Dynamic Graffiti Stylisation with Stochastic Optimal Control

Abstract

We present a method for the interactive generation of stylised letters, curves and motion paths that are similar to the ones that can be observed in art forms such as gra ti and calligraphy. We de ne various stylisations of a le er form over a common geometrical structure, which is given by the spatial layout of a sparse sequence of targets. Di erent stylisations are then generated by optimising the trajectories of a dynamical system that tracks the target sequence.e evolution of the dynamical system is computed with a stochastic formulation of optimal control, in which each target is de ned probabilistically as a multivariate Gaussian. e covariance of each Gaussian explicitly de nes the variability as well as the curvilinear evolution of trajectory segments. Given this probabilistic formulation, the optimisation procedure results in a trajectory distribution rather than a single path. It is then possible to stochastically sample from the distribution an in nite number of dynamically and aesthetically consistent trajectories which mimic the variability that is typically observed in human drawing or writing. We further demonstrate how this system can be used together with a simple user interface in order to explore di erent stylisations of interactively or procedurally de ned le ers. CCS CONCEPTS•Computing methodologies →Procedural animation; •Humancentered computing →Gestural input; KEYWORDSHuman hand-writing movement modeling; procedural calligraphy; gra ti and tags generation; model predictive control; stochastic optimal control; smoothing splines; iconic and kinemic le er forms.

A dot that went for a walk: People prefer lines drawn with human‐like kinematics

“…Table 1 displays the descriptive statistics for pleasantness and naturalness ratings for the three movement models, showing that pleasantness and naturalness ratings are higher for the natural, human-like movement models (the SL and MJ) compared with the uniform model. (a) Initial trajectory randomly generated with the method described in Berio et al (2017). (b) Sigma-lognormal reconstruction of the input trace using the method described in Berio, Fol Leymarie, and Plamondon (2018).…”

A Developmental Evolutionary Learning Framework for Robotic Chinese Stroke Writing

“…Note that Figs. 7,9,11,13, and 15 merely illustrate the writing results with the highest score. If a result with a trajectory crossing situation is assessed, the result might not reflect the highest scores; therefore, these figures do not include many trajectory crossing situations.…”

“…Therefore, imitation learning [6] and other forms of learning [7] can be used to learn from human writing and to improve on initial (preprogrammed) trajectories. If a robot can produce strokes that meet human aesthetic standards, it would also be promising for solving other human aestheticsrelated tasks, such as robotic drawing and graffiti [8], [9].…”

GANCCRobot: Generative adversarial nets based chinese calligraphy robot

“…Robotics has been widely applied to promote human culture and education, such as robotic Chinese character writing [1,2], dancing, and drawing. Robotic writing is a particularly hot topic due to the great applicability of its key technology in other applications, including robotic drawing [3], industrial welding [4,5], and medical rehabilitation [6] among others. The essence of robotic writing is the generation of sequences of robotic actions in accordance with human evaluation criteria.…”