  The objective of this work is to segment human body parts from egocentric
video using semantic segmentation networks. Our contribution is two-fold: i) we
create a semi-synthetic dataset composed of more than 15, 000 realistic images
and associated pixel-wise labels of egocentric human body parts, such as arms
or legs including different demographic factors; ii) building upon the
ThunderNet architecture, we implement a deep learning semantic segmentation
algorithm that is able to perform beyond real-time requirements (16 ms for 720
x 720 images). It is believed that this method will enhance sense of presence
of Virtual Environments and will constitute a more realistic solution to the
standard virtual avatars.
