# Segmentation-Based Parametric Painting

arXiv: https://arxiv.org/abs/2311.14271 · HTML: https://mlsanity.com/paper/2311.14271/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 24 Nov 2023.

## Builds on (most cited first, 15 of 15)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [A Neural Representation of Sketch Drawings](https://mlsanity.com/paper/1704.03477/) (2017)
- [Synthesizing Programs for Images using Reinforced Adversarial Learning](https://mlsanity.com/paper/1804.01118/) (2018)
- [Learning to Paint With Model-based Deep Reinforcement Learning](https://mlsanity.com/paper/1903.04411/) (2019)
- [Paint Transformer: Feed Forward Neural Painting with Stroke Prediction](https://mlsanity.com/paper/2108.03798/) (2021)
- [Stylized Neural Painting](https://mlsanity.com/paper/2011.08114/) (2020)
- [Unsupervised Doodling and Painting with Improved SPIRAL](https://mlsanity.com/paper/1910.01007/) (2019)
- [FRIDA: A Collaborative Robot Painter with a Differentiable, Real2Sim2Real Planning Environment](https://mlsanity.com/paper/2210.00664/) (2022)
- [Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting Agent](https://mlsanity.com/paper/2012.10043/) (2020)
- [Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level Paintings](https://mlsanity.com/paper/2011.12589/) (2020)
- [Artistic Style in Robotic Painting; a Machine Learning Approach to Learning Brushstroke from Human Artists](https://mlsanity.com/paper/2007.03647/) (2020)
- [LPaintB: Learning to Paint from Self-Supervision](https://mlsanity.com/paper/1906.06841/) (2019)

## Built on by (most cited first, 3 of 3)

- [Vectorized Region Based Brush Strokes for Artistic Rendering](https://mlsanity.com/paper/2506.09969/) (2025)
- [AnimatePainter: A Self-Supervised Rendering Framework for Reconstructing Painting Process](https://mlsanity.com/paper/2503.17029/) (2025)
- [Look, Compare and Draw: Differential Query Transformer for Automatic Oil Painting](https://mlsanity.com/paper/2603.27720/) (2026)

## Abstract (arXiv)

We introduce a novel image-to-painting method that facilitates the creation of large-scale, high-fidelity paintings with human-like quality and stylistic variation. To process large images and gain control over the painting process, we introduce a segmentation-based painting process and a dynamic attention map approach inspired by human painting strategies, allowing optimization of brush strokes to proceed in batches over different image regions, thereby capturing both large-scale structure and fine details, while also allowing stylistic control over detail. Our optimized batch processing and patch-based loss framework enable efficient handling of large canvases, ensuring our painted outputs are both aesthetically compelling and functionally superior as compared to previous methods, as confirmed by rigorous evaluations. Code available at: https://github.com/manuelladron/semanticbasedpainting.git