  Egocentric 3D human pose estimation with a single fisheye camera has drawn a
significant amount of attention recently. However, existing methods struggle
with pose estimation from in-the-wild images, because they can only be trained
on synthetic data due to the unavailability of large-scale in-the-wild
egocentric datasets. Furthermore, these methods easily fail when the body parts
are occluded by or interacting with the surrounding scene. To address the
shortage of in-the-wild data, we collect a large-scale in-the-wild egocentric
dataset called Egocentric Poses in the Wild (EgoPW). This dataset is captured
by a head-mounted fisheye camera and an auxiliary external camera, which
provides an additional observation of the human body from a third-person
perspective during training. We present a new egocentric pose estimation
method, which can be trained on the new dataset with weak external supervision.
Specifically, we first generate pseudo labels for the EgoPW dataset with a
spatio-temporal optimization method by incorporating the external-view
supervision. The pseudo labels are then used to train an egocentric pose
estimation network. To facilitate the network training, we propose a novel
learning strategy to supervise the egocentric features with the high-quality
features extracted by a pretrained external-view pose estimation model. The
experiments show that our method predicts accurate 3D poses from a single
in-the-wild egocentric image and outperforms the state-of-the-art methods both
quantitatively and qualitatively.
