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

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

AutoGraff: towards a computational understanding of graffiti writing and related art forms.

The aim of this thesis is to develop a system that generates letters and pictures with a style that is immediately recognizable as graffiti art or calligraphy. The proposed system can be used similarly to, and in tight integration with, conventional computer-aided geometric design tools and can be used to generate synthetic graffiti content for urban environments in games and in movies, and to guide robotic or fabrication systems that can materialise the output of the system with physical drawing media. The thesis is divided into two main parts. The first part describes a set of stroke primitives, building blocks that can be combined to generate different designs that resemble graffiti or calligraphy. These primitives mimic the process typically used to design graffiti letters and exploit well known principles of motor control to model the way in which an artist moves when incrementally tracing stylised letter forms. The second part demonstrates how these stroke primitives can be automatically recovered from input geometry defined in vector form, such as the digitised traces of writing made by a user, or the glyph outlines in a font. This procedure converts the input geometry into a seed that can be transformed into a variety of calligraphic and graffiti stylisations, which depend on parametric variations of the strokes

Dynamic Graffiti Stylisation with Stochastic Optimal Control

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 graffiti and calligraphy. We define various stylisations of a letter form over a common geometrical structure, which is given by the spatial layout of a sparse sequence of targets. Different stylisations are then generated by optimising the trajectories of a dynamical system that tracks the target sequence. The evolution of the dynamical system is computed with a stochastic formulation of optimal control, in which each target is defined probabilistically as a multivariate Gaussian. The covariance of each Gaussian explicitly defines 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 infinite 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 different stylisations of interactively or procedurally defined letters

Kinematic Reconstruction of Calligraphic Traces from Shape Features

Our goal is to be able to reproduce computationally calligraphic traces, e.g. as found in the art practices of graffiti and various forms of more traditional calligraphy, while mimicking the production process of such art forms. We design our user interfaces in a procedural generation and computer aided design (CAD) setting. As a result, we seek to seamlessly work between data used in design packages (without kinematics) and data easily digitised by users (e.g. online, with kinematics). To achieve these goals, we propose a method that allows to reconstruct kinematics from solely the geometric trace of handwritten trace in the form of parameters of the Sigma-Lognormal model. We purposely ignore the kinematics possibly embedded in the data in order to treat online data and vector patterns with the same procedure

GANCCRobot:Generative Adversarial Nets based Chinese Calligraphy Robot

Robotic calligraphy, as a typical application of robot movement planning, is of great significance for the inheritance and education of calligraphy culture. The existing implementations of such robots often suffer from its limited ability for font generation and evaluation, leading to poor writing style diversity and writing quality. This paper proposes a calligraphic robotic framework based on the generative adversarial nets (GAN) to address such limitation. The robot implemented using such framework is able to learn to write fundamental Chinese character strokes with rich diversities and good quality that is close to the human level, without the requirement of specifically designed evaluation functions thanks to the employment of the revised GAN. In particular, the type information of the stroke is introduced as condition information, and the latent codes are applied to maximize the style quality of the generated strokes. Experimental results demonstrate that the proposed model enables a calligraphic robot to successfully write fundamental Chinese strokes based on a given type and style, with overall good quality. Although the proposed model was evaluated in this report using calligraphy writing, the underpinning research is readily applicable to many other applications, such as robotic graffiti and character style conversion