  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.
