以自我为中心的人类分割，实现混合现实

这项工作的目的是使用语义分割网络从以自我为中心的视频中分割出人体部位。我们的贡献是双重的：i）我们创建了一个半合成数据集，该数据集包含15多张，000张逼真的图像以及以自我为中心的人体各部位的相关像素级标签，例如包括不同人口统计学因素的手臂或腿；ii）基于ThunderNet架构，我们实现了深度学习语义分割算法，该算法能够执行超出实时要求（对于720 x 720图像为16 ms）。..

Egocentric Human Segmentation for Mixed Reality

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.