# A Content Transformation Block For Image Style Transfer

arXiv: https://arxiv.org/abs/2003.08407 · HTML: https://mlsanity.com/paper/2003.08407/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2020.

## Built on by (most cited first, 19 of 19)

- [Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning](https://mlsanity.com/paper/2205.09542/) (2022)
- [DRB-GAN: A Dynamic ResBlock Generative Adversarial Network for Artistic Style Transfer](https://mlsanity.com/paper/2108.07379/) (2021)
- [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)
- [AesUST: Towards Aesthetic-Enhanced Universal Style Transfer](https://mlsanity.com/paper/2208.13016/) (2022)
- [Geometry Transfer for Stylizing Radiance Fields](https://mlsanity.com/paper/2402.00863/) (2024)
- [Name Your Style: An Arbitrary Artist-aware Image Style Transfer](https://mlsanity.com/paper/2202.13562/) (2022)
- [Edit360: 2D Image Edits to 3D Assets from Any Angle](https://mlsanity.com/paper/2506.10507/) (2025)
- [A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning](https://mlsanity.com/paper/2303.12710/) (2023)
- [PTDiffusion: Free Lunch for Generating Optical Illusion Hidden Pictures with Phase-Transferred Diffusion Model](https://mlsanity.com/paper/2503.06186/) (2025)
- [An Overview of Color Transfer and Style Transfer for Images and Videos](https://mlsanity.com/paper/2204.13339/) (2022)
- [ArtFusion: Controllable Arbitrary Style Transfer using Dual Conditional Latent Diffusion Models](https://mlsanity.com/paper/2306.09330/) (2023)
- [Frame Difference-Based Temporal Loss for Video Stylization](https://mlsanity.com/paper/2102.05822/) (2021)
- [WaSt-3D: Wasserstein-2 Distance for Scene-to-Scene Stylization on 3D Gaussians](https://mlsanity.com/paper/2409.17917/) (2024)
- [TSSAT: Two-Stage Statistics-Aware Transformation for Artistic Style Transfer](https://mlsanity.com/paper/2309.06004/) (2023)
- [VScript: Controllable Script Generation with Visual Presentation](https://mlsanity.com/paper/2203.00314/) (2022)
- [Interactive Style Transfer: All is Your Palette](https://mlsanity.com/paper/2203.13470/) (2022)
- [Improving Masked Style Transfer using Blended Partial Convolution](https://mlsanity.com/paper/2508.05769/) (2025)
- [Image-Guided Geometric Stylization of 3D Meshes](https://mlsanity.com/paper/2604.07795/) (2026)

## Abstract (arXiv)

Style transfer has recently received a lot of attention, since it allows to study fundamental challenges in image understanding and synthesis. Recent work has significantly improved the representation of color and texture and computational speed and image resolution. The explicit transformation of image content has, however, been mostly neglected: while artistic style affects formal characteristics of an image, such as color, shape or texture, it also deforms, adds or removes content details. This paper explicitly focuses on a content-and style-aware stylization of a content image. Therefore, we introduce a content transformation module between the encoder and decoder. Moreover, we utilize similar content appearing in photographs and style samples to learn how style alters content details and we generalize this to other class details. Additionally, this work presents a novel normalization layer critical for high resolution image synthesis. The robustness and speed of our model enables a video stylization in real-time and high definition. We perform extensive qualitative and quantitative evaluations to demonstrate the validity of our approach.