RF-PoseR: A Human Pose Rectifier for mmWave Radar-Based Pose Estimation

mmWave radar-based human pose estimation is garnering increasing attention in Internet of Things (IoT) applications owing to its robustness under adverse lighting conditions and occlusion scenarios. However, accurately estimating human poses from mmWave signals remains challenging due to two key limitations: the lack of structural consistency when perceiving targets as a unified whole, and the absence of spatiotemporal consistency when tracking individual targets. To tackle these two challenges, this paper introduces RF-PoseR, a dedicated human pose rectifier for mmWave radar-based pose estimation. RF-PoseR serves as a corrective framework that refines the pose outputs generated by mmWave pose estimation systems. Based on the observation that perceptual inconsistencies in radar signals result in severely erroneous joint estimations, we introduce spatial and temporal constraints to enhance pose estimation accuracy. Specifically, RF-PoseR incorporates three core components: (1) unreliable joint removal guided by spatial structure, (2) pose completion leveraging temporal continuity, and (3) cross-modal pre-training utilizing large-scale visual pose datasets. Extensive experiments on mmWave radar pose datasets demonstrate that RF-PoseR significantly enhances the accuracy of poses generated by existing radar-based estimation networks. Furthermore, experiments on visual datasets confirm the method’s broader applicability beyond radar perception tasks.