Human motion data that are captured by the markers attached to an actor's body have been widely used in many areas. However, occlusion caused by the actor's body or clothing might make several markers missing for a period of time during the capture process, which highlights the need for motion recovery in the human motion capture process. In recent years, low-rank matrix completion and sparse coding have been used in many data-driven motion recovery methods. However, applying them directly to recover missing data is not effective because low rank is only a basic statistical property of human motion. In addition, the dictionary is usually learned and used in a complete feature space, while human motion must be recovered from an incomplete feature space. Moreover, low-rank matrix completion and sparse coding take advantage only of the statistical property and ignore another important property, i.e., the kinematic property of human motion. Inspired by coupled dictionary learning, we modify the traditional dictionary learning process and propose a new process for the special task of motion recovery. The new recovery process jointly utilizes statistical and kinematic information. Within the proposed method, we first learn a dictionary from a large number of complete-incomplete training frame pairs, to preserve the statistical information of motion data. Then, with the smoothness constraint and the bone-length constraint which take the kinematic information into recovery process, we recover motions using sparse representations of incomplete frames and a learned dictionary through an optimization model. Additionally, we employ two gradient-based optimization algorithms for dictionary learning and motion recovery. Extensive experiment results and comparisons with four other state-of-the-art methods demonstrate the effectiveness of the proposed method.
['Guiyu Xia', 'Huaijiang Sun', 'Guoqing Zhang', 'Lei Feng']
Human motion recovery jointly utilizing statistical and kinematic information