In this paper, we present, Wi-Mesh, a WiFi vision-based 3D human mesh construction system. Our system leverages the advances of WiFi to visualize the shape and deformations of the human body for 3D mesh construction. In particular, it leverages multiple transmitting and receiving antennas on WiFi devices to estimate the two-dimensional angle of arrival (2D AoA) of the WiFi signal reflections to enable WiFi devices to see the physical environment as we humans do. It then extracts only the images of the human body from the physical environment and leverages deep learning models to digitize the extracted human body into a 3D mesh representation. Experimental evaluation under various indoor environments shows that Wi-Mesh achieves an average vertices location error of 2.81cm and joint position error of 2.4cm, which is comparable to the systems that utilize specialized and dedicated hardware. The proposed system has the advantage of re-using the WiFi devices that already exist in the environment for potential mass adoption. It can also work in non-line of sight (NLoS), poor lighting conditions, and baggy clothes, where the camera-based systems do not work well.
翻译:在本文中,我们介绍Wi-Mesh,一个Wi-Mesh,一个基于WiFi视觉的3D人类网格建筑系统。我们的系统利用Wi-Fi的先进技术,为3D网网格结构的构造和变形进行视觉化,特别是利用WiFi设备上的多个传输和接收天线,以估计WiFi信号反射的到达的二维角度(2D AoA),使WiFi设备能够像人类一样看到物理环境,然后从物理环境中提取人体的图像,并利用深层学习模型将提取的人体体形数字化成3D网格。在各种室内环境中进行的实验评估显示,Wi-Mesh实现了平均脊椎位置误差2.81厘米和2.4cm联合位置误差,这与使用专门和专用硬件的系统相当。拟议的系统具有重新使用在环境中已经存在的WiFi设备进行大规模应用的优势。它也可以在非视线上工作(NLOS),照明条件差,光质不高,光衣衣室工作良好。