| Recently, with the development of technology of 3D animation and motion capture, motion capture has become one of the most promising technologies in character animation. A lager number of realistic motion data can be captured by recording the movement of a real actor with a motion capture system, and then we can apply these motion data to new character. However, while the target character is different from the original one, the target character is likely to lose desire features of original motion. The problem can be solved through motion retargetting technology. This can improve the universality and adaptability of the motion data reusing.This thesis researches on the main methods of motion retargetting, including motion signal processing and constraint solutions. A motion retargetting algorithm based on spacetime constraint and inverse kinematics is put forward. After specifying the spacetime constraints and establishing objective functions of intended motion according to different situations, we get satisfactory motion gesticulation by approaches of inverse kinematics and pseudo inverse matrix of the Jacobian. Then we optimize this algorithm by changing the hard spacetime constraint to the soft one. Experimental results demonstrate that by our method, it is convenient to retarget motion to target character, and can reduce the possibility of abnormal motion.We also study on the motion retargetting method from human model to other model. The algorithm of these motion retargetting is introduced, and improved model blocking algorithm is given, for optimizing the algorithm of motion retargetting from human model to non-articulate model.A motion retargetting system is designed based on MotionBuilder and Open Reality SDK. Some technologies of motion retargetting according to different target character is implemented in this system. Experimental results demonstrate that optimized retargetting algorithm takes a little long time, but it will get better retargetting effect. |
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