# Attention-aware Multi-stroke Style Transfer

arXiv: https://arxiv.org/abs/1901.05127 · HTML: https://mlsanity.com/paper/1901.05127/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2019.

## Built on by (most cited first, 30 of 47)

- [StyTr2: Image Style Transfer with Transformers](https://mlsanity.com/paper/2105.14576/) (2021)
- [AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer](https://mlsanity.com/paper/2108.03647/) (2021)
- [ARF: Artistic Radiance Fields](https://mlsanity.com/paper/2206.06360/) (2022)
- [Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning](https://mlsanity.com/paper/2205.09542/) (2022)
- [Countering Malicious DeepFakes: Survey, Battleground, and Horizon](https://mlsanity.com/paper/2103.00218/) (2021)
- [Rethinking and Improving the Robustness of Image Style Transfer](https://mlsanity.com/paper/2104.05623/) (2021)
- [CCPL: Contrastive Coherence Preserving Loss for Versatile Style Transfer](https://mlsanity.com/paper/2207.04808/) (2022)
- [Domain-Aware Universal Style Transfer](https://mlsanity.com/paper/2108.04441/) (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)
- [Neural Neighbor Style Transfer](https://mlsanity.com/paper/2203.13215/) (2022)
- [Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaborate](https://mlsanity.com/paper/2304.09728/) (2023)
- [All-to-key Attention for Arbitrary Style Transfer](https://mlsanity.com/paper/2212.04105/) (2022)
- [Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization](https://mlsanity.com/paper/2103.11784/) (2021)
- [S2WAT: Image Style Transfer via Hierarchical Vision Transformer using Strips Window Attention](https://mlsanity.com/paper/2210.12381/) (2022)
- [Consistent Style Transfer](https://mlsanity.com/paper/2201.02233/) (2022)
- [DivSwapper: Towards Diversified Patch-based Arbitrary Style Transfer](https://mlsanity.com/paper/2101.06381/) (2021)
- [StylerDALLE: Language-Guided Style Transfer Using a Vector-Quantized Tokenizer of a Large-Scale Generative Model](https://mlsanity.com/paper/2303.09268/) (2023)
- [Master: Meta Style Transformer for Controllable Zero-Shot and Few-Shot Artistic Style Transfer](https://mlsanity.com/paper/2304.11818/) (2023)
- [Free-Lunch Color-Texture Disentanglement for Stylized Image Generation](https://mlsanity.com/paper/2503.14275/) (2025)
- [ReGO: Reference-Guided Outpainting for Scenery Image](https://mlsanity.com/paper/2106.10601/) (2021)
- [An Overview of Color Transfer and Style Transfer for Images and Videos](https://mlsanity.com/paper/2204.13339/) (2022)
- [Quality Evaluation of Arbitrary Style Transfer: Subjective Study and Objective Metric](https://mlsanity.com/paper/2208.00623/) (2022)
- [Deep Learning for Breast MRI Style Transfer with Limited Training Data](https://mlsanity.com/paper/2301.02069/) (2023)
- [Instruct-Video2Avatar: Video-to-Avatar Generation with Instructions](https://mlsanity.com/paper/2306.02903/) (2023)
- [StyleKeeper: Prevent Content Leakage using Negative Visual Query Guidance](https://mlsanity.com/paper/2510.06827/) (2025)
- [DiffuseST: Unleashing the Capability of the Diffusion Model for Style Transfer](https://mlsanity.com/paper/2410.15007/) (2024)
- [ArtFusion: Controllable Arbitrary Style Transfer using Dual Conditional Latent Diffusion Models](https://mlsanity.com/paper/2306.09330/) (2023)
- [A Training-Free Style-aligned Image Generation with Scale-wise Autoregressive Model](https://mlsanity.com/paper/2504.06144/) (2025)
- [SpectralCLIP: Preventing Artifacts in Text-Guided Style Transfer from a Spectral Perspective](https://mlsanity.com/paper/2303.09270/) (2023)

## Abstract (arXiv)

Neural style transfer has drawn considerable attention from both academic and industrial field. Although visual effect and efficiency have been significantly improved, existing methods are unable to coordinate spatial distribution of visual attention between the content image and stylized image, or render diverse level of detail via different brush strokes. In this paper, we tackle these limitations by developing an attention-aware multi-stroke style transfer model. We first propose to assemble self-attention mechanism into a style-agnostic reconstruction autoencoder framework, from which the attention map of a content image can be derived. By performing multi-scale style swap on content features and style features, we produce multiple feature maps reflecting different stroke patterns. A flexible fusion strategy is further presented to incorporate the salient characteristics from the attention map, which allows integrating multiple stroke patterns into different spatial regions of the output image harmoniously. We demonstrate the effectiveness of our method, as well as generate comparable stylized images with multiple stroke patterns against the state-of-the-art methods.