  We propose a computational framework to learn stylisation patterns from
example drawings or writings, and then generate new trajectories that possess
similar stylistic qualities. We particularly focus on the generation and
stylisation of trajectories that are similar to the ones that can be seen in
calligraphy and graffiti art. Our system is able to extract and learn dynamic
and visual qualities from a small number of user defined examples which can be
recorded with a digitiser device, such as a tablet, mouse or motion capture
sensors. Our system is then able to transform new user drawn traces to be
kinematically and stylistically similar to the training examples. We implement
the system using a Recurrent Mixture Density Network (RMDN) combined with a
representation given by the parameters of the Sigma Lognormal model, a
physiologically plausible model of movement that has been shown to closely
reproduce the velocity and trace of human handwriting gestures.
