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
A Neural Algorithm of Artistic Style
In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image.
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
junyanz/pytorch-CycleGAN-and-pix2pix
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ICCV 2017
Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs.
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
We consider image transformation problems, where an input image is transformed into an output image.
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
xunhuang1995/AdaIN-style
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ICCV 2017
Gatys et al. recently introduced a neural algorithm that renders a content image in the style of another image, achieving so-called style transfer.
Instance Normalization: The Missing Ingredient for Fast Stylization
DmitryUlyanov/texture_nets
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It this paper we revisit the fast stylization method introduced in Ulyanov et.
Deep Photo Style Transfer
luanfujun/deep-photo-styletransfer
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CVPR 2017
This paper introduces a deep-learning approach to photographic style transfer that handles a large variety of image content while faithfully transferring the reference style.
Exploring the structure of a real-time, arbitrary neural artistic stylization network
magenta/magenta
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In this paper, we present a method which combines the flexibility of the neural algorithm of artistic style with the speed of fast style transfer networks to allow real-time stylization using any content/style image pair.
Universal Style Transfer via Feature Transforms
Yijunmaverick/UniversalStyleTransfer
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NeurIPS 2017
The whitening and coloring transforms reflect a direct matching of feature covariance of the content image to a given style image, which shares similar spirits with the optimization of Gram matrix based cost in neural style transfer.
Deep Feature Consistent Variational Autoencoder
We present a novel method for constructing Variational Autoencoder (VAE).
Style Transfer from Non-Parallel Text by Cross-Alignment
shentianxiao/language-style-transfer
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NeurIPS 2017
We demonstrate the effectiveness of this cross-alignment method on three tasks: sentiment modification, decipherment of word substitution ciphers, and recovery of word order.
