A large body of traditional animation exists, both from television and film, which contains many interesting characters and animation sequences. This dissertation shows how to incorporate that body of animation into motion libraries by making them re-usable. The most desirable qualities of traditional animation are the nuances an artist adds to each character, giving that character personality and style. As such, the focus is on semi-automatic techniques that allow the re-use of traditional animation, yet include the artist at every step of the process. The objective is to provide a method of re-using traditional animation by creating novel animations from a library of existing hand-drawn cartoons. Sequences of similar-looking cartoon data are combined into a user-directed animation.;This dissertation first addresses the issue of preparing the cartoon images to be incorporated into a motion library. Three methods are investigated for segmenting the cartoon images from their backgrounds: an ad hoc method, level sets, and support vector machines. We find that support vector machines are robust to artifacts in the cartoon images and are able to segment full-size images in a few seconds. Secondly, a method of nonlinear dimensionality reduction is applied to the cartoon images to discover a lower-dimensional manifold of the data. This manifold is traversed to create new sequences of cartoon animation, maintaining a model-free method, i.e., no a priori knowledge of the drawing or character is required. Finally, a radial basis function implicit surface modeling technique and a fast non-rigid elastic registration algorithm are combined to provide inbetween contours and textures given two key images of traditional animation.
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