Human pose estimation (HPE) detects the positions of human body joints for various applications. Compared to using cameras, HPE using radio frequency (RF) signals is non-intrusive and more robust to adverse conditions, exploiting the signal variations caused by human interference. However, existing studies focus on single-domain HPE confined by domain-specific confounders, which cannot generalize to new domains and result in diminished HPE performance. Specifically, the signal variations caused by different human body parts are entangled, containing subject-specific confounders. RF signals are also intertwined with environmental noise, involving environment-specific confounders. In this paper, we propose GenHPE, a 3D HPE approach that generates counterfactual RF signals to eliminate domain-specific confounders. GenHPE trains generative models conditioned on human skeleton labels, learning how human body parts and confounders interfere with RF signals. We manipulate skeleton labels (i.e., removing body parts) as counterfactual conditions for generative models to synthesize counterfactual RF signals. The differences between counterfactual signals approximately eliminate domain-specific confounders and regularize an encoder-decoder model to learn domain-independent representations. Such representations help GenHPE generalize to new subjects/environments for cross-domain 3D HPE. We evaluate GenHPE on three public datasets from WiFi, ultra-wideband, and millimeter wave. Experimental results show that GenHPE outperforms state-of-the-art methods and reduces estimation errors by up to 52.2mm for cross-subject HPE and 10.6mm for cross-environment HPE.

本研究针对现有无线电频率信号下的人类姿态估计（HPE）领域中存在的单一领域限制问题，提出了一种新颖的生成性模型GenHPE，通过生成反事实无线电信号消除领域特定的混杂因素。研究结果表明，GenHPE在跨主体和跨环境的人类姿态估计中性能优于现有最先进的方法，估计误差减少幅度可达52.2mm和10.6mm。

GenHPE：用于3D人类姿态估计的生成性反事实无线电频率信号