# A Style-Aware Content Loss for Real-time HD Style Transfer

arXiv: https://arxiv.org/abs/1807.10201 · HTML: https://mlsanity.com/paper/1807.10201/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jul 2018.

## Built on by (most cited first, 30 of 58)

- [StyleDiffusion: Controllable Disentangled Style Transfer via Diffusion Models](https://mlsanity.com/paper/2308.07863/) (2023)
- [Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning](https://mlsanity.com/paper/2205.09542/) (2022)
- [ArtFID: Quantitative Evaluation of Neural Style Transfer](https://mlsanity.com/paper/2207.12280/) (2022)
- [Rethinking Style Transfer: From Pixels to Parameterized Brushstrokes](https://mlsanity.com/paper/2103.17185/) (2021)
- [DRB-GAN: A Dynamic ResBlock Generative Adversarial Network for Artistic Style Transfer](https://mlsanity.com/paper/2108.07379/) (2021)
- [Language-Driven Image Style Transfer](https://mlsanity.com/paper/2106.00178/) (2021)
- [ArtBank: Artistic Style Transfer with Pre-trained Diffusion Model and Implicit Style Prompt Bank](https://mlsanity.com/paper/2312.06135/) (2023)
- [AesPA-Net: Aesthetic Pattern-Aware Style Transfer Networks](https://mlsanity.com/paper/2307.09724/) (2023)
- [Style-Aware Normalized Loss for Improving Arbitrary Style Transfer](https://mlsanity.com/paper/2104.10064/) (2021)
- [Neural Neighbor Style Transfer](https://mlsanity.com/paper/2203.13215/) (2022)
- [FreeStyle: Free Lunch for Text-guided Style Transfer using Diffusion Models](https://mlsanity.com/paper/2401.15636/) (2024)
- [AesUST: Towards Aesthetic-Enhanced Universal Style Transfer](https://mlsanity.com/paper/2208.13016/) (2022)
- [Locally Stylized Neural Radiance Fields](https://mlsanity.com/paper/2309.10684/) (2023)
- [MicroAST: Towards Super-Fast Ultra-Resolution Arbitrary Style Transfer](https://mlsanity.com/paper/2211.15313/) (2022)
- [Style-ERD: Responsive and Coherent Online Motion Style Transfer](https://mlsanity.com/paper/2203.02574/) (2022)
- [Geometry Transfer for Stylizing Radiance Fields](https://mlsanity.com/paper/2402.00863/) (2024)
- [Towards Highly Realistic Artistic Style Transfer via Stable Diffusion with Step-aware and Layer-aware Prompt](https://mlsanity.com/paper/2404.11474/) (2024)
- [Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization](https://mlsanity.com/paper/2103.11784/) (2021)
- [Name Your Style: An Arbitrary Artist-aware Image Style Transfer](https://mlsanity.com/paper/2202.13562/) (2022)
- [Null-text Guidance in Diffusion Models is Secretly a Cartoon-style Creator](https://mlsanity.com/paper/2305.06710/) (2023)
- [U-StyDiT: Ultra-high Quality Artistic Style Transfer Using Diffusion Transformers](https://mlsanity.com/paper/2503.08157/) (2025)
- [Mimetic Models: Ethical Implications of AI that Acts Like You](https://mlsanity.com/paper/2207.09394/) (2022)
- [StylerDALLE: Language-Guided Style Transfer Using a Vector-Quantized Tokenizer of a Large-Scale Generative Model](https://mlsanity.com/paper/2303.09268/) (2023)
- [A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning](https://mlsanity.com/paper/2303.12710/) (2023)
- [Learning to Evaluate the Artness of AI-generated Images](https://mlsanity.com/paper/2305.04923/) (2023)
- [Depth-aware Neural Style Transfer using Instance Normalization](https://mlsanity.com/paper/2203.09242/) (2022)
- [DejaVu: Conditional Regenerative Learning to Enhance Dense Prediction](https://mlsanity.com/paper/2303.01573/) (2023)
- [Deep Translation Prior: Test-time Training for Photorealistic Style Transfer](https://mlsanity.com/paper/2112.06150/) (2021)
- [An Overview of Color Transfer and Style Transfer for Images and Videos](https://mlsanity.com/paper/2204.13339/) (2022)
- [DiffArtist: Towards Structure and Appearance Controllable Image Stylization](https://mlsanity.com/paper/2407.15842/) (2024)

## Abstract (arXiv)

Recently, style transfer has received a lot of attention. While much of this research has aimed at speeding up processing, the approaches are still lacking from a principled, art historical standpoint: a style is more than just a single image or an artist, but previous work is limited to only a single instance of a style or shows no benefit from more images. Moreover, previous work has relied on a direct comparison of art in the domain of RGB images or on CNNs pre-trained on ImageNet, which requires millions of labeled object bounding boxes and can introduce an extra bias, since it has been assembled without artistic consideration. To circumvent these issues, we propose a style-aware content loss, which is trained jointly with a deep encoder-decoder network for real-time, high-resolution stylization of images and videos. We propose a quantitative measure for evaluating the quality of a stylized image and also have art historians rank patches from our approach against those from previous work. These and our qualitative results ranging from small image patches to megapixel stylistic images and videos show that our approach better captures the subtle nature in which a style affects content.