and 4.) keyframe animation. Motion capture uses cameras placed around a subject. The subject is generally fitted either with reflectors (passive motion capture) or sources (active motion capture) that precisely determine the subject's position in space. The data recorded by the cameras is then digitized and converted into a three-dimensional computer model of the subject. Until recently, the size of the detectors/sources used by motion capture systems made the technology inappropriate for facial capture. However, miniaturization and other advancements have made motion capture a viable tool for computer facial animation. Facial motion capture was used extensively in Polar Express by Imageworks where hundreds of motion points were captured. This film was very accomplished and while it attempted to recreate realism, it was criticized for having fallen in the 'uncanny valley', the realm where animation realism is sufficient for human recognition and to convey the emotional message but where the characters fail to be perceived as realistic. The main difficulties of motion capture are the quality of the data which may include vibration as well as the retargeting of the geometry of the points. Markerless motion capture aims at simplifying the motion capture process by avoiding encumbering the performer with markers. Several techniques came out recently leveraging different sensors, among which standard video cameras, Kinect and depth sensors or other structured-light based devices. Systems based on structured light may achieve real-time performance without the use of any markers using a high speed structured light scanner. The system is based on a robust offline face tracking stage which trains the system with different facial expressions. The matched sequences are used to build a person-specific linear face model that is subsequently used for online face tracking and expression transfer. Audio-driven techniques are particularly well fitted for speech animation. Speech is usually treated in a different way to the animation of facial expressions, this is because simple keyframe-based approaches to animation typically provide a poor approximation to real speech dynamics. Often visemes are used to represent the key poses in observed speech (i.e. the position of the lips, jaw and tongue when producing a particular phoneme), however there is a great deal of variation in the realisation of visemes during the production of natural speech. The source of this variation is termed coarticulation which is the influence of surrounding visemes upon the current viseme (i.e. the effect of context). To account for coarticulation current