神经风格转换 (Neural-Style-Transfer-Papers)
原文：https://github.com/ycjing/Neural-Style-Transfer-Papers
Neural-Style-Transfer-Papers
Selected papers, corresponding codes and pre-trained models in our review paper "Neural Style Transfer: A Review"
Citation
If you find this repository useful for your research, please cite
@article{jing2017neural,
title={Neural Style Transfer: A Review},
author={Jing, Yongcheng and Yang, Yezhou and Feng, Zunlei and Ye, Jingwen and Song, Mingli},
journal={arXiv preprint arXiv:1705.04058},
year={2017}
}
Pre-trained Models in Our Paper
✅[Coming Soon]
A Taxonomy of Current Methods
1. Descriptive Neural Methods Based On Image Iteration
1.1. MMD-based Descriptive Neural Methods
✅ [A Neural Algorithm of Artistic Style] [Paper] (First Neural Style Transfer Paper)
❇️ Code:
✅ [Image Style Transfer Using Convolutional Neural Networks] [Paper] (CVPR 2016)
✅ [Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses] [Paper] (CVPR 2017)
✅ [Demystifying Neural Style Transfer] [Paper] (Theoretical Explanation) (IJCAI 2017)
❇️ Code:
✅ [Content-Aware Neural Style Transfer] [Paper]
✅ [Towards Deep Style Transfer: A Content-Aware Perspective] [Paper] (BMVC 2016)
1.2. MRF-based Descriptive Neural Methods
✅ [Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis] [Paper] (CVPR 2016)
❇️ Code:
✅ [Neural Doodle_Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artwork] [Paper]
2. Generative Neural Methods Based On Model Iteration
✅ [Perceptual Losses for Real-Time Style Transfer and Super-Resolution] [Paper] (ECCV 2016)
❇️ Code:
❇️ Pre-trained Models:
✅ [Texture Networks: Feed-forward Synthesis of Textures and Stylized Images] [Paper] (ICML 2016)
❇️ Code:
✅ [Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis] [Paper] (CVPR 2017)
❇️ Code:
✅ [Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks] [Paper] (ECCV 2016)
❇️ Code:
✅ [A Learned Representation for Artistic Style] [Paper] (ICLR 2017)
❇️ Code:
✅ [Fast Patch-based Style Transfer of Arbitrary Style] [Paper]
❇️ Code:
Slight Modifications of Current Methods
1. Modifications of Descriptive Neural Methods
✅ [Exploring the Neural Algorithm of Artistic Style] [Paper]
✅ [Improving the Neural Algorithm of Artistic Style] [Paper]
✅ [Preserving Color in Neural Artistic Style Transfer] [Paper]
✅ [Controlling Perceptual Factors in Neural Style Transfer] [Paper]
❇️ Code:
2. Modifications of Generative Neural Methods
✅ [Instance Normalization：The Missing Ingredient for Fast Stylization] [Paper]
❇️ Code:
✅ [Depth-Preserving Style Transfer] [Paper]
❇️ Code:
Extensions to Specific Types of Images
✅ [Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artwork] [Paper]
❇️ Code:
✅ [Painting Style Transfer for Head Portraits Using Convolutional Neural Networks] [Paper] (SIGGRAPH 2016)
✅ [Son of Zorn's Lemma Targeted Style Transfer Using Instance-aware Semantic Segmentation] [Paper]
✅ [Artistic Style Transfer for Videos] [Paper] (GCPR 2016)
❇️ Code:
✅ [DeepMovie: Using Optical Flow and Deep Neural Networks to Stylize Movies] [Paper]
Application
✅ Prisma
✅ Ostagram
❇️ Code:
Application Papers
✅ [Bringing Impressionism to Life with Neural Style Transfer in Come Swim] [Paper]
✅ [Imaging Novecento. A Mobile App for Automatic Recognition of Artworks and Transfer of Artistic Styles] [Paper]
Blogs
✅ https://code.facebook.com/posts/196146247499076/delivering-real-time-ai-in-the-palm-of-your-hand/
✅ https://research.googleblog.com/2016/10/supercharging-style-transfer.html
Exciting New Directions
✅ Character Style Transfer
