Motion Segmentation based Human Motion Capture Data Recovery via Sparse and Low-rank Decomposition

According to the complexity of human movement and randomness of the noise interference,this paper presents a motion segmentation based approach for human motion capture data recovery via the sparse and low-rank decomposition.The proposed approach first employs the bilateral filter to amend the distorted human motion capture data,featuring on removing singular values and smoothing the motion sequence.Then,the probabilistic principal component analysis(PPCA) method is utilized to segment the motion data into different semantic behaviors automatically.Subsequently,the accelerated proximal gradient algorithm(APG) based sparse and low-rank decomposition is adopted to achieve the partial data recovery with respected to each separated semantic behavior.Finally,all the recovered sub-motions are sequentially combined to achieve the whole motion recovery.The experimental results have shown that the proposed motion recovery approach can well restore the distorted human motion data with better performance.The proposed approach can be well utilized to approximate the realistic human behaviors from the corrupted motion sequences,and the experimental results have shown the satisfactory performances.