Style Transfer
651 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
Latest papers
IGUANe: a 3D generalizable CycleGAN for multicenter harmonization of brain MR images
rocavincent/iguane_harmonization
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In MRI studies, the aggregation of imaging data from multiple acquisition sites enhances sample size but may introduce site-related variabilities that hinder consistency in subsequent analyses.
ConRF: Zero-shot Stylization of 3D Scenes with Conditioned Radiation Fields
xingy038/conrf
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Most of the existing works on arbitrary 3D NeRF style transfer required retraining on each single style condition.
Procedural terrain generation with style transfer
fmerizzi/procedural-terrain-generation-with-style-transfer
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In this study we introduce a new technique for the generation of terrain maps, exploiting a combination of procedural generation and Neural Style Transfer.
CreativeSynth: Creative Blending and Synthesis of Visual Arts based on Multimodal Diffusion
Large-scale text-to-image generative models have made impressive strides, showcasing their ability to synthesize a vast array of high-quality images.
CAT-LLM: Prompting Large Language Models with Text Style Definition for Chinese Article-style Transfer
Text style transfer is increasingly prominent in online entertainment and social media.
Zero Shot Audio to Audio Emotion Transfer With Speaker Disentanglement
iiscleap/zest
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The problem of audio-to-audio (A2A) style transfer involves replacing the style features of the source audio with those from the target audio while preserving the content related attributes of the source audio.
Auffusion: Leveraging the Power of Diffusion and Large Language Models for Text-to-Audio Generation
happylittlecat2333/Auffusion
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Drawing inspiration from state-of-the-art Text-to-Image (T2I) diffusion models, we introduce Auffusion, a TTA system adapting T2I model frameworks to TTA task, by effectively leveraging their inherent generative strengths and precise cross-modal alignment.
Balancing the Style-Content Trade-Off in Sentiment Transfer Using Polarity-Aware Denoising
souro/polarity-denoising-sentiment-transfer
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Text sentiment transfer aims to flip the sentiment polarity of a sentence (positive to negative or vice versa) while preserving its sentiment-independent content.
HyperEditor: Achieving Both Authenticity and Cross-Domain Capability in Image Editing via Hypernetworks
Editing real images authentically while also achieving cross-domain editing remains a challenge.
FontDiffuser: One-Shot Font Generation via Denoising Diffusion with Multi-Scale Content Aggregation and Style Contrastive Learning
Automatic font generation is an imitation task, which aims to create a font library that mimics the style of reference images while preserving the content from source images.
