Human Motion Capture Data Recovery Using Capped Nuclear Norm Regularization

Using the low-rank property of human motion data, the problem of recovering human motion capture data is modeled as a low-rank matrix completion problem. Different from the traditional methods which utilize the nuclear norm as the convex relaxation of rank function, a non-convex matrix Capped nuclear norm (CaNN) is introduced in this paper, and then the recovery model of human motion capture data is established based on the Capped nuclear norm regularization. Next, the model is efficiently solved by using the alternative direction method of multipliers, combined with adaptive learning for the truncated parameter and (inverse) discrete cosine Fourier transform. Finally, the proposed model CaNN is compared with four classical models, i.e., TSMC, TrNN, IRNN-Lp and TSPN, on CMU dataset and HDM05 dataset. By comparing the recovery error and visual effect, the experimental results show that CaNN has a good ability to recover the corrupted motion data, and the recovered motions can well approximate the true ones.