INTELLIGENT EXPRESSION BLENDING FOR PERFORMANCE DRIVEN FACIAL ANIMATION

Animated facial avatars are being increasingly incorporated into many applications including teleconferencing, entertainment, education, and web-commerce. In many cases, there is a tendency to replace human performers with these virtual actors, owing to the benefits they offer in terms of cost and flexibility. However, these animated face models are under a strong constraint to offer a convincing behavior in order to be truly acceptable to the users. One of the most important aspects of this believability is the use of facial expressions by an avatar. It is extremely desirable that an animated facial model should be able, on the one hand to understand the facial expressions shown by its user, and on the other, to show facial expressions to the user, and to adapt its own facial expressions in response to any change in the situation in which it is immersed. Motion-capture based Performance Driven Facial Animation (PDFA) provides a cost-effective and intuitive means of creating expressive facial avatars. In PDFA, facial actions of an input facial video are tracked by different methods, and re-targeted to any desired facial model which may or may not be the same as the input face. However, ther