  In this paper, we present Wi-Mose, the first 3D moving human pose estimation
system using commodity WiFi. Previous WiFi-based works have achieved 2D and 3D
pose estimation. These solutions either capture poses from one perspective or
construct poses of people who are at a fixed point, preventing their wide
adoption in daily scenarios. To reconstruct 3D poses of people who move
throughout the space rather than a fixed point, we fuse the amplitude and phase
into Channel State Information (CSI) images which can provide both pose and
position information. Besides, we design a neural network to extract features
that are only associated with poses from CSI images and then convert the
features into key-point coordinates. Experimental results show that Wi-Mose can
localize key-point with 29.7mm and 37.8mm Procrustes analysis Mean Per Joint
Position Error (P-MPJPE) in the Line of Sight (LoS) and Non-Line of Sight
(NLoS) scenarios, respectively, achieving higher performance than the
state-of-the-art method. The results indicate that Wi-Mose can capture
high-precision 3D human poses throughout the space.
