Missing Human Motion Capture Data Recovery via Fuzzy Clustering and Projected Proximal Point Algorithm

It is a challenging topic to recover the missing values within the motion capture data. To address this issue, this paper presents an effective missing human motion capture data recovery approach via fuzzy clustering and projected proximal point algorithm（Pro PPA）. The proposed approach first utilizes the linear interpolation method to roughly fill the missing values within the incomplete motion matrix. Then, the fuzzy c-means clustering is employed to separate the previously processed motion sequence into several motion sub-clips, and each sub-clip motion incorporates a particular semantic behavior. Subsequently, according to the low-rank property within each sub-clip motion matrix, the Pro PPA algorithm is selected to achieve matrix completion and the missing values with respected to the original motion capture data can be recovered. Finally, the recovered positions of the non-missing values of the temporally combined sub-clip motions are further replaced by the corresponding original data and linearly smoothed on the basis of the local linear property of human movements simultaneously, whereby the whole incomplete motion sequence can be well reconstructed. The experimental results have shown that the proposed approach is able to well perform the incomplete motion recovery with high accuracy, and the recovered motions can well approximate the real moving trajectories with satisfactory performance.