mlsanityData to 14 Oct 2026

Toward Modeling Creative Processes for Algorithmic Painting

arXiv 2205.01605 ↗First posted 3 May 2022cs.AI cs.CV cs.GRcited by 9

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Abstract (arXiv)

This paper proposes a framework for computational modeling of artistic painting algorithms, inspired by human creative practices. Based on examples from expert artists and from the author's own experience, the paper argues that creative processes often involve two important components: vague, high-level goals (e.g., "make a good painting"), and exploratory processes for discovering new ideas. This paper then sketches out possible computational mechanisms for imitating those elements of the painting process, including underspecified loss functions and iterative painting procedures with explicit task decompositions.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 15 of its 66 references that are in the map. First posted on arXiv 3 May 2022.

Builds on

15 of 66 references.

References · 15

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  2. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,585
  3. 2017Curiosity-driven Exploration by Self-supervised Predictioncited by 1,194
  4. 2021CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoderscited by 187
  5. 2017CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Normscited by 96
  6. 2019Learning to Paint With Model-based Deep Reinforcement Learningcited by 71
  7. 2017Thinking Fast and Slow: Optimization Decomposition Across Timescalescited by 60
  8. 2020Stylized Neural Paintingcited by 50
  9. 2019Neural Painters: A learned differentiable constraint for generating brushstroke paintingscited by 28
  10. 2020Sequential Gallery for Interactive Visual Design Optimizationcited by 25
  11. 2019Unsupervised Doodling and Painting with Improved SPIRALcited by 21
  12. 2020Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting Agentcited by 15
3 more
  1. 2021Learning Task Decomposition with Ordered Memory Policy Networkcited by 15
  2. 2019LPaintB: Learning to Paint from Self-Supervisioncited by 5
  3. 2020Exploring Crowd Co-creation Scenarios for Sketchescited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 4; 2025: 1; 2026: 1.

9 papers.

  1. 2023Art and the science of generative AI: A deeper divecited by 95
  2. 2024ProcessPainter: Learn Painting Process from Sequence Datacited by 38
  3. 2024Block and Detail: Scaffolding Sketch-to-Image Generationcited by 9
  4. 2025Free-Lunch Color-Texture Disentanglement for Stylized Image Generationcited by 8
  5. 2024EDT: An Efficient Diffusion Transformer Framework Inspired by Human-like Sketchingcited by 5
  6. 2024AI Art Neural Constellation: Revealing the Collective and Contrastive State of AI-Generated and Human Artcited by 2
  7. 2023Robot Synesthesia: A Sound and Emotion Guided AI Paintercited by 0
  8. 2023Controlling Geometric Abstraction and Texture for Artistic Imagescited by 0
  9. 2026DiDE:Direct Injection with Color-Texture DEcoupling for 3D Stylizationcited by 0