  Avatar refers to a representative of a physical user in the virtual world
that can engage in different activities and interact with other objects in
metaverse. Simulating the avatar requires accurate human pose estimation.
Though camera-based solutions yield remarkable performance, they encounter the
privacy issue and degraded performance caused by varying illumination,
especially in smart home. In this paper, we propose a WiFi-based IoT-enabled
human pose estimation scheme for metaverse avatar simulation, namely MetaFi.
Specifically, a deep neural network is designed with customized convolutional
layers and residual blocks to map the channel state information to human pose
landmarks. It is enforced to learn the annotations from the accurate computer
vision model, thus achieving cross-modal supervision. WiFi is ubiquitous and
robust to illumination, making it a feasible solution for avatar applications
in smart home. The experiments are conducted in the real world, and the results
show that the MetaFi achieves very high performance with a PCK@50 of 95.23%.
