  In this study, we focus on the egocentric segmentation of arms to improve
self-perception in Augmented Virtuality (AV). The main contributions of this
work are: i) a comprehensive survey of segmentation algorithms for AV; ii) an
Egocentric Arm Segmentation Dataset, composed of more than 10, 000 images,
comprising variations of skin color, and gender, among others. We provide all
details required for the automated generation of groundtruth and semi-synthetic
images; iii) the use of deep learning for the first time for segmenting arms in
AV; iv) to showcase the usefulness of this database, we report results on
different real egocentric hand datasets, including GTEA Gaze+, EDSH, EgoHands,
Ego Youtube Hands, THU-Read, TEgO, FPAB, and Ego Gesture, which allow for
direct comparisons with existing approaches utilizing color or depth. Results
confirm the suitability of the EgoArm dataset for this task, achieving
improvement up to 40% with respect to the original network, depending on the
particular dataset. Results also suggest that, while approaches based on color
or depth can work in controlled conditions (lack of occlusion, uniform
lighting, only objects of interest in the near range, controlled background,
etc.), egocentric segmentation based on deep learning is more robust in real AV
applications.
