Stroke-based Rendering: From Heuristics to Deep Learning

TL;DR

This paper surveys the evolution of stroke-based rendering, highlighting how deep learning techniques are transforming artistic image creation from heuristic methods to neural rendering approaches.

Contribution

It provides a structured overview of stroke-based rendering algorithms, covering traditional heuristics, optimization, and recent deep learning methods.

Findings

Deep learning bridges the gap between stroke-based and pixel-based image generation.

Stroke-based rendering algorithms range from rule-based heuristics to neural network approaches.

Recent methods include differentiable vector graphics and reinforcement learning for painting.

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.