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

From Point to Space: 3D Moving Human Pose Estimation Using Commodity WiFi