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

MetaFi: Device-Free Pose Estimation via Commodity WiFi for Metaverse Avatar Simulation