论文标题
在视频透明的增强虚拟性中使用CNN用于用户细分
Using CNNs For Users Segmentation In Video See-Through Augmented Virtuality
论文作者
论文摘要
在本文中，我们介绍了使用深度学习技术将用户和其他参与者集成到头部安装的视频透明的增强虚拟场景中的初步结果。以前已经证明，在此类模拟中看到用户身体可以改善虚拟环境中自我和社交形象的感觉以及用户性能。我们建议使用卷积神经网络在从用户的角度获取的立体RGB视频流中用户身体的实时语义分割。我们描述了系统的设计问题以及实施详细信息，并证明了将此类神经网络与用户合并到增强的虚拟模拟中的可行性。
In this paper, we present preliminary results on the use of deep learning techniques to integrate the users self-body and other participants into a head-mounted video see-through augmented virtuality scenario. It has been previously shown that seeing users bodies in such simulations may improve the feeling of both self and social presence in the virtual environment, as well as user performance. We propose to use a convolutional neural network for real time semantic segmentation of users bodies in the stereoscopic RGB video streams acquired from the perspective of the user. We describe design issues as well as implementation details of the system and demonstrate the feasibility of using such neural networks for merging users bodies in an augmented virtuality simulation.
