Computer Facial Animation - Techniques - 3D Animation

3D Animation

Three-dimensional head models provide the most powerful means of generating computer facial animation. One of the earliest works on computerized head models for graphics and animation was done by Parke. The model was a mesh of 3D points controlled by a set of conformation and expression parameters. The former group controls the relative location of facial feature points such as eye and lip corners. Changing these parameters can re-shape a base model to create new heads. The latter group of parameters (expression) are facial actions that can be performed on face such as stretching lips or closing eyes. This model was extended by other researchers to include more facial features and add more flexibility. Different methods for initializing such “generic” model based on individual (3D or 2D) data have been proposed and successfully implemented. The parameterized models are effective ways due to use of limited parameters, associated to main facial feature points. The MPEG-4 standard (Section 7.15.3 – Face animation parameter data) defines a minimum set of parameters for facial animation.

Animation is done by changing parameters over time. Facial animation is approached in different ways, traditional techniques include

  1. shapes/morph targets,
  2. skeleton-muscle systems,
  3. bones/cages,
  4. motion capture on points on the face and
  5. knowledge based solver deformations.

1. Shape based systems offer a fast playback as well as a high degree of fidelity of expressions. The technique involves modelling portions of the face mesh to approximate expressions and visemes and then blending the different sub meshes, known as morph targets or shapes. Perhaps the most accomplished character using this technique was Gollum, from The Lord of the Rings. Drawbacks of this technique are that they involve intensive manual labor, are specific to each character and must be animated by slider parameter tables.

2. Skeletal Muscle systems, physically based head models form another approach in modeling the head and face. Here the physical and anatomical characteristics of bones, tissues, and skin are simulated to provide a realistic appearance (e.g. spring-like elasticity). Such methods can be very powerful for creating realism but the complexity of facial structures make them computationally expensive, and difficult to create. Considering the effectiveness of parameterized models for communicative purposes (as explained in the next section), it may be argued that physically based models are not a very efficient choice in many applications. This does not deny the advantages of physically based models and the fact that they can even be used within the context of parameterized models to provide local details when needed. Waters, Terzopoulos, Kahler, and Seidel (among others) have developed physically based facial animation systems.

3. 'Envelope Bones' or 'Cages' are commonly used in games. They produce simple and fast models, but are not prone to portray subtlety.

4. 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 criticised for having fallen in the 'uncanny valley', the realm where animation realism is sufficient for human recognition but fails to convey the emotional message. 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. A recent technology developed at the Applied Geometry Group and Computer Vision Laboratory at ETH Zurich achieves 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.

5. Deformation Solver Face Robot.

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