GF-DecNet: Geometry-Aware Feature Decoupling Network for Millimeter-Wave Radar Pose Estimation

Millimeter-wave (mmWave) radar is an effective sensor for human pose estimation, particularly in challenging environments where vision-based systems may struggle. However, the sparse, noisy nature of mmWave radar point clouds, along with multipath effects, presents difficulties for precise pose reconstruction. This paper introduces the Geometry-aware Feature Decoupling Network (GF-DecNet), an approach that employs a dynamic graph convolutional network (DGCN) framework to overcome these challenges. GF-DecNet combines Set Abstraction (SA) modules with Geometry-aware Feature Decoupling (GFD) blocks for effective multi-scale feature learning. This design captures both local geometric details and global context, which are crucial for accurate pose estimation. The GFD blocks are designed to disentangle local geometric features from higher-level semantic features, enhancing the network’s ability to process sparse and noisy data. A multi-scale feature aggregation module merges these learned features, providing a comprehensive representation of human pose that integrates low-level geometric details with high-level semantic information. Additionally, a learnable geometric constraint mechanism, grounded in biomechanical models, enhances robustness by ensuring consistency with human body proportions. Extensive experiments on four established public datasets (MiliPoint, MM-Fi, mRI, and mmBody) and our curated WavePose dataset demonstrate that GF-DecNet consistently achieves competitive performance, achieving significant improvements in Mean Per Joint Position Error (MPJPE). Ablation studies further validate the effectiveness of each key component of GF-DecNet, These findings establish GF-DecNet as a strong benchmark in the field of mmWave radar-based pose estimation.