Millimeter-wave (mmWave) radar enables privacy-preserving, illumination-invariant Human Pose Estimation (HPE). However, current mmWave-based HPE systems face a signal-noise dilemma: Heatmaps retain human reflections but embed environmental clutter, while Point Clouds (PC) suppress noise through aggressive thresholding but discard informative human reflections, limiting robustness across environments and radar configurations. To address this intrinsic bottleneck, we introduce Person Parametric Physics-informed Representation (PPPR), a physics-informed parametric intermediate representation that replaces purely signal-level encodings with human-centric parameterization. PPPR models each human joint as a Gaussian primitive encoding both kinematic properties, which include position, velocity, orientation, and electromagnetic properties, which include scattering intensity and Doppler signature. These parameters enable optimization through a dual-constraint process: kinematic objectives enforce biomechanical consistency to suppress spatial artifacts, while electromagnetic objectives ensure adherence to mmWave propagation physics, decoupling input representations from non-human noise. Experiments across three mmWave-based HPE datasets with four HPE models demonstrate that replacing conventional inputs with PPPR consistently yields substantial accuracy gains. Furthermore, cross-scenes and cross-datasets experiments confirm PPPR's noise decoupling capability: models trained with PPPR maintain stable performance across diverse furniture arrangements and different radar chipsets, demonstrating its promising generalization capability in the challenging cross-dataset settings. Code will be released upon publication.

翻译：毫米波雷达能够实现保护隐私、光照无关的人体姿态估计。然而，当前基于毫米波的HPE系统面临信噪困境：热图保留了人体反射但嵌入了环境杂波，而点云通过激进阈值抑制噪声却丢弃了信息丰富的人体反射，这限制了其在跨环境和雷达配置下的鲁棒性。为解决这一固有瓶颈，我们提出了人物参数化物理信息表征，这是一种物理信息参数化中间表征，它以人为中心的参数化取代了纯信号级编码。PPPR将每个人体关节建模为一个高斯基元，同时编码运动学属性（包括位置、速度、方位）和电磁属性（包括散射强度和多普勒特征）。这些参数通过双约束过程实现优化：运动学目标强制执行生物力学一致性以抑制空间伪影，而电磁目标确保遵循毫米波传播物理规律，从而将输入表征与非人体噪声解耦。在三个基于毫米波的HPE数据集和四种HPE模型上的实验表明，用PPPR替换传统输入能持续带来显著的精度提升。此外，跨场景和跨数据集实验证实了PPPR的噪声解耦能力：使用PPPR训练的模型在不同家具布置和不同雷达芯片组下均保持稳定性能，证明了其在具有挑战性的跨数据集设置中良好的泛化能力。代码将在论文发表后开源。