Skinning is the process of attaching a renderable skin to an underlying articulated skeleton. There are several approaches to skinning with varying degrees of realism and complexity. Our main focus will be on the smooth skinning algorithm, which is both fast and reasonably effective, and has been used extensively in real time and pre-rendered animation. The smooth skinning algorithm goes by many other names in the literature, such as blended skinning, multimatrix skinning, linear blend skinning, skeletal subspace deformation (SSD), and sometimes just skinning. This chapter will explain the smooth skinning algorithm in detail and provide additional information about the offline creation and binding process. Binding refers to the initial attachment of the skin to the underlying skeleton and assigning any necessary information to the vertices. Smooth skinning, while fast and straightforward, does have its limitations, and so alternative techniques are also briefly introduced and referenced, including a variety of deformation techniques and some more elaborate anatomically based approaches that simulate muscle and skin deformations

Generalized coordinates In analytical mechanics, specifically the study of the rigid body dynamics of multibody systems, the term generalized coordinates refers to the parameters that describe the configuration of the system relative to some reference configuration. Example is a vector to specify the posture of the body q=(q1 ,q2 ,q3 ,q4 ,q5 , q6 , q7 ,... ,qn) Usually, the first three numbers: location of the root the next three numbers: orientation of root The rest: the joint angles of the body

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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