  This paper presents a new framework for human body part segmentation based on
Deep Convolutional Neural Networks trained using only synthetic data. The
proposed approach achieves cutting-edge results without the need of training
the models with real annotated data of human body parts. Our contributions
include a data generation pipeline, that exploits a game engine for the
creation of the synthetic data used for training the network, and a novel
pre-processing module, that combines edge response map and adaptive histogram
equalization to guide the network to learn the shape of the human body parts
ensuring robustness to changes in the illumination conditions. For selecting
the best candidate architecture, we performed exhaustive tests on
manually-annotated images of real human body limbs. We further present an
ablation study to validate our pre-processing module. The results show that our
method outperforms several state-of-the-art semantic segmentation networks by a
large margin. We release an implementation of the proposed approach along with
the acquired datasets with this paper.
