Selected papers, corresponding codes and pre-trained models in our review paper "Neural Style Transfer: A Review"

If I missed your paper in this review, please email me or just pull a request here. I am more than happy to add it. Thanks!

• [Apr, 2018] We have released a new version of the paper with significant changes at: https://arxiv.org/pdf/1705.04058.pdf
  Appreciate the feedback!

• [Feb, 2018] Update the Images (Images_neuralStyleTransferReview_v2) in the Materials. Add the results of Li et al.'s NIPS 2017 paper.

• [Jan, 2018] Pre-trained models and all the content images, the style images, and the stylized results in the paper have been released.

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 Yu, Yizhou and Song, Mingli},
  journal={arXiv preprint arXiv:1705.04058},
  year={2017}
}

✅ Supplementary Materials

✅ Pre-trained Models

✅ Images (v2)

✅ [A Neural Algorithm of Artistic Style] [Paper] (First Neural Style Transfer Paper)

❇️ Code:

• Torch-based
• TensorFlow-based
• TensorFlow-based with L-BFGS optimizer support
• Caffe-based
• Keras-based
• MXNet-based
• MatConvNet-based

✅ [Image Style Transfer Using Convolutional Neural Networks] [Paper] (CVPR 2016)

✅ [Incorporating Long-range Consistency in CNN-based Texture Generation] [Paper] (ICLR 2017)

❇️ Code:

•   Theano-based

✅ [Laplacian-Steered Neural Style Transfer] [Paper] (ACM MM 2017)

❇️ Code:

• Torch-based & TensorFlow-based

✅ [Demystifying Neural Style Transfer] [Paper] (Theoretical Explanation) (IJCAI 2017)

❇️ Code:

•   MXNet-based

✅ [Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses] [Paper]

✅ [Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis] [Paper] (CVPR 2016)

❇️ Code:

• Torch-based

✅ [Perceptual Losses for Real-Time Style Transfer and Super-Resolution] [Paper] (ECCV 2016)

❇️ Code:

• Torch-based
• TensorFlow-based
• Chainer-based

❇️ Pre-trained Models:

• Torch-models
• Chainer-models

✅ [Texture Networks: Feed-forward Synthesis of Textures and Stylized Images] [Paper] (ICML 2016)

❇️ Code:

• Torch-based
• TensorFlow-based

✅ [Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks] [Paper] (ECCV 2016)

❇️ Code:

• Torch-based

✅ [A Learned Representation for Artistic Style] [Paper] (ICLR 2017)

❇️ Code:

• TensorFlow-based

✅ [Multi-style Generative Network for Real-time Transfer] [Paper]  （arXiv, 03/2017）

❇️ Code:

• PyTorch-based
• Torch-based

✅ [Diversified Texture Synthesis With Feed-Forward Networks] [Paper] (CVPR 2017)

❇️ Code:

•   Torch-based

✅ [StyleBank: An Explicit Representation for Neural Image Style Transfer] [Paper] (CVPR 2017)

✅ [Fast Patch-based Style Transfer of Arbitrary Style] [Paper]

❇️ Code:

• Torch-based

✅ [Exploring the Structure of a Real-time, Arbitrary Neural Artistic Stylization Network] [Paper] (BMVC 2017)

❇️ Code:

• TensorFlow-based

✅ [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization] [Paper] (ICCV 2017)

❇️ Code:

• Torch-based
• TensorFlow-based with Keras
• TensorFlow-based without Keras

✅ [Universal Style Transfer via Feature Transforms] [Paper] (NIPS 2017)

❇️ Code:

• Torch-based
• TensorFlow-based
• PyTorch-based

✅ [Preserving Color in Neural Artistic Style Transfer] [Paper]

✅ [Controlling Perceptual Factors in Neural Style Transfer] [Paper] (CVPR 2017)

❇️ Code:

• Torch-based

✅ [Content-Aware Neural Style Transfer] [Paper]

✅ [Towards Deep Style Transfer: A Content-Aware Perspective] [Paper] (BMVC 2016)

✅ [Neural Doodle_Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artwork] [Paper]

✅ [Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artwork] [Paper]

❇️ Code:

• Torch-based

✅ [The Contextual Loss for Image Transformation with Non-Aligned Data] [Paper]

❇️ Code:

• TensorFlow-based

✅ [Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis] [Paper] (CVPR 2017)

❇️ Code:

• Torch-based

✅ [Instance Normalization：The Missing Ingredient for Fast Stylization] [Paper]

❇️ Code:

• Torch-based

✅ [Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfer] [Paper] (CVPR 2017)

❇️ Code:

• TensorFlow-based

✅ Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields[Paper]

❇️ Code:

• TensorFlow-based

✅ [Depth-Preserving Style Transfer] [Paper]

❇️ Code:

• Torch-based

✅ [Depth-Aware Neural Style Transfer] [Paper] (NPAR 2017)

✅ [Neural Style Transfer: A Paradigm Shift for Image-based Artistic Rendering?] [Paper] (NPAR 2017)

✅ [Pictory: Combining Neural Style Transfer and Image Filtering] [Paper] (ACM SIGGRAPH 2017 Appy Hour)

✅ [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] (ICASSP 2017)

✅ [Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN] [Paper] (ACPR 2017)

✅ [Artistic Style Transfer for Videos] [Paper] (GCPR 2016)

❇️ Code:

• Torch-based

✅ [DeepMovie: Using Optical Flow and Deep Neural Networks to Stylize Movies] [Paper]

✅ [Characterizing and Improving Stability in Neural Style Transfer] [Paper]) (ICCV 2017)

✅ [Coherent Online Video Style Transfer] [Paper] (ICCV 2017)

✅ [Real-Time Neural Style Transfer for Videos] [Paper] (CVPR 2017)

✅ [Deep Photo Style Transfer] [Paper] (CVPR 2017)

❇️ Code:

• Torch-based
• TensorFlow-based

✅ [A Closed-form Solution to Photorealistic Image Stylization] [Paper]

❇️ Code:

• PyTorch-based

✅ [Decoder Network Over Lightweight Reconstructed Feature for Fast Semantic Style Transfer] [Paper] (ICCV 2017)

✅ [Stereoscopic Neural Style Transfer] [Paper] (CVPR 2018)

✅ [Awesome Typography: Statistics-based Text Effects Transfer][Paper] (CVPR 2017)

❇️ Code:

• Matlab-based

✅ [Neural Font Style Transfer][Paper] (ICDAR 2017)

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

✅ [Separating Style and Content for Generalized Style Transfer][Paper] (CVPR 2018)

✅ Visual Attribute Transfer through Deep Image Analogy[Paper] (SIGGRAPH 2017)

❇️ Code:

• Caffe-based

✅ Fashion Style Generator [Paper] (IJCAI 2017)

✅ Deep Painterly Harmonization [Paper]

❇️ Code:

• Torch-based

✅ Fast Face-Swap Using Convolutional Neural Networks [Paper] (ICCV 2017)

✅ Prisma

✅ Ostagram

❇️ Code:

• Website code

✅ Deep Forger

✅ NeuralStyler

✅ Style2Paints

❇️ Code:

• Website code

✅ [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]

✅ [ProsumerFX: Mobile Design of Image Stylization Components] [Paper]

✅ [Pictory - Neural Style Transfer and Editing with coreML] [Paper]

✅ [Tiny Transform Net for Mobile Image Stylization] [Paper] (ICMR 2017)

✅ [Caffe2Go][https://code.facebook.com/posts/196146247499076/delivering-real-time-ai-in-the-palm-of-your-hand/]

✅ [Supercharging Style Transfer][https://research.googleblog.com/2016/10/supercharging-style-transfer.html]

✅ [Issue of Layer Chosen Strategy][http://yongchengjing.com/pdf/Issue_layerChosenStrategy_neuralStyleTransfer.pdf]

✅ [Picking an optimizer for Style Transfer][https://blog.slavv.com/picking-an-optimizer-for-style-transfer-86e7b8cba84b]