# Multi-style Generative Network for Real-time Transfer

arXiv: https://arxiv.org/abs/1703.06953 · HTML: https://mlsanity.com/paper/1703.06953/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Mar 2017.

## Built on by (most cited first, 30 of 61)

- [AdaAttN: Revisit Attention Mechanism in Arbitrary Neural Style Transfer](https://mlsanity.com/paper/2108.03647/) (2021)
- [ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows](https://mlsanity.com/paper/2103.16877/) (2021)
- [Domain Enhanced Arbitrary Image Style Transfer via Contrastive Learning](https://mlsanity.com/paper/2205.09542/) (2022)
- [DG-Font: Deformable Generative Networks for Unsupervised Font Generation](https://mlsanity.com/paper/2104.03064/) (2021)
- [Multimodal Object Detection via Probabilistic Ensembling](https://mlsanity.com/paper/2104.02904/) (2021)
- [Deformable Sprites for Unsupervised Video Decomposition](https://mlsanity.com/paper/2204.07151/) (2022)
- [SSH: A Self-Supervised Framework for Image Harmonization](https://mlsanity.com/paper/2108.06805/) (2021)
- [BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation](https://mlsanity.com/paper/2110.11728/) (2021)
- [Drafting and Revision: Laplacian Pyramid Network for Fast High-Quality Artistic Style Transfer](https://mlsanity.com/paper/2104.05376/) (2021)
- [Federated Domain Generalization for Image Recognition via Cross-Client Style Transfer](https://mlsanity.com/paper/2210.00912/) (2022)
- [DRB-GAN: A Dynamic ResBlock Generative Adversarial Network for Artistic Style Transfer](https://mlsanity.com/paper/2108.07379/) (2021)
- [Style-Aware Normalized Loss for Improving Arbitrary Style Transfer](https://mlsanity.com/paper/2104.10064/) (2021)
- [Learning Graph Neural Networks for Image Style Transfer](https://mlsanity.com/paper/2207.11681/) (2022)
- [Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity](https://mlsanity.com/paper/2111.02338/) (2021)
- [Towards Ultra-Resolution Neural Style Transfer via Thumbnail Instance Normalization](https://mlsanity.com/paper/2103.11784/) (2021)
- [Testing Human Ability To Detect Deepfake Images of Human Faces](https://mlsanity.com/paper/2212.05056/) (2022)
- [S2WAT: Image Style Transfer via Hierarchical Vision Transformer using Strips Window Attention](https://mlsanity.com/paper/2210.12381/) (2022)
- [StyleTex: Style Image-Guided Texture Generation for 3D Models](https://mlsanity.com/paper/2411.00399/) (2024)
- [Consistent Style Transfer](https://mlsanity.com/paper/2201.02233/) (2022)
- [Fine-Grained Image Style Transfer with Visual Transformers](https://mlsanity.com/paper/2210.05176/) (2022)
- [A Unified Arbitrary Style Transfer Framework via Adaptive Contrastive Learning](https://mlsanity.com/paper/2303.12710/) (2023)
- [Multiple Style Transfer via Variational AutoEncoder](https://mlsanity.com/paper/2110.07375/) (2021)
- [Industrial Style Transfer with Large-scale Geometric Warping and Content Preservation](https://mlsanity.com/paper/2203.12835/) (2022)
- [Training-Free Multi-Style Fusion Through Reference-Based Adaptive Modulation](https://mlsanity.com/paper/2509.18602/) (2025)
- [An Overview of Color Transfer and Style Transfer for Images and Videos](https://mlsanity.com/paper/2204.13339/) (2022)
- [Photo-realistic Neural Domain Randomization](https://mlsanity.com/paper/2210.12682/) (2022)
- [Uni-Fusion: Universal Continuous Mapping](https://mlsanity.com/paper/2303.12678/) (2023)
- [AnyPattern: Towards In-context Image Copy Detection](https://mlsanity.com/paper/2404.13788/) (2024)
- [SaMam: Style-aware State Space Model for Arbitrary Image Style Transfer](https://mlsanity.com/paper/2503.15934/) (2025)
- [Vis2Mus: Exploring Multimodal Representation Mapping for Controllable Music Generation](https://mlsanity.com/paper/2211.05543/) (2022)

## Abstract (arXiv)

Despite the rapid progress in style transfer, existing approaches using feed-forward generative network for multi-style or arbitrary-style transfer are usually compromised of image quality and model flexibility. We find it is fundamentally difficult to achieve comprehensive style modeling using 1-dimensional style embedding. Motivated by this, we introduce CoMatch Layer that learns to match the second order feature statistics with the target styles. With the CoMatch Layer, we build a Multi-style Generative Network (MSG-Net), which achieves real-time performance. We also employ an specific strategy of upsampled convolution which avoids checkerboard artifacts caused by fractionally-strided convolution. Our method has achieved superior image quality comparing to state-of-the-art approaches. The proposed MSG-Net as a general approach for real-time style transfer is compatible with most existing techniques including content-style interpolation, color-preserving, spatial control and brush stroke size control. MSG-Net is the first to achieve real-time brush-size control in a purely feed-forward manner for style transfer. Our implementations and pre-trained models for Torch, PyTorch and MXNet frameworks will be publicly available.