Abstract: Since the existing approaches to control human motion synthesis are too complicated, we propose a templated motion synthesis model to reduce the difficulty of using motion synthetic technology. We use sparse principal component analysis(SPCA), group lasso and exclusive group lasso to model human motions so that each low-dimensional parameter depends on a few human joints which form an intrinsic degree of freedom(DOF) with intuitive meanings. Meanwhile, our approach makes each joint controlled by as few low-dimensional parameters as possible to reduce the interferences between different DOFs. Our experiments demonstrate that users can control the motion features like amplitude of swing arm, kick height and jump distance by modifying the low-dimensional parameters intuitively in real time. This two-step approach of template learning and template customization can effectively reduce the complexity of synthesis control, and allows inexperienced users to create a realistic human animation quickly and easily.