Automatic User-Specific Avatar Parametrisation and Emotion Mapping

In this paper an approach for automatic user-specific 3D model generation and expression classification is proposed. User performance-driven avatar animation is recently in the focus of research due to the increasing amount of low-cost acquisition devices with integrated depth map computation. Thereby challenging is the user-specific emotion classification without a complex manual initial step. Correct classification and emotion intensity identification can only be done with known expression specific facial feature displacement which differs from user to user. The use of facial feature tracking on predefined 3D model expression animations is presented here as solution statement for automatic emotion classification and intensity calculation. Consequently with this approach partial occlusions of a presented emotion do not hamper expression identification based on the symmetrical structure of human faces. Thus, a markerless, automatic and easy to use performance-driven avatar animation approach is presented.