# LPaintB: Learning to Paint from Self-Supervision

arXiv: https://arxiv.org/abs/1906.06841 · HTML: https://mlsanity.com/paper/1906.06841/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2019.

## Built on by (most cited first, 5 of 5)

- [Toward Modeling Creative Processes for Algorithmic Painting](https://mlsanity.com/paper/2205.01605/) (2022)
- [Intelli-Paint: Towards Developing Human-like Painting Agents](https://mlsanity.com/paper/2112.08930/) (2021)
- [Stroke-based Rendering: From Heuristics to Deep Learning](https://mlsanity.com/paper/2302.00595/) (2022)
- [Segmentation-Based Parametric Painting](https://mlsanity.com/paper/2311.14271/) (2023)
- [Differentiable Stroke Planning with Dual Parameterization for Efficient and High-Fidelity Painting Creation](https://mlsanity.com/paper/2604.02752/) (2026)

## Abstract (arXiv)

We present a novel reinforcement learning-based natural media painting algorithm. Our goal is to reproduce a reference image using brush strokes and we encode the objective through observations. Our formulation takes into account that the distribution of the reward in the action space is sparse and training a reinforcement learning algorithm from scratch can be difficult. We present an approach that combines self-supervised learning and reinforcement learning to effectively transfer negative samples into positive ones and change the reward distribution. We demonstrate the benefits of our painting agent to reproduce reference images with brush strokes. The training phase takes about one hour and the runtime algorithm takes about 30 seconds on a GTX1080 GPU reproducing a 1000x800 image with 20,000 strokes.