# Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields

arXiv: https://arxiv.org/abs/1802.07101 · HTML: https://mlsanity.com/paper/1802.07101/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2018.

## Built on by (most cited first, 30 of 33)

- [AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer](https://mlsanity.com/paper/2108.03647/) (2021)
- [Zero-Shot Video Editing Using Off-The-Shelf Image Diffusion Models](https://mlsanity.com/paper/2303.17599/) (2023)
- [Dataset Distillation via Factorization](https://mlsanity.com/paper/2210.16774/) (2022)
- [AesPA-Net: Aesthetic Pattern-Aware Style Transfer Networks](https://mlsanity.com/paper/2307.09724/) (2023)
- [General Image-to-Image Translation with One-Shot Image Guidance](https://mlsanity.com/paper/2307.14352/) (2023)
- [Learning Graph Neural Networks for Image Style Transfer](https://mlsanity.com/paper/2207.11681/) (2022)
- [StylizedGS: Controllable Stylization for 3D Gaussian Splatting](https://mlsanity.com/paper/2404.05220/) (2024)
- [MicroAST: Towards Super-Fast Ultra-Resolution Arbitrary Style Transfer](https://mlsanity.com/paper/2211.15313/) (2022)
- [StyleSplat: 3D Object Style Transfer with Gaussian Splatting](https://mlsanity.com/paper/2407.09473/) (2024)
- [Any-to-Any Style Transfer: Making Picasso and Da Vinci Collaborate](https://mlsanity.com/paper/2304.09728/) (2023)
- [InstaStyle: Inversion Noise of a Stylized Image is Secretly a Style Adviser](https://mlsanity.com/paper/2311.15040/) (2023)
- [Geometry Transfer for Stylizing Radiance Fields](https://mlsanity.com/paper/2402.00863/) (2024)
- [Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization](https://mlsanity.com/paper/2103.11784/) (2021)
- [CoARF: Controllable 3D Artistic Style Transfer for Radiance Fields](https://mlsanity.com/paper/2404.14967/) (2024)
- [Texture Reformer: Towards Fast and Universal Interactive Texture Transfer](https://mlsanity.com/paper/2112.02788/) (2021)
- [Fine-Grained Image Style Transfer with Visual Transformers](https://mlsanity.com/paper/2210.05176/) (2022)
- [Training Generative Adversarial Networks in One Stage](https://mlsanity.com/paper/2103.00430/) (2021)
- [An Overview of Color Transfer and Style Transfer for Images and Videos](https://mlsanity.com/paper/2204.13339/) (2022)
- [ARF-Plus: Controlling Perceptual Factors in Artistic Radiance Fields for 3D Scene Stylization](https://mlsanity.com/paper/2308.12452/) (2023)
- [WISE: Whitebox Image Stylization by Example-based Learning](https://mlsanity.com/paper/2207.14606/) (2022)
- [Interactive Cartoonization with Controllable Perceptual Factors](https://mlsanity.com/paper/2212.09555/) (2022)
- [Interactive Multi-level Stroke Control for Neural Style Transfer](https://mlsanity.com/paper/2106.13787/) (2021)
- [Playing Lottery Tickets in Style Transfer Models](https://mlsanity.com/paper/2203.13802/) (2022)
- [Lightweight texture transfer based on texture feature preset](https://mlsanity.com/paper/2306.16846/) (2023)
- [Color and Texture Dual Pipeline Lightweight Style Transfer](https://mlsanity.com/paper/2310.01321/) (2023)
- [Deep Feature Rotation for Multimodal Image Style Transfer](https://mlsanity.com/paper/2202.04426/) (2022)
- [Neural Artistic Style Transfer with Conditional Adversaria](https://mlsanity.com/paper/2302.03875/) (2023)
- [Controlling Geometric Abstraction and Texture for Artistic Images](https://mlsanity.com/paper/2308.00148/) (2023)
- [Region-controlled Style Transfer](https://mlsanity.com/paper/2310.15658/) (2023)
- [Dual Pipeline Style Transfer with Input Distribution Differentiation](https://mlsanity.com/paper/2311.05432/) (2023)

## Abstract (arXiv)

The Fast Style Transfer methods have been recently proposed to transfer a photograph to an artistic style in real-time. This task involves controlling the stroke size in the stylized results, which remains an open challenge. In this paper, we present a stroke controllable style transfer network that can achieve continuous and spatial stroke size control. By analyzing the factors that influence the stroke size, we propose to explicitly account for the receptive field and the style image scales. We propose a StrokePyramid module to endow the network with adaptive receptive fields, and two training strategies to achieve faster convergence and augment new stroke sizes upon a trained model respectively. By combining the proposed runtime control strategies, our network can achieve continuous changes in stroke sizes and produce distinct stroke sizes in different spatial regions within the same output image.