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

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

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

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

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

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

Quantifying scribal behavior : a novel approach to digital paleography

We propose a novel approach for analyzing scribal behavior quantitatively using information about the handwriting of characters. To implement this approach, we develop a computational framework that recovers this information and decomposes the characters into primitives (called strokes) to create a hierarchically structured representation. We then propose a number of intuitive metrics quantifying various facets of scribal behavior, which are derived from the recovered information and character structure. We further propose the use of techniques modeling the generation of handwriting to directly study the changes in writing behavior. We then present a case study in which we use our framework and metrics to analyze the development of four major Indic scripts. We show that our framework and metrics coupled with appropriate statistical methods can provide great insight into scribal behavior by discovering specific trends and phenomena with quantitative methods. We also illustrate the use of handwriting modeling techniques in this context to study the divergence of the Brahmi script into two daughter scripts. We conduct a user study with domain experts to evaluate our framework and salient results from the case study, and we elaborate on the results of this evaluation. Finally, we present our conclusions and discuss the limitations of our research along with future work that needs to be done

Abstractions for Probabilistic Programming to Support Model Development

Probabilistic programming is a recent advancement in probabilistic modeling whereby we can express a model as a program with little concern for the details of probabilistic inference. Probabilistic programming thereby provides a clean and powerful abstraction to its users, letting even non-experts develop clear and concise models that can leverage state-of-the-art computational inference algorithms. This model-as-program representation also presents a unique opportunity: we can apply methods from the study of programming languages directly onto probabilistic models. By developing techniques to analyze, transform, or extend the capabilities of probabilistic programs, we can immediately improve the workflow of probabilistic modeling and benefit all of its applications throughout science and industry. The aim of this dissertation is to support an ideal probabilistic modeling workflow byaddressing two limitations of probabilistic programming: that a program can only represent one model; and that the structure of the model that it represents is often opaque to users and to the compiler. In particular, I make the following primary contributions: (1) I introduce Multi-Model Probabilistic Programming: an extension of probabilistic programming whereby a program can represent a network of interrelated models. This new representation allows users to construct and leverage spaces of models in the same way that probabilistic programs do for individual models. Multi-Model Probabilistic Programming lets us visualize and navigate solution spaces, track and document model development paths, and audit modeler degrees of freedom to mitigate issues like p-hacking. It also provides an efficient computational foundation for the automation of model-space applications like model search, sensitivity analysis, and ensemble methods. I give a formal language specification and semantics for Multi-Model Probabilistic Programming built on the Stan language, I provide algorithms for the fundamental model-space operations along with proofs of correctness and efficiency, and I present a prototype implementation, with which I demonstrate a variety of practical applications. (2) I present a method for automatically transforming probabilistic programs into semantically related forms by using static analysis and constraint solving to recover the structure of their underlying models. In particular, I automate two general model transformations that are required for diagnostic checks which are important steps of a model-building workflow. Automating these transformations frees the user from manually rewriting their models, thereby avoiding potential correctness and efficiency issues. (3) I present a probabilistic program analysis tool, “Pedantic Mode”, that automatically warns users about potential statistical issues with the model described by their program. “Pedantic Mode” uses specialized static analysis methods to decompose the structure of the underlying model. Lastly, I discuss future work in these areas, such as advanced model-space algorithms and other general-purpose model transformations. I also discuss how these ideas may fit into future modeling workflows as technologies

Quantifying, Modeling and Managing How People Interact with Visualizations on the Web

The growing number of interactive visualizations on the web has made it possible for the general public to access data and insights that were once only available to domain experts. At the same time, this rise has yielded new challenges for visualization creators, who must now understand and engage a growing and diverse audience. To bridge this gap between creators and audiences, we explore and evaluate components of a design-feedback loop that would enable visualization creators to better accommodate their audiences as they explore the visualizations. In this dissertation, we approach this goal by quantifying, modeling and creating tools that manage people’s open-ended explorations of visualizations on the web. In particular, we: 1. Quantify the effects of design alternatives on people’s interaction patterns in visualizations. We define and evaluate two techniques: HindSight (encoding a user’s interaction history) and text-based search, where controlled experiments suggest that design details can significantly modulate the interaction patterns we observe from participants using a given visualization. 2. Develop new metrics that characterize facets of people’s exploration processes. Specifically, we derive expressive metrics describing interaction patterns such as exploration uniqueness, and use Bayesian inference to model distributional effects on interaction behavior. Our results show that these metrics capture novel patterns in people’s interactions with visualizations. 3. Create tools that manage and analyze an audience’s interaction data for a given visualization. We develop a prototype tool, ReVisIt, that visualizes an audience’s interactions with a given visualization. Through an interview study with visualization creators, we found that ReVisIt make creators aware of individual and overall trends in their audiences’ interaction patterns. By establishing some of the core elements of a design-feedback loop for visualization creators, the results in this research may have a tangible impact on the future of publishing interactive visualizations on the web. Equipped with techniques, metrics, and tools that realize an initial feedback loop, creators are better able to understand the behavior and user needs, and thus create visualizations that make data and insights more accessible to the diverse audiences on the web