  Recently, commodity Wi-Fi devices have been shown to be able to construct
human pose images, i.e., human skeletons, as fine-grained as cameras. Existing
papers achieve good results when constructing the images of subjects who are in
the prior training samples. However, the performance drops when it comes to new
subjects, i.e., the subjects who are not in the training samples. This paper
focuses on solving the subject-generalization problem in human pose image
construction. To this end, we define the subject as the domain. Then we design
a Domain-Independent Neural Network (DINN) to extract subject-independent
features and convert them into fine-grained human pose images. We also propose
a novel training method to train the DINN and it has no re-training overhead
comparing with the domain-adversarial approach. We build a prototype system and
experimental results demonstrate that our system can construct fine-grained
human pose images of new subjects with commodity Wi-Fi in both the visible and
through-wall scenarios, which shows the effectiveness and the
subject-generalization ability of our model.
