mlsanityData to 7 Oct 2026

Stroke-based Rendering: From Heuristics to Deep Learning

arXiv 2302.00595 ↗First posted 30 Dec 2022cs.CV cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 45 of its 135 references that are in the map. First posted on arXiv 30 Dec 2022.

Builds on

45 of 135 references.

References · 45

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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 1; 2025: 1; 2026: 1.

4 papers.

  1. 2023Shape complexity estimation using VAEcited by 4
  2. 2024Lane Graph Extraction from Aerial Imagery via Lane Segmentation Refinement with Diffusion Modelscited by 4
  3. 2025Vectorized Region Based Brush Strokes for Artistic Renderingcited by 1
  4. 2026PaintCopilot: Modeling Painting as Autonomous Artistic Continuationcited by 0