An efficient bilinear factorization based method for motion capture data refinement
As a preprocessing step, motion capture (mocap) data refinement is to predict missing data and remove noises and outliers. In recent years, low-rank matrix completion has been successfully applied to mocap dara refinement by considering the low-rank property of motion data, wherein a representative approach is called TSMC proposed by Feng et al. However, this approach heavily depends on singular value decomposition (SVD) and requires to calculate the inversion of a smoothing related matrix at each iteration whose size is equal to the frame number of motion sequence. Thus, it is very slow for long motion sequences. In this paper, we aim to present an efficient method, in which the matrix bilinear factorization and the variational definition of matrix nuclear norm are employed to avoid SVD. Besides, after analyzing the eigendecomposition of the smoothing related matrix, we convert matrix inversion into discrete cosine transform (DCT) and inverse DCT. The augmented Lagrange multiplier algorithm is adopted to solve the refinement optimization model. Experimental results show the proposed approach is much more accurate and efficient than TSMC.