for stereo view capture. We next generate a large corpus of
human motions. As a consequence, UnrealEgo is the first dataset to provide
in-the-wild stereo images with the largest variety of motions among existing
egocentric datasets. Furthermore, we propose a new benchmark method with a
simple but effective idea of devising a 2D keypoint estimation module for
stereo inputs to improve 3D human pose estimation. The extensive experiments
show that our approach outperforms the previous state-of-the-art methods
qualitatively and quantitatively. UnrealEgo and our source codes are available
on our project web page.
