# Neural Abstract Style Transfer for Chinese Traditional Painting

arXiv: https://arxiv.org/abs/1812.03264 · HTML: https://mlsanity.com/paper/1812.03264/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2018.

## Built on by (most cited first, 6 of 6)

- [AesBench: An Expert Benchmark for Multimodal Large Language Models on Image Aesthetics Perception](https://mlsanity.com/paper/2401.08276/) (2024)
- [AesExpert: Towards Multi-modality Foundation Model for Image Aesthetics Perception](https://mlsanity.com/paper/2404.09624/) (2024)
- [CCLAP: Controllable Chinese Landscape Painting Generation via Latent Diffusion Model](https://mlsanity.com/paper/2304.04156/) (2023)
- [SCSA: A Plug-and-Play Semantic Continuous-Sparse Attention for Arbitrary Semantic Style Transfer](https://mlsanity.com/paper/2503.04119/) (2025)
- [Artistic Intelligence: A Diffusion-Based Framework for High-Fidelity Landscape Painting Synthesis](https://mlsanity.com/paper/2407.17229/) (2024)
- [DLP-GAN: learning to draw modern Chinese landscape photos with generative adversarial network](https://mlsanity.com/paper/2403.03456/) (2024)

## Abstract (arXiv)

Chinese traditional painting is one of the most historical artworks in the world. It is very popular in Eastern and Southeast Asia due to being aesthetically appealing. Compared with western artistic painting, it is usually more visually abstract and textureless. Recently, neural network based style transfer methods have shown promising and appealing results which are mainly focused on western painting. It remains a challenging problem to preserve abstraction in neural style transfer. In this paper, we present a Neural Abstract Style Transfer method for Chinese traditional painting. It learns to preserve abstraction and other style jointly end-to-end via a novel MXDoG-guided filter (Modified version of the eXtended Difference-of-Gaussians) and three fully differentiable loss terms. To the best of our knowledge, there is little work study on neural style transfer of Chinese traditional painting. To promote research on this direction, we collect a new dataset with diverse photo-realistic images and Chinese traditional paintings. In experiments, the proposed method shows more appealing stylized results in transferring the style of Chinese traditional painting than state-of-the-art neural style transfer methods.