Wi-Mesh: A WiFi Vision-Based Approach for 3D Human Mesh Construction
Wi-Mesh: A WiFi Vision-Based Approach for 3D Human Mesh Construction
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DOI：
10.1145/3560905.3568536
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Yichao Wang;Yili Ren;Yingying Chen;Jie Yang
中科院分区：
文献类型：
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作者：
Yichao Wang;Yili Ren;Yingying Chen;Jie Yang
本文提出了一种基于WiFi视觉的三维人体网格构建系统——Wi-Mesh。我们的系统利用WiFi的进步来可视化人体的形状和变形，用于3D网格构建。特别是，它利用WiFi设备上的多个发射和接收天线来估计WiFi信号反射的二维到达角（2D AoA），使WiFi设备能够像人类一样“看到”物理环境。然后，它只从物理环境中提取人体图像，并利用深度学习模型将提取的人体数字化为3D网格表示。在各种室内环境下的实验评估表明，Wi-Mesh的平均顶点定位误差为2.81cm，节点位置误差为2.4cm，与使用专用硬件的系统相当。所提出的系统具有重用环境中已经存在的WiFi设备的优势，可以用于潜在的大规模采用。它也可以在非视线（NLoS），光线条件差，和宽松的衣服中工作，在这些地方，基于相机的系统不能很好地工作。
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 reusing 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.