If you look at the training and test set info:
> We report results on two protocols: (1) Same layout: We train on the training set in all 16 spatial layouts, and test on remaining frames. Following [31], we randomly select 80% of the samples to be our training set, and the rest to be our testing set. The training and testing samples are different in the person’s location and pose, but share the same person’s identities and background. This is a reasonable assumption since the WiFi device is usually installed in a fixed location. (2) Different layout: We train on 15 spatial layouts and test on 1 unseen spatial layout. The unseen layout is in the classroom scenarios.
Depending on how they selected various frames -- let's just say it was random -- the model could have learned something to effect of "this RF pattern is most similar to these two other readings I'm familiar with" (from the surrounding frames) and can therefore just interpolate between the resulting poses associated with those RF patterns (that the model has compressed/memorized into trained weights).
If you look at the meshes between the image ground truth and the paper's results, you'll see that they are strikingly similar. I find this also suspect because WiFi-band RF interacts a lot more with water than with clothes and so you would expect the outline/mesh to get the "meat-bag" parts of you correct but not be able to guess the contours of baggy clothes. That is... unless it has memorized them from the training set.
> It should be noted that many WiFi routers, such as TP-Link AC1750, come with 3 antennas, so our method only requires 2 of these routers.
> WiFi-based perception is based on the Channel-state-information (CSI) that represents the ratio between the transmitted signal wave and the received signal wave. The CSIs are complex decimal sequences that do not have spatial correspondence to spatial locations
AFAIK, access to raw CSI data is not available on consumer routers. You need specific FPGA/SDR boards which happens to speak the WIFI 2.4G/5G protocol.
There are some open sources efforts such as https://github.com/open-sdr/openwifi