We revisit millimeter-wave (mmWave) human pose estimation (HPE) from a signal preprocessing perspective. A single mmWave frame provides structured dimensions that map directly to human geometry and motion: range, angle, and Doppler, offering pose-aligned cues that are not explicitly present in RGB images. However, recent mmWave-based HPE systems require more parameters and compute resources yet yield lower estimation accuracy than vision baselines. We attribute this to preprocessing modules: most systems rely on data-driven modules to estimate phenomena that are already well-defined by mmWave sensing physics, whereas human pose could be captured more efficiently with explicit physical priors. To this end, we introduce processing modules that explicitly model mmWave's inter-dimensional correlations and human kinematics. Our design (1) couples range and angle to preserve spatial human structure, (2) leverages Doppler to retain human motion continuity, and (3) applies multi-scale fusion aligned with the human body. A lightweight MLP is involved as the regressor. In experiments, this framework reduces the number of parameters by 55.7-88.9% on the HPE task relative to existing mmWave baselines while maintaining competitive accuracy. Meanwhile, its lightweight nature enables real-time Raspberry Pi deployment. Code and deployment artifacts will be released upon acceptance.

翻译：本文从信号预处理的角度重新审视毫米波人体姿态估计。单个毫米波帧提供了可直接映射到人体几何结构与运动的结构化维度：距离、角度与多普勒，这些维度提供了RGB图像中未明确存在的姿态对齐线索。然而，现有的毫米波人体姿态估计系统需要更多参数与计算资源，其估计精度却低于视觉基线方法。我们将此归因于预处理模块：多数系统依赖数据驱动模块来估计本已由毫米波传感物理明确定义的现象，而利用显式的物理先验可以更高效地捕捉人体姿态。为此，我们引入了显式建模毫米波跨维度关联与人体运动学的处理模块。我们的设计（1）耦合距离与角度以保持人体的空间结构，（2）利用多普勒信息以保持人体运动的连续性，（3）应用与人体结构对齐的多尺度融合。系统采用一个轻量级MLP作为回归器。实验表明，在人体姿态估计任务上，该框架相较于现有毫米波基线方法，参数量减少了55.7-88.9%，同时保持了有竞争力的精度。此外，其轻量级特性使得实时部署于树莓派成为可能。代码与部署构件将在论文录用后发布。