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

, , , , and . MOCO, page 25:1-25:8. ACM, (2017)

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Dynamic Graffiti Stylisation with Stochastic Optimal Control., , and . MOCO, page 18:1-18:8. ACM, (2017)Sketching and Layering Graffiti Primitives., , , and . Expressive, page 51-59. Eurographics Association, (2019)Generating Calligraphic Trajectories with Model Predictive Control., , and . Graphics Interface, page 132-139. Canadian Human-Computer Communications Society / ACM, (2017)Computational models for the analysis and synthesis of graffiti tag strokes., and . CAe@Expressive, page 35-47. Eurographics Association, (2015)Learning dynamic graffiti strokes with a compliant robot., , and . IROS, page 3981-3986. IEEE, (2016)Lognormality: An Open Window on Neuromotor Control., , , , , , , , , and 12 other author(s). IGS, volume 14285 of Lecture Notes in Computer Science, page 205-258. Springer, (2023)Calligraphic Stylisation Learning with a Physiologically Plausible Model of Movement and Recurrent Neural Networks., , , , and . MOCO, page 25:1-25:8. ACM, (2017)Expressive Curve Editing with the Sigma Lognormal Model., , and . Eurographics (Short Papers), page 33-36. Eurographics Association, (2018)Word-As-Image for Semantic Typography., , , , , and . ACM Trans. Graph., 42 (4): 151:1-151:11 (August 2023)Kinematics Reconstruction of Static Calligraphic Traces from Curvilinear Shape Features., , and . The Lognormality Principle, volume 88 of Series in Machine Perception and Artificial Intelligence, World Scientific, (2020)