Complex realistic human motion sequences satisfying environmental constraints can be created by motion capture, which is a reliable way to reproduce human motions. However, motion capture data is difficult to modify in order to obtain variant motion sequences for multiple tasks. In this thesis, a system for synthesizing complex realistic human motion sequences based on a small group of sample motions to satisfy constraints is proposed. Methods are proposed for the system to preprocesses raw motion capture data to create sample motions that can be easily modified for the purpose of meeting specific requirements, while maintaining the subtleties of the origin motion capture data. Methods for the system to scan user-input constraints, to choose the best sample motion and synthesize the motion sequence based on route affected by the constraint are also proposed. Each generated motion piece is blended with the default motion, and thus a motion sequence composed of several pieces of motion based on constraints will be generated. Artifacts that arise during motion generation are identified and handled properly. Experimental results will show that the system can create cyclical sample motions from motion capture data, generate motion pieces based on environmental constraints, and synthesize complex realistic human motion sequences.