Stroke-based Rendering: From Heuristics to Deep Learning
Abstract
In the last few years, artistic image-making with deep learning models has gained a considerable amount of traction. A large number of these models operate directly in the pixel space and generate raster images. This is however not how most humans would produce artworks, for example, by planning a sequence of shapes and strokes to draw. Recent developments in deep learning methods help to bridge the gap between stroke-based paintings and pixel photo generation. With this survey, we aim to provide a structured introduction and understanding of common challenges and approaches in stroke-based rendering algorithms. These algorithms range from simple rule-based heuristics to stroke optimization and deep reinforcement agents, trained to paint images with differentiable vector graphics and neural rendering.
Cite
@article{arxiv.2302.00595,
title = {Stroke-based Rendering: From Heuristics to Deep Learning},
author = {Florian Nolte and Andrew Melnik and Helge Ritter},
journal= {arXiv preprint arXiv:2302.00595},
year = {2023}
}