An example-based approach to style translation for line drawings
We present an example-based system for translating line drawings into di erent styles. The system is given a training set of many di erent lines, each drawn by an artist in various styles, which is used to translate new lines made by a user into a particular desired style with a K-nearest neighbor algorithm. This algorithm ts each input line as a linear combination of the several training lines in the same style which are most similar to it. The t line can then be rendered in di erent styles because the training set contains versions of each training line in each style. By describing input lines as linear combinations of training set lines, this procedure is expressive enough to t a broad range of input drawings. By restricting these linear combinations to contain only the most similar training set lines, this procedure is constrained enough to preserve the distinctive stylistic features of translated lines. We represent input lines by splines with nonuniformly spaced control points, which emphasizes these stylistic features. Our example-based approach has a number of advantages over conventional parameteric approaches to translating style. It can handle styles which are di cult to describe parametrically, and its repertoire can be easily extended by the user at any time. Moreover, given appropriate representations, it can be generalized to modify the style of other kinds of graphics objects, such as the font of a letter or the movement style of an animated character.