# Chinese Painting Style Transfer Using Deep Generative Models

arXiv: https://arxiv.org/abs/2310.09978 · HTML: https://mlsanity.com/paper/2310.09978/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 7 references that are in the map.
- First posted on arXiv 15 Oct 2023.

## Builds on (most cited first, 3 of 3)

- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [A Neural Algorithm of Artistic Style](https://mlsanity.com/paper/1508.06576/) (2015)

## Built on by (most cited first, 1 of 1)

- [Artistic Intelligence: A Diffusion-Based Framework for High-Fidelity Landscape Painting Synthesis](https://mlsanity.com/paper/2407.17229/) (2024)

## Abstract (arXiv)

Artistic style transfer aims to modify the style of the image while preserving its content. Style transfer using deep learning models has been widely studied since 2015, and most of the applications are focused on specific artists like Van Gogh, Monet, Cezanne. There are few researches and applications on traditional Chinese painting style transfer. In this paper, we will study and leverage different state-of-the-art deep generative models for Chinese painting style transfer and evaluate the performance both qualitatively and quantitatively. In addition, we propose our own algorithm that combines several style transfer models for our task. Specifically, we will transfer two main types of traditional Chinese painting style, known as "Gong-bi" and "Shui-mo" (to modern images like nature objects, portraits and landscapes.