HiMoReNet: A Hierarchical Model for Human Motion Refinement

3D human pose estimation has a broad range of applications, including anomaly detection and animation creation. Despite that significant progress on relative research has been made during the past decades, producing precise and smooth estimations for input videos still remains challenging mainly because of its ill-posed attributes. In this letter, we propose HiMoReNet, a post-processing motion refinement neural network based on an elaborate hierarchical architecture. Firstly, we distinguish characteristic motion patterns of joints at different locations by grouping the joints and employing respective spatiotemporal processing modules for each group. In addition, by mimicking interactions among multiple body parts, global context information is leveraged to further guide the motion refinement. Quantitative and qualitative results on the 3DPW dataset demonstrate that our proposed HiMoReNet achieves the state-of-the-art performance, and excels in jitter removal and precise pose estimation.