For the situation that the adjacent markers of Motion Capture (MOCAP) data missing for a period of time due to lights and other factors when practically gathering data, a new MOCAP data recovery algorithm is proposed by using the latent correlation and the skeleton constraint in MOCAP data. The algorithm firstly transforms the MOCAP data to represent the changes of the relative position of adjacent markers to acquire the skeleton constraint term. Then the sparse representation and the skeleton constraint term are used for dictionary training which is utilized to recovery missing data. The experiment results show that the algorithm can improve the recovery accuracy of the coordinates of the missing markers and increase the bone length recovery accuracy to 10^-4 cm, and verify the feasibility and effectiveness of the algorithm.