Autograff - PhD thesis

In the last few years, I have been researching ways to computationally reproduce the aesthetics of graffiti art. I recently defended my PhD thesis on this topic, which is titled AutoGraff: towards a computational understanding of graffiti writing and related art forms. You can download the thesis document here. 

The thesis covers techniques inspired by principles of human motor control to design and generate expressive curves, methods to rapidly design synthetic graffiti in different styles, and methods for graffiti stylization of text and fonts. Many of the graphics in this website are experiments that relate to the technologies that I developed in the thesis.

Examples

Different stroke stylizations applied to the same "motor plan"

User interaction (Weighted Sigma-Lognormal model)

Stroke rendering, animation and variation

Minimal intervention control (MIC), calligraphic stylization

MIC, stochastic sampling

MIC, tied/semi-tied covariance interface

MIC, motor plan stylization with semi-tied covarances

"Graffiti" stylizations textured in a game environment (Unreal Engine)