Human model simulation has been widely used in various industrial areas such as ergonomic design, product evaluation and characteristic analysis of work-related musculoskeletal disorders. However, the process of building digital human models and capturing their behaviors requires many costly and time-consuming fabrication iterations. To overcome the limitations of this expensive and time-consuming process, many studies have recently presented a markerless motion capture approach that reconstructs the time-varying skeletal motions from optical devices. However, the drawback of the markerless motion capture approach is that the phenomenon of occlusion of motion data occurs in real-time human simulation. In this study, we propose a systematic method of discriminating missing or inaccurate motion data due to motion occlusion and interpolating a sequence of motion frames captured by a markerless depth camera.