# Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks

arXiv: https://arxiv.org/abs/1603.01768 · HTML: https://mlsanity.com/paper/1603.01768/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2016.

## Built on by (most cited first, 22 of 22)

- [Splicing ViT Features for Semantic Appearance Transfer](https://mlsanity.com/paper/2201.00424/) (2022)
- [Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning](https://mlsanity.com/paper/2205.09542/) (2022)
- [Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer](https://mlsanity.com/paper/2104.05376/) (2021)
- [Learning Graph Neural Networks for Image Style Transfer](https://mlsanity.com/paper/2207.11681/) (2022)
- [Semantic Segmentation in Art Paintings](https://mlsanity.com/paper/2203.03238/) (2022)
- [AesUST: Towards Aesthetic-Enhanced Universal Style Transfer](https://mlsanity.com/paper/2208.13016/) (2022)
- [MicroAST: Towards Super-Fast Ultra-Resolution Arbitrary Style Transfer](https://mlsanity.com/paper/2211.15313/) (2022)
- [Texture Reformer: Towards Fast and Universal Interactive Texture Transfer](https://mlsanity.com/paper/2112.02788/) (2021)
- [DivSwapper: Towards Diversified Patch-based Arbitrary Style Transfer](https://mlsanity.com/paper/2101.06381/) (2021)
- [GenText: Unsupervised Artistic Text Generation via Decoupled Font and Texture Manipulation](https://mlsanity.com/paper/2207.09649/) (2022)
- [Disentangling Structure and Appearance in ViT Feature Space](https://mlsanity.com/paper/2311.12193/) (2023)
- [InfoStyler: Disentanglement Information Bottleneck for Artistic Style Transfer](https://mlsanity.com/paper/2307.16227/) (2023)
- [SCSA: A Plug-and-Play Semantic Continuous-Sparse Attention for Arbitrary Semantic Style Transfer](https://mlsanity.com/paper/2503.04119/) (2025)
- [FaceRefiner: High-Fidelity Facial Texture Refinement with Differentiable Rendering-based Style Transfer](https://mlsanity.com/paper/2601.04520/) (2026)
- [tile2tile: Learning Game Filters for Platformer Style Transfer](https://mlsanity.com/paper/2208.07699/) (2022)
- [Controllable Multi-domain Semantic Artwork Synthesis](https://mlsanity.com/paper/2308.10111/) (2023)
- [Threshold Designer Adaptation: Improved Adaptation for Designers in Co-creative Systems](https://mlsanity.com/paper/2205.09269/) (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)
- [Dual Pipeline Style Transfer with Input Distribution Differentiation](https://mlsanity.com/paper/2311.05432/) (2023)
- [Pluggable Style Representation Learning for Multi-Style Transfer](https://mlsanity.com/paper/2503.20368/) (2025)
- [Enhancing Multi-Region Stylization with Interior-Guided Boundary Repair](https://mlsanity.com/paper/2610.09706/) (2026)

## Abstract (arXiv)

Convolutional neural networks (CNNs) have proven highly effective at image synthesis and style transfer. For most users, however, using them as tools can be a challenging task due to their unpredictable behavior that goes against common intuitions. This paper introduces a novel concept to augment such generative architectures with semantic annotations, either by manually authoring pixel labels or using existing solutions for semantic segmentation. The result is a content-aware generative algorithm that offers meaningful control over the outcome. Thus, we increase the quality of images generated by avoiding common glitches, make the results look significantly more plausible, and extend the functional range of these algorithms---whether for portraits or landscapes, etc. Applications include semantic style transfer and turning doodles with few colors into masterful paintings!