Style Transfer
650 papers with code • 2 benchmarks • 17 datasets
Style Transfer is a technique in computer vision and graphics that involves generating a new image by combining the content of one image with the style of another image. The goal of style transfer is to create an image that preserves the content of the original image while applying the visual style of another image.
( Image credit: A Neural Algorithm of Artistic Style )
Benchmarks
These leaderboards are used to track progress in Style Transfer
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Libraries
Use these libraries to find Style Transfer models and implementationsDatasets
DukeMTMC-reID
MPI Sintel
GYAFC
WikiArt
MPIIGaze
POP909
iKala
Touchdown Dataset
TextSeg
Subtasks
Most implemented papers
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
PaddlePaddle/PaddleSpeech
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ICML 2018
In this work, we propose "global style tokens" (GSTs), a bank of embeddings that are jointly trained within Tacotron, a state-of-the-art end-to-end speech synthesis system.
AUTOVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss
liusongxiang/StarGAN-Voice-Conversion
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On the other hand, CVAE training is simple but does not come with the distribution-matching property of a GAN.
Texture Networks: Feed-forward Synthesis of Textures and Stylized Images
DmitryUlyanov/texture_nets
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Gatys et al. recently demonstrated that deep networks can generate beautiful textures and stylized images from a single texture example.
A Style-Aware Content Loss for Real-time HD Style Transfer
CompVis/adaptive-style-transfer
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ECCV 2018
These and our qualitative results ranging from small image patches to megapixel stylistic images and videos show that our approach better captures the subtle nature in which a style affects content.
Neural Style Transfer: A Review
ycjing/Neural-Style-Transfer-Papers
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We first propose a taxonomy of current algorithms in the field of NST.
ReCoNet: Real-time Coherent Video Style Transfer Network
Image style transfer models based on convolutional neural networks usually suffer from high temporal inconsistency when applied to videos.
Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks
Convolutional neural networks (CNNs) have proven highly effective at image synthesis and style transfer.
Preserving Color in Neural Artistic Style Transfer
This note presents an extension to the neural artistic style transfer algorithm (Gatys et al.).
Controlling Perceptual Factors in Neural Style Transfer
leongatys/NeuralImageSynthesis
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CVPR 2017
Neural Style Transfer has shown very exciting results enabling new forms of image manipulation.
Fast Patch-based Style Transfer of Arbitrary Style
This results in a procedure for artistic style transfer that is efficient but also allows arbitrary content and style images.
