Multi Hybrid Extractor Network for 3D Human Pose Estimation

Monocular image or video based 3D human pose estimation remains a very challenging task because of depth ambiguity and occluded joints. To relieve this limitation, we propose a Multiple Hybrid Extraction Network (MHENet), which obtains three different representations of pose hypotheses features by multiple hybrid extractors with different structures, and uses pose interaction and fusion to obtain accurate 3D pose. The Hybrid Extraction Module obtains three hypotheses features: base features correspond to structural information, diverse features correspond to detail information, and condensed features correspond to action information. Hypotheses Interaction Fusion Modul builds relationships across hypotheses feature to generate more accurate 3D poses. Extensive qualitative and quantitative experimental results on a large-scale publicly available dataset demonstrate that our approach achieves competitive performance compared to state-of-the-art methods. The code will be made publicly.