While millimeter-wave (mmWave) presents advantages for Human Pose Estimation (HPE) through its non-intrusive sensing capabilities, current mmWave-based HPE methods face limitations in two predominant input paradigms: Heatmap and Point Cloud (PC). Heatmap represents dense multi-dimensional features derived from mmWave, but is significantly affected by multipath propagation and hardware modulation noise. PC, a set of 3D points, is obtained by applying the Constant False Alarm Rate algorithm to the Heatmap, which suppresses noise but results in sparse human-related features. To address these limitations, we study the feasibility of providing an alternative input paradigm: Differentiable Physics-driven Human Representation (DIPR), which represents humans as an ensemble of Gaussian distributions with kinematic and electromagnetic parameters. Inspired by Gaussian Splatting, DIPR leverages human kinematic priors and mmWave propagation physics to enhance human features while mitigating non-human noise through two strategies: 1) We incorporate prior kinematic knowledge to initialize DIPR based on the Heatmap and establish multi-faceted optimization objectives, ensuring biomechanical validity and enhancing motion features. 2) We simulate complete mmWave processing pipelines, re-render a new Heatmap from DIPR, and compare it with the original Heatmap, avoiding spurious noise generation due to kinematic constraints overfitting. Experimental results on three datasets with four methods demonstrate that existing mmWave-based HPE methods can easily integrate DIPR and achieve superior performance.

翻译：尽管毫米波凭借其非侵入式感知能力在人体姿态估计中展现出优势，但当前基于毫米波的姿态估计方法在两种主流输入范式上存在局限：热力图与点云。热力图表征从毫米波信号提取的密集多维特征，但受多径传播和硬件调制噪声影响显著；点云作为三维点集，通过对热力图应用恒虚警率算法获得，虽能抑制噪声，却导致人体相关特征稀疏。为克服这些局限，本研究探讨了提供替代输入范式的可行性：可微分物理驱动的人体表征。该表征将人体建模为具有运动学与电磁参数的高斯分布集合。受高斯溅射技术启发，DIPR融合人体运动学先验知识与毫米波传播物理特性，通过双重策略增强人体特征并抑制非人体噪声：1）引入运动学先验知识，基于热力图初始化DIPR并建立多维度优化目标，确保生物力学有效性并强化运动特征；2）模拟完整毫米波处理流程，从DIPR重新渲染新热力图，并与原始热力图进行比对，避免因运动学约束过拟合而产生的伪噪声。在三个数据集上对四种方法的实验结果表明，现有基于毫米波的姿态估计方法可无缝集成DIPR并实现更优性能。