R-DMRF-HPE: Robust Dynamic Multi-modal Radar-vision Fusion for Human Pose Estimation

Accurate 3D human pose estimation has important application value in fields such as human–computer interaction, motion analysis, and medical rehabilitation. Traditional single-modal methods have significant limitations in complex environments. This paper proposes a dynamic multi-modal human pose estimation method that fuses visual sensors and millimeter-wave radar. First, we construct a radar point cloud processing framework based on graph neural networks. This framework maintains spatial topological relationships through a k-nearest neighbor graph structure and fuses five-dimensional feature information using a reflection intensity-weighted message passing mechanism. Second, we design a dynamic fusion strategy that combines basic quality assessment, learnable quality assessment, and modal prior weights to achieve quality-aware adaptive fusion. Systematic experiments on two datasets demonstrate the effectiveness of our approach. On the standard environment mRI dataset, our method achieves an MPJPE of 91.82  41.81 mm. On the complex environment mmBody dataset, the average MPJPE is 62.47  22.39 mm. Statistical analysis indicates that all improvements are significant (). This method demonstrates excellent robustness in complex environments.

Shengjian Hu, Weining Fang ^*, Haifeng Bao. R-DMRF-HPE: Robust Dynamic Multi-modal Radar-vision Fusion for Human Pose Estimation.Measurement，Volume 268, 7 April 2026, 120687