├── README.md ├── framework_n4.png └── framework_n5.png /README.md: -------------------------------------------------------------------------------- 1 | # Neural-Style-Transfer-Papers :art: 2 | Selected papers, corresponding codes and pre-trained models in our review paper **"Neural Style Transfer: A Review" [[arXiv Version]](https://arxiv.org/abs/1705.04058) [[IEEE Version]](https://ieeexplore.ieee.org/document/8732370)** 3 | 4 | The corresponding OSF repository can be found at: https://osf.io/f8tu4/. 5 | 6 | *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!* 7 | 8 | 9 | ## Citation 10 | If you find this repository useful for your research, please consider citing 11 | 12 | ``` 13 | @article{jing2019neural, 14 | title={Neural Style Transfer: A Review}, 15 | author={Jing, Yongcheng and Yang, Yezhou and Feng, Zunlei and Ye, Jingwen and Yu, Yizhou and Song, Mingli}, 16 | journal={IEEE Transactions on Visualization and Computer Graphics}, 17 | year={2019} 18 | } 19 | ``` 20 | Please also consider citing our ECCV paper and AAAI (Oral) paper: 21 | 22 | ``` 23 | @inproceedings{jing2018stroke, 24 | title={Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields}, 25 | author={Jing, Yongcheng and Liu, Yang and Yang, Yezhou and Feng, Zunlei and Yu, Yizhou and Tao, Dacheng and Song, Mingli}, 26 | booktitle={ECCV}, 27 | year={2018} 28 | } 29 | ``` 30 | ``` 31 | @inproceedings{jing2020dynamic, 32 | title={Dynamic Instance Normalization for Arbitrary Style Transfer}, 33 | author={Jing, Yongcheng and Liu, Xiao and Ding, Yukang and Wang, Xinchao and Ding, Errui and Song, Mingli and Wen, Shilei}, 34 | booktitle={AAAI}, 35 | year={2020} 36 | } 37 | ``` 38 | 39 | Thanks! 40 | 41 | ## Framework 42 | 43 | There is a recent nice NST framework called [pystiche](https://github.com/ycjing/Neural-Style-Transfer-Papers/issues/17#issue-725784148), developed by [Philip Meier](https://github.com/pmeier). If you are interested, please refer to https://github.com/pmeier/pystiche. A package that comprises reference implementations of NST papers with pystiche can be found at [pystiche_papers](https://github.com/pmeier/pystiche_papers) (work in progress). 44 | 45 | 46 | --- 47 | 48 | ## *News!* 49 | 50 | - [June, 2019] Update the *Images (TVCG)* (.png) and *Supplementary Material (TVCG)* in the *Materials*. **Warmly welcome to use *Images (TVCG)* for comparison results in your paper!** 51 | 52 | - [May, 2019] Our paper *Neural Style Transfer: A Review* has been accepted by TVCG as a regular paper. This repository will be updated soon. 53 | 54 | - [July, 2018] Our paper *Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields* has been accepted by ECCV 2018. Our review will be updated correspondingly. 55 | 56 | - [June, 2018] Upload a new version of our paper on arXiv which adds several missing papers (e.g., the work of Wang et al. *ZM-Net: Real-time Zero-shot Image Manipulation Network*). 57 | 58 | - [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! 59 | 60 | - [Feb, 2018] Update the *Images* *(Images_neuralStyleTransferReview_v2)* in the *Materials*. Add the results of Li et al.'s NIPS 2017 paper. 61 | 62 | - [Jan, 2018] *Pre-trained models* and all the *content images*, the *style images*, and the *stylized results* in the paper have been released. 63 | 64 | 65 |

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69 | 70 | 71 | ## Materials corresponding to Our Paper 72 | 73 | :white_check_mark: [**Supplementary Material (TVCG)**](https://drive.google.com/file/d/1_VTS4rUSl488wgSrz2K5BfKra33gvaHH/view?usp=sharing) 74 | 75 | :white_check_mark: [**Pre-trained Models**](https://www.dropbox.com/s/37lje23pb75ecob/Models_neuralStyleTransferReview.zip?dl=0) 76 | 77 | :white_check_mark: [**Images (TVCG)(.png)**](https://drive.google.com/file/d/14RN0GN09-rordzRqp4o8oU30BB7uiNcj/view?usp=sharing) 78 | 79 | ## A Taxonomy of Current Methods 80 | 81 | ### 1. Image-Optimisation-Based Online Neural Methods 82 | 83 | ### 1.1. Parametric Neural Methods with Summary Statistics 84 | 85 | :white_check_mark: [**A Neural Algorithm of Artistic Style**] [[Paper]](https://arxiv.org/pdf/1508.06576.pdf) *(First Neural Style Transfer Paper)* 86 | 87 | :sparkle: **Code:** 88 | 89 | * [Torch-based](https://github.com/jcjohnson/neural-style) 90 | * [TensorFlow-based](https://github.com/anishathalye/neural-style) 91 | * [TensorFlow-based with L-BFGS optimizer support](https://github.com/cysmith/neural-style-tf) 92 | * [Caffe-based](https://github.com/fzliu/style-transfer) 93 | * [Keras-based](https://github.com/titu1994/Neural-Style-Transfer) 94 | * [MXNet-based](https://github.com/pavelgonchar/neural-art-mini) 95 | * [MatConvNet-based](https://github.com/aravindhm/neural-style-matconvnet) 96 | 97 | :white_check_mark: [**Image Style Transfer Using Convolutional Neural Networks**] [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Gatys_Image_Style_Transfer_CVPR_2016_paper.pdf) *(CVPR 2016)* 98 | 99 | :white_check_mark: [**Incorporating Long-range Consistency in CNN-based Texture Generation**] [[Paper]](https://arxiv.org/pdf/1606.01286.pdf) *(ICLR 2017)* 100 | 101 | :sparkle: **Code:** 102 | 103 | *   [Theano-based](https://github.com/guillaumebrg/texture_generation) 104 | 105 | :white_check_mark: [**Laplacian-Steered Neural Style Transfer**] [[Paper]](https://arxiv.org/pdf/1707.01253.pdf) *(ACM MM 2017)* 106 | 107 | :sparkle: **Code:** 108 | 109 | * [Torch-based & TensorFlow-based](https://github.com/askerlee/lapstyle) 110 | 111 | :white_check_mark: [**Demystifying Neural Style Transfer**] [[Paper]](https://arxiv.org/pdf/1701.01036.pdf) *(Theoretical Explanation)* *(IJCAI 2017)* 112 | 113 | :sparkle: **Code:** 114 | 115 | *   [MXNet-based](https://github.com/lyttonhao/Neural-Style-MMD) 116 | 117 | :white_check_mark: [**Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses**] [[Paper]](https://arxiv.org/pdf/1701.08893.pdf) 118 | 119 | 120 | ### 1.2. Non-parametric Neural Methods with MRFs 121 | 122 | :white_check_mark: [**Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis**] [[Paper]](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Li_Combining_Markov_Random_CVPR_2016_paper.pdf) *(CVPR 2016)* 123 | 124 | :sparkle: **Code:** 125 | 126 | * [Torch-based](https://github.com/chuanli11/CNNMRF) 127 | 128 | 129 | :white_check_mark: [**Arbitrary Style Transfer with Deep Feature Reshuffle**] [[Paper]](https://arxiv.org/pdf/1805.04103.pdf) *(CVPR 2018)* 130 | 131 | ### 2. Model-Optimisation-Based Offline Neural Methods 132 | 133 | ### 2.1. Per-Style-Per-Model Neural Methods 134 | 135 | 136 | :white_check_mark: [**Perceptual Losses for Real-Time Style Transfer and Super-Resolution**] [[Paper]](https://arxiv.org/pdf/1603.08155.pdf) *(ECCV 2016)* 137 | 138 | :sparkle: **Code:** 139 | 140 | * [Torch-based](https://github.com/jcjohnson/fast-neural-style) 141 | * [TensorFlow-based](https://github.com/lengstrom/fast-style-transfer) 142 | * [Chainer-based](https://github.com/yusuketomoto/chainer-fast-neuralstyle) 143 | 144 | :sparkle: **Pre-trained Models:** 145 | 146 | * [Torch-models](https://github.com/ProGamerGov/Torch-Models) 147 | * [Chainer-models](https://github.com/gafr/chainer-fast-neuralstyle-models) 148 | 149 | 150 | :white_check_mark: [**Texture Networks: Feed-forward Synthesis of Textures and Stylized Images**] [[Paper]](http://www.jmlr.org/proceedings/papers/v48/ulyanov16.pdf) *(ICML 2016)* 151 | 152 | :sparkle: **Code:** 153 | 154 | * [Torch-based](https://github.com/DmitryUlyanov/texture_nets) 155 | * [TensorFlow-based](https://github.com/tgyg-jegli/tf_texture_net) 156 | 157 | 158 | :white_check_mark: [**Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks**] [[Paper]](https://arxiv.org/pdf/1604.04382.pdf) *(ECCV 2016)* 159 | 160 | :sparkle: **Code:** 161 | 162 | * [Torch-based](https://github.com/chuanli11/MGANs) 163 | 164 | 165 | 166 | 167 | ### 2.2. Multiple-Style-Per-Model Neural Methods 168 | 169 | :white_check_mark: [**A Learned Representation for Artistic Style**] [[Paper]](https://arxiv.org/pdf/1610.07629.pdf) *(ICLR 2017)* 170 | 171 | :sparkle: **Code:** 172 | 173 | * [TensorFlow-based](https://github.com/tensorflow/magenta/tree/master/magenta/models/image_stylization) 174 | 175 | :white_check_mark: [**Multi-style Generative Network for Real-time Transfer**] [[Paper]](https://arxiv.org/pdf/1703.06953.pdf)  ***（arXiv, 03/2017）*** 176 | 177 | :sparkle: **Code:** 178 | 179 | * [PyTorch-based](https://github.com/zhanghang1989/PyTorch-Style-Transfer) 180 | * [Torch-based](https://github.com/zhanghang1989/MSG-Net) 181 | 182 | :white_check_mark: [**Diversified Texture Synthesis With Feed-Forward Networks**] [[Paper]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Li_Diversified_Texture_Synthesis_CVPR_2017_paper.pdf) *(CVPR 2017)* 183 | 184 | :sparkle: **Code:** 185 | 186 | *   [Torch-based](https://github.com/Yijunmaverick/MultiTextureSynthesis) 187 | 188 | :white_check_mark: [**StyleBank: An Explicit Representation for Neural Image Style Transfer**] [[Paper]](https://arxiv.org/pdf/1703.09210.pdf) *(CVPR 2017)* 189 | 190 | 191 | 192 | ### 2.3. Arbitrary-Style-Per-Model Neural Methods 193 | 194 | :white_check_mark: [**Fast Patch-based Style Transfer of Arbitrary Style**] [[Paper]](https://arxiv.org/pdf/1612.04337.pdf) 195 | 196 | :sparkle: **Code:** 197 | 198 | * [Torch-based](https://github.com/rtqichen/style-swap) 199 | 200 | :white_check_mark: [**Exploring the Structure of a Real-time, Arbitrary Neural Artistic Stylization Network**] [[Paper]](https://arxiv.org/pdf/1705.06830.pdf) *(BMVC 2017)* 201 | 202 | :sparkle: **Code:** 203 | 204 | * [TensorFlow-based](https://github.com/tensorflow/magenta/tree/master/magenta/models/arbitrary_image_stylization) 205 | 206 | 207 | :white_check_mark: [**Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization**] [[Paper]](https://arxiv.org/pdf/1703.06868.pdf) *(ICCV 2017)* 208 | 209 | :sparkle: **Code:** 210 | 211 | * [Torch-based](https://github.com/xunhuang1995/AdaIN-style) 212 | * [TensorFlow-based with Keras](https://github.com/eridgd/AdaIN-TF) 213 | * [TensorFlow-based without Keras](https://github.com/elleryqueenhomels/arbitrary_style_transfer) 214 | 215 | :white_check_mark: [**Dynamic Instance Normalization for Arbitrary Style Transfer**] [[Paper]](https://arxiv.org/pdf/1911.06953.pdf) *(AAAI 2020)* 216 | 217 | :white_check_mark: [**Universal Style Transfer via Feature Transforms**] [[Paper]](https://arxiv.org/pdf/1705.08086.pdf) *(NIPS 2017)* 218 | 219 | :sparkle: **Code:** 220 | 221 | * [Torch-based](https://github.com/Yijunmaverick/UniversalStyleTransfer) 222 | * [TensorFlow-based](https://github.com/eridgd/WCT-TF) 223 | * [PyTorch-based #1](https://github.com/sunshineatnoon/PytorchWCT) 224 | * [PyTorch-based #2](https://github.com/pietrocarbo/deep-transfer) 225 | 226 | :white_check_mark: [**Meta Networks for Neural Style Transfer**] [[Paper]](https://arxiv.org/pdf/1709.04111.pdf) *(CVPR 2018)* 227 | 228 | :sparkle: **Code:** 229 | 230 | * [Caffe-based](https://github.com/FalongShen/styletransfer) 231 | 232 | :white_check_mark: [**ZM-Net: Real-time Zero-shot Image Manipulation Network**] [[Paper]](https://arxiv.org/pdf/1703.07255.pdf) 233 | 234 | :white_check_mark: [**Avatar-Net: Multi-Scale Zero-Shot Style Transfer by Feature Decoration**] [[Paper]](http://openaccess.thecvf.com/content_cvpr_2018/CameraReady/0137.pdf) *(CVPR 2018)* 235 | 236 | :sparkle: **Code:** 237 | 238 | * [TensorFlow-based](https://github.com/LucasSheng/avatar-net) 239 | 240 | :white_check_mark: [**Learning Linear Transformations for Fast Arbitrary Style Transfer**] [[Paper]](https://arxiv.org/pdf/1808.04537.pdf) 241 | 242 | :sparkle: **Code:** 243 | 244 | * [PyTorch-based](https://github.com/sunshineatnoon/LinearStyleTransfer) 245 | 246 | ## Improvements and Extensions 247 | 248 | :white_check_mark: [**Preserving Color in Neural Artistic Style Transfer**] [[Paper]](https://arxiv.org/pdf/1606.05897.pdf) 249 | 250 | :white_check_mark: [**Controlling Perceptual Factors in Neural Style Transfer**] [[Paper]](https://arxiv.org/pdf/1611.07865.pdf) *(CVPR 2017)* 251 | 252 | :sparkle: **Code:** 253 | 254 | * [Torch-based](https://github.com/leongatys/NeuralImageSynthesis) 255 | 256 | :white_check_mark: [**Content-Aware Neural Style Transfer**] [[Paper]](https://arxiv.org/pdf/1601.04568.pdf) 257 | 258 | :white_check_mark: [**Towards Deep Style Transfer: A Content-Aware Perspective**] [[Paper]](http://www.bmva.org/bmvc/2016/papers/paper008/paper008.pdf) *(BMVC 2016)* 259 | 260 | :white_check_mark: [**Neural Doodle_Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artwork**] [[Paper]](https://arxiv.org/pdf/1603.01768.pdf) 261 | 262 | :white_check_mark: [**Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artwork**] [[Paper]](https://arxiv.org/pdf/1603.01768.pdf) 263 | 264 | :sparkle: **Code:** 265 | 266 | * [Torch-based](https://github.com/alexjc/neural-doodle) 267 | 268 | :white_check_mark: [**The Contextual Loss for Image Transformation with Non-Aligned Data**] [[Paper]](https://arxiv.org/pdf/1803.02077) *(ECCV 2018)* 269 | 270 | :sparkle: **Code:** 271 | 272 | * [TensorFlow-based](https://github.com/roimehrez/contextualLoss) 273 | 274 | :white_check_mark: [**Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis**] [[Paper]](https://arxiv.org/pdf/1701.02096.pdf) *(CVPR 2017)* 275 | 276 | :sparkle: **Code:** 277 | 278 | * [Torch-based](https://github.com/DmitryUlyanov/texture_nets) 279 | 280 | :white_check_mark: [**Instance Normalization：The Missing Ingredient for Fast Stylization**] [[Paper]](https://arxiv.org/pdf/1607.08022.pdf) 281 | 282 | :sparkle: **Code:** 283 | 284 | * [Torch-based](https://github.com/DmitryUlyanov/texture_nets) 285 | 286 | :white_check_mark: [**A Style-Aware Content Loss for Real-time HD Style Transfer**] [[Paper]](https://arxiv.org/pdf/1807.10201) *(ECCV 2018)* 287 | 288 | :white_check_mark: [**Multimodal Transfer: A Hierarchical Deep Convolutional Neural Network for Fast Artistic Style Transfer**] [[Paper]](https://arxiv.org/pdf/1612.01895.pdf) *(CVPR 2017)* 289 | 290 | :sparkle: **Code:** 291 | 292 | * [TensorFlow-based](https://github.com/fullfanta/multimodal_transfer) 293 | 294 | :white_check_mark: [**Stroke Controllable Fast Style Transfer with Adaptive Receptive Fields**] [[Paper]](https://arxiv.org/pdf/1802.07101.pdf) *(ECCV 2018)* 295 | 296 | :sparkle: **Code:** 297 | 298 | * [TensorFlow-based](https://github.com/LouieYang/stroke-controllable-fast-style-transfer) 299 | 300 | 301 | :white_check_mark: [**Depth-Preserving Style Transfer**] [[Paper]](https://github.com/xiumingzhang/depth-preserving-neural-style-transfer/blob/master/report/egpaper_final.pdf) 302 | 303 | :sparkle: **Code:** 304 | 305 | * [Torch-based](https://github.com/xiumingzhang/depth-preserving-neural-style-transfer) 306 | 307 | :white_check_mark: [**Depth-Aware Neural Style Transfer**] [[Paper]](https://dl.acm.org/citation.cfm?id=3092924) *(NPAR 2017)* 308 | 309 | :white_check_mark: [**Neural Style Transfer: A Paradigm Shift for Image-based Artistic Rendering?**] [[Paper]](https://tobias.isenberg.cc/personal/papers/Semmo_2017_NST.pdf) *(NPAR 2017)* 310 | 311 | :white_check_mark: [**Pictory: Combining Neural Style Transfer and Image Filtering**] [[Paper]](https://www.researchgate.net/publication/320035123_Demo_Pictory_-_Neural_Style_Transfer_and_Editing_with_CoreML) *(ACM SIGGRAPH 2017 Appy Hour)* 312 | 313 | :white_check_mark: [**Painting Style Transfer for Head Portraits Using Convolutional Neural Networks**] [[Paper]](http://dl.acm.org/citation.cfm?id=2925968) *(SIGGRAPH 2016)* 314 | 315 | :white_check_mark: [**Son of Zorn's Lemma Targeted Style Transfer Using Instance-aware Semantic Segmentation**] [[Paper]](https://arxiv.org/pdf/1701.02357.pdf) *(ICASSP 2017)* 316 | 317 | :white_check_mark: [**Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN**] [[Paper]](https://arxiv.org/pdf/1706.03319.pdf) *(ACPR 2017)* 318 | 319 | :white_check_mark: [**Artistic Style Transfer for Videos**] [[Paper]](https://arxiv.org/pdf/1604.08610.pdf) *(GCPR 2016)* 320 | 321 | :sparkle: **Code:** 322 | 323 | * [Torch-based](https://github.com/manuelruder/artistic-videos) 324 | 325 | :white_check_mark: [**DeepMovie: Using Optical Flow and Deep Neural Networks to Stylize Movies**] [[Paper]](https://arxiv.org/pdf/1605.08153.pdf) 326 | 327 | :white_check_mark: [**Characterizing and Improving Stability in Neural Style Transfer**] [[Paper]](https://arxiv.org/pdf/1705.02092.pdf)) *(ICCV 2017)* 328 | 329 | :white_check_mark: [**Coherent Online Video Style Transfer**] [[Paper]](https://arxiv.org/pdf/1703.09211.pdf) *(ICCV 2017)* 330 | 331 | :white_check_mark: [**Real-Time Neural Style Transfer for Videos**] [[Paper]](http://openaccess.thecvf.com/content_cvpr_2017/papers/Huang_Real-Time_Neural_Style_CVPR_2017_paper.pdf) *(CVPR 2017)* 332 | 333 | :white_check_mark: [**A Common Framework for Interactive Texture Transfer**] [[Paper]](http://www.icst.pku.edu.cn/F/zLian/papers/CVPR18-Men.pdf) *(CVPR 2018)* 334 | 335 | :white_check_mark: [**Deep Photo Style Transfer**] [[Paper]](https://arxiv.org/pdf/1703.07511.pdf) *(CVPR 2017)* 336 | 337 | 338 | :sparkle: **Code:** 339 | 340 | * [Torch-based](https://github.com/luanfujun/deep-photo-styletransfer) 341 | * [TensorFlow-based](https://github.com/LouieYang/deep-photo-styletransfer-tf) 342 | 343 | :white_check_mark: [**A Closed-form Solution to Photorealistic Image Stylization**] [[Paper]](https://arxiv.org/pdf/1802.06474.pdf) *(ECCV 2018)* 344 | 345 | :sparkle: **Code:** 346 | 347 | * [PyTorch-based](https://github.com/NVIDIA/FastPhotoStyle) 348 | 349 | :white_check_mark: [**Photorealistic Style Transfer via Wavelet Transforms**] [[Paper]](https://arxiv.org/pdf/1903.09760.pdf) 350 | 351 | :sparkle: **Code:** 352 | 353 | * [PyTorch-based](https://github.com/clovaai/WCT2) 354 | 355 | :white_check_mark: [**Decoder Network Over Lightweight Reconstructed Feature for Fast Semantic Style Transfer**] [[Paper]](http://feng-xu.com/papers/iccv2017_style.pdf) *(ICCV 2017)* 356 | 357 | :white_check_mark: [**Stereoscopic Neural Style Transfer**] [[Paper]](https://arxiv.org/pdf/1802.10591.pdf) *(CVPR 2018)* 358 | 359 | 360 | 361 | 362 | 363 | :white_check_mark: [**Awesome Typography: Statistics-based Text Effects Transfer**] [[Paper]](https://arxiv.org/abs/1611.09026) *(CVPR 2017)* 364 | 365 | :sparkle: **Code:** 366 | 367 | * [Matlab-based](https://github.com/williamyang1991/Text-Effects-Transfer) 368 | 369 | :white_check_mark: [**Neural Font Style Transfer**] [[Paper]](http://ieeexplore.ieee.org/document/8270274/) *(ICDAR 2017)* 370 | 371 | :white_check_mark: [**Rewrite: Neural Style Transfer For Chinese Fonts**] [[Project]](https://github.com/kaonashi-tyc/Rewrite) 372 | 373 | :white_check_mark: [**Separating Style and Content for Generalized Style Transfer**] [[Paper]](https://arxiv.org/pdf/1711.06454.pdf) *(CVPR 2018)* 374 | 375 | :white_check_mark: [**Visual Attribute Transfer through Deep Image Analogy**] [[Paper]](https://arxiv.org/pdf/1705.01088.pdf) *(SIGGRAPH 2017)* 376 | 377 | :sparkle: **Code:** 378 | 379 | * [Caffe-based](https://github.com/msracver/Deep-Image-Analogy) 380 | 381 | :white_check_mark: [**Fashion Style Generator**] [[Paper]](https://www.ijcai.org/proceedings/2017/0520.pdf) *(IJCAI 2017)* 382 | 383 | :white_check_mark: [**Deep Painterly Harmonization**] [[Paper]](https://arxiv.org/abs/1804.03189) 384 | 385 | :sparkle: **Code:** 386 | 387 | * [Torch-based](https://github.com/luanfujun/deep-painterly-harmonization) 388 | 389 | :white_check_mark: [**Fast Face-Swap Using Convolutional Neural Networks**] [[Paper]](http://openaccess.thecvf.com/content_ICCV_2017/papers/Korshunova_Fast_Face-Swap_Using_ICCV_2017_paper.pdf) *(ICCV 2017)* 390 | 391 | :white_check_mark: [**Learning Selfie-Friendly Abstraction from Artistic Style Images**] [[Paper]]() *(ACML 2018)* 392 | 393 | :white_check_mark: [**Style Transfer with Adaptation to the Central Objects of the Scene**] [[Paper]](https://link.springer.com/chapter/10.1007/978-3-030-30425-6_40) *(NEUROINFORMATICS 2019)* 394 | 395 | 396 | ## Application 397 | 398 | :white_check_mark: [**Prisma**](https://prisma-ai.com/) 399 | 400 | :white_check_mark: [**Ostagram**](https://ostagram.ru/) 401 | 402 | :white_check_mark: [**AlterDraw**](https://alterdraw.com/en/) 403 | 404 | :white_check_mark: [**Vinci**](https://play.google.com/store/apps/details?id=io.vinci.android&hl=en_US&gl=US) 405 | 406 | :white_check_mark: [**Artisto**](https://en.wikipedia.org/wiki/Artisto) 407 | 408 | :sparkle: **Code:** 409 | 410 | * [Website code](https://github.com/SergeyMorugin/ostagram) 411 | 412 | :white_check_mark: [**Deep Forger**](https://deepforger.com/) 413 | 414 | :white_check_mark: [**NeuralStyler**](http://neuralstyler.com/) 415 | 416 | :white_check_mark: [**Style2Paints**](http://paintstransfer.com/) 417 | 418 | :sparkle: **Code:** 419 | 420 | * [Website code](https://github.com/lllyasviel/style2paints) 421 | 422 | ## Application Papers 423 | 424 | :white_check_mark: [**Bringing Impressionism to Life with Neural Style Transfer in Come Swim**] [[Paper]](https://arxiv.org/pdf/1701.04928.pdf) 425 | 426 | :white_check_mark: [**Imaging Novecento. A Mobile App for Automatic Recognition of Artworks and Transfer of Artistic Styles**] [[Paper]](https://www.micc.unifi.it/wp-content/uploads/2017/01/imaging900.pdf) 427 | 428 | :white_check_mark: [**ProsumerFX: Mobile Design of Image Stylization Components**] [[Paper]](https://www.researchgate.net/publication/319631844_ProsumerFX_Mobile_Design_of_Image_Stylization_Components) 429 | 430 | :white_check_mark: [**Pictory - Neural Style Transfer and Editing with coreML**] [[Paper]](https://www.researchgate.net/publication/320035123_Demo_Pictory_-_Neural_Style_Transfer_and_Editing_with_CoreML) 431 | 432 | :white_check_mark: [**Tiny Transform Net for Mobile Image Stylization**] [[Paper]](https://dl.acm.org/citation.cfm?id=3079034) *(ICMR 2017)* 433 | 434 | ## Blogs 435 | 436 | :white_check_mark: [**Caffe2Go**][https://code.facebook.com/posts/196146247499076/delivering-real-time-ai-in-the-palm-of-your-hand/] 437 | 438 | :white_check_mark: [**Supercharging Style Transfer**][https://research.googleblog.com/2016/10/supercharging-style-transfer.html] 439 | 440 | :white_check_mark: [**Issue of Layer Chosen Strategy**][http://yongchengjing.com/pdf/Issue_layerChosenStrategy_neuralStyleTransfer.pdf] 441 | 442 | :white_check_mark: [**Picking an optimizer for Style Transfer**][https://blog.slavv.com/picking-an-optimizer-for-style-transfer-86e7b8cba84b] 443 | 444 | :white_check_mark: [**Enhanced Color Style Transfer (Photo-surrealism Style Transfer)**] [[Project]](https://github.com/TJCoding/Enhanced-Image-Colour-Transfer) 445 | 446 | 447 | ## Others 448 | 449 | :white_check_mark: [**Conditional Fast Style Transfer Network**] [[Paper]](http://img.cs.uec.ac.jp/pub/conf17/170612yanai_0.pdf) 450 | 451 | :white_check_mark: [**Unseen Style Transfer Based on a Conditional Fast Style Transfer Network**] [[Paper]](https://openreview.net/forum?id=H1Y7-1HYg¬eId=H1Y7-1HYg) 452 | 453 | :white_check_mark: [**DeepStyleCam: A Real-time Style Transfer App on iOS**] [[Paper]](http://img.cs.uec.ac.jp/pub/conf16/170103tanno_0.pdf) 454 | 455 | :white_check_mark: [**Deep Feature Rotation for Multimodal Image Style Transfer**] [[Paper]](https://arxiv.org/pdf/2202.04426.pdf)  *(NICS 2021)* 456 | 457 | :sparkle: **Code:** 458 | 459 | * [TensorFlow-based](https://github.com/sonnguyen129/deep-feature-rotation) 460 | 461 | -------------------------------------------------------------------------------- /framework_n4.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ycjing/Neural-Style-Transfer-Papers/dca522b0a2da37bf4e189830fe6204330f38980e/framework_n4.png -------------------------------------------------------------------------------- /framework_n5.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ycjing/Neural-Style-Transfer-Papers/dca522b0a2da37bf4e189830fe6204330f38980e/framework_n5.png --------------------------------------------------------------------------------