Publication

Abstract : Motion capture (mocap) data is often corrupted by the presence of erroneous entries. Corrupted mocap data tends to deteriorate the structure of the recorded human actions, causing hindrance while using it for different applications. In this paper, we propose a new four-way optimization model for recovering plausible human actions from the misrepresented mocap sequences without the requirement of any pre-trained model. The proposed model employs joint regularized low rank and sparse priors for separating the clean mocap data and the erroneous entries effectively. The brilliance of this work is confined in the efficient formulation of the optimization model by integrating two additional constraints based on the movement of individual node trajectories and the hierarchy of the parent-child node pairs. The former ensures the smoothness of the action sequences, while the latter limits the spatial drifting of the skeletal nodes. Detailed analysis of the experimental results showcases the ability of the developed algorithm in achieving commendable human action recovery in minimal time compared to the recent counterparts, even from mocap data with 20–30% of misrepresented joints in 90% of the frames.