# A Unified Framework for Generalizable Style Transfer: Style and Content Separation

arXiv: https://arxiv.org/abs/1806.05173 · HTML: https://mlsanity.com/paper/1806.05173/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2018.

## Built on by (most cited first, 11 of 11)

- [AttenST: A Training-Free Attention-Driven Style Transfer Framework with Pre-Trained Diffusion Models](https://mlsanity.com/paper/2503.07307/) (2025)
- [DiffArtist: Towards Structure and Appearance Controllable Image Stylization](https://mlsanity.com/paper/2407.15842/) (2024)
- [Scalable Font Reconstruction with Dual Latent Manifolds](https://mlsanity.com/paper/2109.06627/) (2021)
- [AIComposer: Any Style and Content Image Composition via Feature Integration](https://mlsanity.com/paper/2507.20721/) (2025)
- [SIGAN: A Novel Image Generation Method for Solar Cell Defect Segmentation and Augmentation](https://mlsanity.com/paper/2104.04953/) (2021)
- [UStyle: Waterbody Style Transfer of Underwater Scenes by Depth-Guided Feature Synthesis](https://mlsanity.com/paper/2503.11893/) (2025)
- [Local Style Awareness of Font Images](https://mlsanity.com/paper/2310.06337/) (2023)
- [Generalized W-Net: Arbitrary-style Chinese Character Synthesization](https://mlsanity.com/paper/2406.06847/) (2024)
- [Less is More: Masking Elements in Image Condition Features Avoids Content Leakages in Style Transfer Diffusion Models](https://mlsanity.com/paper/2502.07466/) (2025)
- [CFNet: Optimizing Remote Sensing Change Detection through Content-Aware Enhancement](https://mlsanity.com/paper/2503.08505/) (2025)
- [Structure-Level Disentangled Diffusion for Few-Shot Chinese Font Generation](https://mlsanity.com/paper/2602.18874/) (2026)

## Abstract (arXiv)

Image style transfer has drawn broad attention in recent years. However, most existing methods aim to explicitly model the transformation between different styles, and the learned model is thus not generalizable to new styles. We here propose a unified style transfer framework for both character typeface transfer and neural style transfer tasks leveraging style and content separation. A key merit of such framework is its generalizability to new styles and contents. The overall framework consists of style encoder, content encoder, mixer and decoder. The style encoder and content encoder are used to extract the style and content representations from the corresponding reference images. The mixer integrates the above two representations and feeds it into the decoder to generate images with the target style and content. During training, the encoder networks learn to extract styles and contents from limited size of style/content reference images. This learning framework allows simultaneous style transfer among multiple styles and can be deemed as a special `multi-task' learning scenario. The encoders are expected to capture the underlying features for different styles and contents which is generalizable to new styles and contents. Under this framework, we design two individual networks for character typeface transfer and neural style transfer, respectively. For character typeface transfer, to separate the style features and content features, we leverage the conditional dependence of styles and contents given an image. For neural style transfer, we leverage the statistical information of feature maps in certain layers to represent style. Extensive experimental results have demonstrated the effectiveness and robustness of the proposed methods.