Calligraphic Stylisation Learning with a Physiologically Plausible Model of Movement and Recurrent Neural Networks. Berio, D., Akten, M., Leymarie, F. F., Grierson, M., & Plamondon, R. In Gillies, M. & Niehaus, K., editors, Proceedings of the 4th International Conference on Movement Computing, London, United Kingdom, June 28-30, 2017, pages 25:1–25:8, 2017. ACM.
Calligraphic Stylisation Learning with a Physiologically Plausible Model of Movement and Recurrent Neural Networks [link]Paper  doi  bibtex   
@inproceedings{DBLP:conf/moco/BerioALGP17,
  author    = {Daniel Berio and
               Memo Akten and
               Frederic Fol Leymarie and
               Mick Grierson and
               R{\'{e}}jean Plamondon},
  editor    = {Marco Gillies and
               Kiona Niehaus},
  title     = {Calligraphic Stylisation Learning with a Physiologically Plausible
               Model of Movement and Recurrent Neural Networks},
  booktitle = {Proceedings of the 4th International Conference on Movement Computing,
               London, United Kingdom, June 28-30, 2017},
  pages     = {25:1--25:8},
  publisher = {{ACM}},
  year      = {2017},
  url       = {https://doi.org/10.1145/3077981.3078049},
  doi       = {10.1145/3077981.3078049},
  timestamp = {Sun, 25 Oct 2020 01:00:00 +0200},
  biburl    = {https://dblp.org/rec/conf/moco/BerioALGP17.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Downloads: 0