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%.

本文提出了一种基于WiFi的智能家居环境中，用于 metaverse Avatar 模拟的人体姿态估计方案 MetaFi，该方案通过使用深度神经网络，能够准确映射通道状态信息到人类姿势地标，并且相比于基于摄像机的解决方案，两种方案的性能相差不大；实验数据表明该方案 PCK@50 值高达95.23%。

使用通用WiFi实现元界头像模拟的无设备姿态估计MetaFi