  In this paper, we propose a novel approach to enhance the 3D body pose
estimation of a person computed from videos captured from a single wearable
camera. The key idea is to leverage high-level features linking first- and
third-views in a joint embedding space. To learn such embedding space we
introduce First2Third-Pose, a new paired synchronized dataset of nearly 2,000
videos depicting human activities captured from both first- and third-view
perspectives. We explicitly consider spatial- and motion-domain features,
combined using a semi-Siamese architecture trained in a self-supervised
fashion. Experimental results demonstrate that the joint multi-view embedded
space learned with our dataset is useful to extract discriminatory features
from arbitrary single-view egocentric videos, without needing domain adaptation
nor knowledge of camera parameters. We achieve significant improvement of
egocentric 3D body pose estimation performance on two unconstrained datasets,
over three supervised state-of-the-art approaches. Our dataset and code will be
available for research purposes.
