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Animation of synthetic faces

In the last two decades, a variety of synthetic faces have been designed all over the world with the objective of their animation. The quality of facial models goes from a simple electronic curve on an oscilloscope, through a wide range of pre-stored human face shapes and more or less caricatural 2D vector-driven models of the most salient human face contours, to a very natural rendering of a 3D model (by mapping of real photographs on it, for instance). I think that, as in the case of acoustic speech synthesis, one must differentiate the final model from its animation technique. I suggest the reader refer to [46] for a turorial presentation of the techniques and methods used in facial animation. To sum up, Figure B.3 gives a classification of the most noticeable publications of designed systems along these two criteria. For simplification purposes, the table does not consider the work done by investigators to develop or apply the models cited, nor to synchronise them with speech synthesizers. Of course, the control parameters of a rule-driven model may be given by a code-book, after analysis of the acoustic wave-form, so the Y-axis could have been presented differently. However, the table aims at showing the basic characteristics of the various approaches by assigning the most suitable animation technique to a given model.

Figure B.3 : Classification of best-known face synthesizers based on the kind of facial model developed (X-axis), and on its underlying method of animation (Y-axis). Only the first author and date of publication are quoted.

Whatever the facial model, it may be animated for speech by three main methods: {

  1. A direct mapping from acoustics to geometry creates a correspondence between the energy in a filtered bandwith of the speech signal and the voltage input of an oscilloscope, and therefore with the horizontal or vertical size of an elliptical image [37,96]. An indirect mapping may also be achieved through a stochastic network which outputs a given image from a parametrization of the speech signal, after training of the network in order to make it match any possible input with its corresponding output as accurately as possible. Simons and Cox [313] used a Hidden Markov network, whereas Morishima et al. [230] preferred a connectionist network, for instance. In both, the inputs of the networks were LPC parameters calculated from the speech signal.
  2. When the facial model has previously been designed so that it can be animated through control parameters, it is possible to elaborate rules which simulate the gestural movements of the face. These commands can be based on a geometrical parametrization, such as jaw rotation or mouth width for instance, in the case of the 2D models by Brooke [45] or Guiard-Marigny [132], or the 3D models by Parke [258], Cohen and Massaro [71], or Guiard-Marigny et al. [133]. The commands can also be based on an anatomical description, such as the muscle actions simulated in the 3D models by Platt and Badler [273], Waters [355], Magnenat-Thalmann et al. [202], or Viaud [345].
  3. Facial models may only mimic a closed set of human expressions, whatever the tool used to create them: If no control parameters can be applied to the structure obtained in order to deform it and so generate different expressions, and if digitizing of multiple expressions is impossible, hand-modification by an expert is necessary in order to create a set of relevant key-frame images. The pre-stored images can then be concatenated as in cartoons, so that a skilled animator may achieve coherent animation. Such a technique has been widely employed, since it only requires a superficial model of the face, and as little physiological knowledge of speech production is needed to modify the external texture so that natural images can be duplicated (rotoscoping technique).
I also want to mention two other techniques that directly rely on human gestures:
  1. Expression slaving consists of the automatic measurement of geometrical characteristics (reference points or anatomical measurements) of a speakers face, which are mapped onto a facial model for local deformation [18,337,363,259,177]. In the computed-generated animation The Audition [224], the facial expressions of a synthetic dog are mapped onto its facial model from natural deformations automatically extracted from a human speakers face [259].
  2. Attempts have also been made to map expressions to a facial model from control commands driven in real-time by the hand of a skilled puppeteer [79]. In a famous computer-generated French TV series, Canaille Peluchegif, the body and face gestures of Mat, the synthetic ghost, are so created. Finally, a deformation that is rather based on the texture than on the shape of the model can be achieved by a mapping of various natural photos on the model [166].
The basic principles of these various animation techniques are represented in figure B.4 .

Figure B.4 : General architecture of Facial Animation showing the possible techniques.

next up previous contents
Next: Audio-visual speech synthesis Up: Audio-Visual Speech Synthesis Previous: Audio-Visual Speech Synthesis

Esprit Project 8579/MIAMI (Schomaker et al., '95)
Thu May 18 16:00:17 MET DST 1995