# Toward Modeling Creative Processes for Algorithmic Painting

arXiv: https://arxiv.org/abs/2205.01605 · HTML: https://mlsanity.com/paper/2205.01605/

## 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 (most cited first, 15 of 15)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders](https://mlsanity.com/paper/2106.14843/) (2021)
- [CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms](https://mlsanity.com/paper/1706.07068/) (2017)
- [Learning to Paint With Model-based Deep Reinforcement Learning](https://mlsanity.com/paper/1903.04411/) (2019)
- [Thinking Fast and Slow: Optimization Decomposition Across Timescales](https://mlsanity.com/paper/1704.07785/) (2017)
- [Stylized Neural Painting](https://mlsanity.com/paper/2011.08114/) (2020)
- [Neural Painters: A learned differentiable constraint for generating brushstroke paintings](https://mlsanity.com/paper/1904.08410/) (2019)
- [Sequential Gallery for Interactive Visual Design Optimization](https://mlsanity.com/paper/2005.04107/) (2020)
- [Unsupervised Doodling and Painting with Improved SPIRAL](https://mlsanity.com/paper/1910.01007/) (2019)
- [Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting Agent](https://mlsanity.com/paper/2012.10043/) (2020)
- [Learning Task Decomposition with Ordered Memory Policy Network](https://mlsanity.com/paper/2103.10972/) (2021)
- [LPaintB: Learning to Paint from Self-Supervision](https://mlsanity.com/paper/1906.06841/) (2019)
- [Exploring Crowd Co-creation Scenarios for Sketches](https://mlsanity.com/paper/2005.07328/) (2020)

## Built on by (most cited first, 9 of 9)

- [Art and the science of generative AI: A deeper dive](https://mlsanity.com/paper/2306.04141/) (2023)
- [ProcessPainter: Learn Painting Process from Sequence Data](https://mlsanity.com/paper/2406.06062/) (2024)
- [Block and Detail: Scaffolding Sketch-to-Image Generation](https://mlsanity.com/paper/2402.18116/) (2024)
- [Free-Lunch Color-Texture Disentanglement for Stylized Image Generation](https://mlsanity.com/paper/2503.14275/) (2025)
- [EDT: An Efficient Diffusion Transformer Framework Inspired by Human-like Sketching](https://mlsanity.com/paper/2410.23788/) (2024)
- [AI Art Neural Constellation: Revealing the Collective and Contrastive State of AI-Generated and Human Art](https://mlsanity.com/paper/2402.02453/) (2024)
- [Robot Synesthesia: A Sound and Emotion Guided AI Painter](https://mlsanity.com/paper/2302.04850/) (2023)
- [Controlling Geometric Abstraction and Texture for Artistic Images](https://mlsanity.com/paper/2308.00148/) (2023)
- [DiDE:Direct Injection with Color-Texture DEcoupling for 3D Stylization](https://mlsanity.com/paper/2610.02044/) (2026)

## 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.