R-DMRF-HPE: Robust Dynamic Multi-modal Radar-vision Fusion for Human Pose Estimation
Year: 2026
Type: article
Abstract: 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 ( p < 0 . 001 ). This method demonstrates excellent robustness in complex environments.
Source: Measurement
Authors: Shengjian Hu, Weining Fang, Haifeng Bao
Institution: Beijing Jiaotong University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,760
Awards: G3979542949, G919811726
OpenAlex ID: W7127314225