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This paper presents a new approach to estimate 3D head pose from a sequence of input images and retarget facial expression to 3D face model using RBF(Radial Based Function) for vision-based animation. The exact head pose estimation and facial motion tracking are critical problems to be solved in developing a vision based human computer interaction or animation. Given an initial reference template of head image and corresponding 3D head pose, full the head motion is recovered by projecting a cylindrical head model to the face image. By updating the template dynamically, it is possible to recover head pose robustly regardless of light variation and self-occlusion. Moreover, to produce a realistic 3D face model, we utilize Gaussian RBF to deform the 3D face model according to the detected facial feature points from input images. During the model deformation, the clusters of the minor feature points around the major facial features are estimated and the positions of the clusters are changed according to the variation of the major feature points. From the experiments, the proposed method can efficiently estimate and track the 3D head pose and create a realistic 3D facial animation model.