/Neural-Style-Transfer-Papers

:pencil2: Neural Style Transfer: A Review

Neural-Style-Transfer-Papers :art:

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)

❇️ 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:

✅ Deep Forger

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

  • [Awesome Typography: Statistics-based Text Effects Transfer][Paper]

  • [Rewrite: Neural Style Transfer For Chinese Fonts][Project]