Abstract
Abstract We propose the concept of a latent doodle space, a low‐dimensional space derived from a set of input doodles, or simple line drawings. The latent space provides a foundation for generating new drawings that are similar, but not identical to, the input examples. The two key components of this technique are 1) a heuristic algorithm for finding stroke correspondences between the drawings, and 2) the use of latent variable methods to automatically extract a low‐dimensional latent doodle space from the inputs. We present two practical applications that demonstrate the utility of this idea: first, a randomized stamp tool that creates a different image on every usage; and second, “personalized probabilistic fonts,” a handwriting synthesis technique that mimics the idiosyncrasies of one's own handwriting. Keywords: sketch, by‐example, style learning, scattered data interpolation, principal component analysis, radial basis functions, Gaussian processes, digital in‐betweening, handwriting synthesis
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