Proceedings: GI 2017

Generating Calligraphic Trajectories with Model Predictive Control

Daniel Berio (Goldsmiths, University of London), Sylvain Calinon (Idiap Research Institute), Frederic Fol Leymarie (Goldsmiths, University of London)

Proceedings of Graphics Interface 2017: Edmonton, Alberta, 16-19 May 2017, 132 - 139

DOI 10.20380/GI2017.17

  • BibTex

    author = {Berio, Daniel and Calinon, Sylvain and Leymarie, Frederic Fol},
    title = {Generating Calligraphic Trajectories with Model Predictive Control},
    booktitle = {Proceedings of Graphics Interface 2017},
    series = {GI 2017},
    year = {2017},
    issn = {0713-5424},
    isbn = {978-0-9947868-2-1},
    location = {Edmonton, Alberta},
    pages = {132 -- 139},
    numpages = {8},
    doi = {10.20380/GI2017.17},
    publisher = {Canadian Human-Computer Communications Society / Soci{\'e}t{\'e} canadienne du dialogue humain-machine},
  • Supplementary Media


We describe a methodology for the interactive definition of curves and motion paths using a stochastic formulation of optimal control. We demonstrate how the same optimization framework can be used in different ways to generate curves and traces that are geometrically and dynamically similar to the ones that can be seen in art forms such as calligraphy or graffiti art. The method provides a probabilistic description of trajectories that can be edited similarly to the control polygon typically used in the popular spline based methods. Furthermore, it also encapsulates movement kinematics, deformations and variability. The user is then provided with a simple interactive interface that can generate multiple movements and traces at once, by visually defining a distribution of trajectories rather than a single one. The input to our method is a sparse sequence of targets defined as multivariate Gaussians. The output is a dynamical system generating curves that are natural looking and reflect the kinematics of a movement, similar to that produced by human drawing or writing.