Calligraphic Stylisation Learning with a Physiologically Plausible Model of Movement and Recurrent Neural Networks

Berio, Daniel; Akten, Memo; Leymarie, Frederic Fol; Grierson, Mick and Plamondon, Rejean. 2017. 'Calligraphic Stylisation Learning with a Physiologically Plausible Model of Movement and Recurrent Neural Networks'. In: Proceedings of the 4th International Conference on Movement and Computing (MOCO). London, United Kingdom 28-30 June 2017. [Conference or Workshop Item]

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Abstract or Description

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.

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Conference or Workshop Item (Paper)

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1 April 2017Accepted
28 June 2017Published

Event Location:

London, United Kingdom

Date range:

28-30 June 2017

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Date Deposited:

08 Aug 2017 09:39

Last Modified:

29 Apr 2020 16:28


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