The 'Hybrid Predictive Dynamics Method for Digital Human Modeling' is analyzed in this work. The Hybrid' prefix mentioned in the literature recently [1] refers to the use of motion capture data for improving human motion simulations. This use of motion capture compensates for the inherent weaknesses of purely theoretical motion prediction due to deficiencies in computational power or available theoretical backgrounds. In this work, it is shown that while using the Hybrid' the more precisely and finely the human motion is modeled (if computational and theoretical limitations allow), the less will be the need for the 'Hybrid' method and the more will the human model be able to change the prediction if the inputs are varied (cause and effect). Several human motion scenarios are mentioned in this work. These motion tasks are: "Jogging around Markers", "Rolling Over", "Getting up from Prone", "Vertical Jumping" and "Kneeling and Aiming". The digital human model is a full-body, three dimensional model with 55 degrees of freedom. Six degrees of freedom speck the global position and orientation of the coordinate frame attached to the pelvic point of the digital human and 49 degrees of freedom represent the revolute joints which model the human joints and determine the kinematics of the entire digital human. Motion is generated by a multi-objective optimization approach. The optimization problem is subject to constraints which represent the limitations of the environment, the digital human model and the motion task Design variables are the joint angle profiles. All the forces, inertial, gravitational as well as external, are known, except the ground reaction forces. The feasibility of the generation of that arbitrary motion by using the given ground contact areas is ensured by using the well-known Zero Moment Point (ZMP) constraint.