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

StrokeStyles: Stroke-based Segmentation and Stylization of Fonts

We develop a method to automatically segment a font’s glyphs into a set of overlapping and intersecting strokes with the aim of generating artistic stylizations. The segmentation method relies on a geometric analysis of the glyph’s outline, its interior, and the surrounding areas and is grounded in perceptually informed principles and measures. Our method does not require training data or templates and applies to glyphs in a large variety of input languages, writing systems, and styles. It uses the medial axis, curvilinear shape features that specify convex and concave outline parts, links that connect concavities, and seven junction types. We show that the resulting decomposition in strokes can be used to create variations, stylizations, and animations in different artistic or design-oriented styles while remaining recognizably similar to the input font

Aesthetics of graffiti: Comparison to text-based and pictorial artforms

Graffiti art is a controversial art form, and as such there has been little empirical work assessing its aesthetic value. A recent study examined image statistical properties of text-based artwork and revealed that images of text contain less global structure relative to fine detail compared to artworks. However, previous research did not include graffiti tags or murals, which reside in the space between text and visual art (Melmer et al., 2013). The current study investigated the image statistical properties and attractiveness of graffiti relative to other text-based and pictorial art forms, focusing additionally on the role of expertise. A series of images (N=140; graffiti, text and paintings) were presented to a group of observers with varying degrees of art interest and expertise (N=169). Findings revealed that image statistics predicted attractiveness ratings to images, and that biases against graffiti art are less salient in an expert sample

Computer Aided Design of Handwriting Trajectories with the Kinematic Theory of Rapid Human Movements

We present the Sigma Lognormal model as a potential tool for curve generation in computer graphics related applications. We discuss its extension and parameterisations for the interactive definition of handwriting, drawing and calligraphic trajectories. This results in an effcient trajectory synthesis method, that has a user interface similar to the ones commonly used with Bezier curves or splines, but with the added benefit of capturing the kinematics of human drawing or writing movements. Such kinematics produced by the model can then be exploited to generate realistic stroke animations or to facilitate expressive rendering methods

Computational Models for the Analysis and Synthesis of Graffiti Tag Strokes

In this paper we describe a system aimed at the generation and analysis of graffiti tags.We argue that the dynamics of the movement involved in generating tags is in large part — and at a higher degree with respect to many other visual art forms — determinant of their stylistic quality. To capture this notion computationally, we rely on a biophysically plausible model of handwriting gestures (the Sigma Lognormal Model proposed by Réjean Plamondon et al.) that permits the generation of curves which are aesthetically and kinetically similar to the ones made by a human hand when writing. We build upon this model and extend it in order to facilitate the interactive construction and manipulation of digital tags. We then describe a method that reconstructs any planar curve or a sequence of planar points with a set of corresponding model parameters. By doing so, we seek to recover plausible velocity and temporal information for a static trace. We present a number of applications of our system: (i) the interactive design of curves that closely resemble the ones typically observed in graffiti art; (ii) the stylisation and beautification of input point sequences via curves that evoke a smooth and rapidly executed movement; (iii) the generation of multiple instances of a synthetic tag from a single example. This last application is a step in the direction of our longer term plan of realising a system which is capable of automatically generating convincing images in the graffiti style space

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

A dominant theory of embodied aesthetic experience (Freedberg & Gallese, 2007) posits that the appreciation of visual art is linked to the artist’s movements when creating the artwork, yet a direct link between the kinematics of drawing actions and the aesthetics of drawing outcomes has not been experimentally demonstrated. Across four experiments we measured aesthetic responses of students from arts and non-arts backgrounds to drawing movements generated from computational models of human writing. Experiment 1 demonstrated that human-like drawing movements with bell-shaped velocity profiles (Sigma Lognormal (SL) and Minimum Jerk (MJ)) are perceived as more natural and pleasant than movements with a uniform profile, and in both Experiments 1 and 2 movements that were perceived as more natural were also preferred. Experiment 3 showed that this effect persists if lower-level dynamic stimulus features are fully matched across experimental and control conditions. Furthermore, aesthetic preference for human-like movements were associated with greater perceptual fluency in Experiment 3, evidenced by unbiased estimations of the duration of natural movements. In Experiment 4, line drawings with visual features consistent with the dynamics of natural, human-like movements were preferred, but only by art students. Our findings directly link the aesthetics of human action to the visual aesthetics of drawings, but highlight the importance of incorporating artistic expertise into embodied accounts of aesthetic experience

Generating Calligraphic Trajectories with Model Predictive Control

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

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

Learning dynamic graffiti strokes with a compliant robot

We present an approach to generate rapid and fluid drawing movements on a compliant Baxter robot, by taking advantage of the kinematic redundancy and torque control capabilities of the robot. We concentrate on the task of reproducing graffiti-stylised letter-forms with a marker. For this purpose, we exploit a compact lognormal-stroke based representation of movement to generate natural drawing trajectories. An Expectation-Maximisation (EM) algorithm is used to iteratively improve tracking performance with low gain feedback control. The resulting system captures the aesthetic and dynamic features of the style under investigation and permits its reproduction with a compliant controller that is safe for users surrounding the robot

Expressive Curve Editing with the Sigma Lognormal Model

We describe a practical application of the Sigma Lognormal model of handwriting movements for computer graphics applications that require the interactive or procedural definition of artistic or calligraphic traces. The method allows to easily edit curves with physiologically plausible kinematics that can be exploited in order to generate expressive brush renderings, natural looking stroke animations and easily generate stylistic variations of a trace