  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 maps 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 perform exhaustive tests on manually
annotated images of real human body limbs. We further compare our method
against several high-end commercial segmentation tools on the body parts
segmentation task. The results show that our method outperforms the other
models by a significant margin. Finally, we present an ablation study to
validate our pre-processing module. With this paper, we release an
implementation of the proposed approach along with the acquired datasets.
