Facial Anination deals with the simulation of the different facial expresions a character can make. Inorder to simulate conversation, visible emotional expressions(e.g Saddness, Happines), we need to understand the different mechanisims and muscle structures that the face uses to accurately represent the emotion / visemes. generally the facial expressions and visemes in animation relies on three different appoaches: • Muscle-based models • Capture real human data • Expression Cloning

Data based facial animation The generation of facial animation data can be approached in different ways: 1. marker-based motion capture on points or marks on the face of a performer 2. markerless motion capture techniques using different type of cameras 3. audio-driven techniques 4. key frame animation

There are three methods • Create them manually • Use real human / animal motions • Use physically based simulation Using Real Human (Animal) Motion • Real human (animal) motion is realistic • Much faster and cheaper than manually producing the data We use the motion capture device (Mocap) • There are four major types of Mocaps – Optical – Magnetic – Inertial trackers

Motion tracking or motion capture started as a photogrammetric analysis tool in biomechanics research in the 1970s and 1980s, and expanded into education, training, sports and recently computer animation for television, cinema, and video games as the technology matured. A performer wears markers near each joint to identify the motion by the positions or angles between the markers. Acoustic, inertial, LED, magnetic or reflective markers, or combinations of any of these, are tracked, optimally at least two times the frequency rate of the desired motion, to submillimeter positions. The resolution of the system is important in both the spatial resolution and temporal resolution as motion blur causes almost the same problems as low resolution.

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