论文标题
以混合现实为中心的人类细分
Egocentric Human Segmentation for Mixed Reality
论文作者
论文摘要
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进行细分人体部分。我们的贡献是两个方面：i）我们创建了一个半合成数据集，该数据集由超过15，000多个逼真的图像和相关的以像素为中心的人体部位的像素标签组成，例如手臂或腿部，包括不同的人口统计学因素； ii）在Thundernet体系结构的基础上，我们实现了一种深度学习语义分割算法，该算法能够超出实时要求（720 x 720图像16毫秒）。人们认为，这种方法将增强虚拟环境的存在感，并构成对标准虚拟化身的更现实的解决方案。
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.
