Human motion data refinement unitizing structural sparsity and spatial-temporal information

Human motion capture techniques (MOCAP) are widely applied in many areas such as computer vision, computer animation, digital effect and virtual reality. Even with professional MOCAP system, the acquired motion data still always contains noise and outliers, which highlights the need for the essential motion refinement methods. In recent years, many approaches for motion refinement have been developed, including signal processing based methods, sparse coding based methods and low-rank matrix completion based methods. However, motion refinement is still a challenging task due to the complexity and diversity of human motion. In this paper, we propose a data-driven-based human motion refinement approach by exploiting the structural sparsity and spatio-temporal information embedded in motion data. First of all, a human partial model is applied to replace the entire pose model for a better feature representation to exploit the abundant local body posture. Then, a dictionary learning which is for special task of motion refinement is designed and applied in parallel. Meanwhile, the objective function is derived by taking the statistical and locality property of motion data into account. Compared with several state-of-art motion refine methods, the experimental result demonstrates that our approach outperforms the competitors.