AI RESEARCH
mmGAT: Pose Estimation by Graph Attention with Mutual Features from mmWave Radar Point Cloud
arXiv CS.CV
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ArXi:2603.08551v1 Announce Type: new 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.