  WiFi-based pose estimation is a technology with great potential for the
development of smart homes and metaverse avatar generation. However, current
WiFi-based pose estimation methods are predominantly evaluated under controlled
laboratory conditions with sophisticated vision models to acquire accurately
labeled data. Furthermore, WiFi CSI is highly sensitive to environmental
variables, and direct application of a pre-trained model to a new environment
may yield suboptimal results due to domain shift. In this paper, we proposes a
domain adaptation algorithm, AdaPose, designed specifically for
weakly-supervised WiFi-based pose estimation. The proposed method aims to
identify consistent human poses that are highly resistant to environmental
dynamics. To achieve this goal, we introduce a Mapping Consistency Loss that
aligns the domain discrepancy of source and target domains based on inner
consistency between input and output at the mapping level. We conduct extensive
experiments on domain adaptation in two different scenes using our
self-collected pose estimation dataset containing WiFi CSI frames. The results
demonstrate the effectiveness and robustness of AdaPose in eliminating domain
shift, thereby facilitating the widespread application of WiFi-based pose
estimation in smart cities.
