Pose estimation and human action recognition (HAR) are pivotal technologies spanning various domains. While the image-based pose estimation and HAR are widely admired for their superior performance, they lack in privacy protection and suboptimal performance in low-light and dark environments. This paper exploits the capabilities of millimeter-wave (mmWave) radar technology for human pose estimation by processing radar data with Graph Neural Network (GNN) architecture, coupled with the attention mechanism. Our goal is to capture the finer details of the radar point cloud to improve the pose estimation performance. To this end, we present a unique feature extraction technique that exploits the full potential of the GNN processing method for pose estimation. Our model mmGAT demonstrates remarkable performance on two publicly available benchmark mmWave datasets and establishes new state of the art results in most scenarios in terms of human pose estimation. Our approach achieves a noteworthy reduction of pose estimation mean per joint position error (MPJPE) by 35.6% and PA-MPJPE by 14.1% from the current state of the art benchmark within this domain.

翻译：姿态估计与人体动作识别（HAR）是横跨多个领域的关键技术。尽管基于图像的姿态估计与HAR因其优越性能而广受赞誉，但它们在隐私保护方面存在不足，且在低光照与黑暗环境下表现欠佳。本文利用毫米波（mmWave）雷达技术，通过图神经网络（GNN）架构结合注意力机制处理雷达数据，实现人体姿态估计。我们的目标是捕捉雷达点云中更精细的细节，以提升姿态估计性能。为此，我们提出了一种独特的特征提取技术，充分发挥GNN处理方法在姿态估计中的潜力。我们的模型mmGAT在两个公开的毫米波基准数据集上展现出卓越性能，并在多数场景下为人体姿态估计树立了新的最优结果。我们的方法在该领域当前最优基准的基础上，将姿态估计的平均关节位置误差（MPJPE）显著降低了35.6%，PA-MPJPE降低了14.1%。