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Image Stylization

Image stylization is a task that involves transforming an input image into a new image that has a different style, while preserving the content of the original image. The goal of image stylization is to create visually appealing images with a specific style or aesthetic, such as impressionism, cubism, or surrealism. It can also be used to make images more visually appealing for specific applications, such as social media or advertising.

Papers

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Showing 26–50 of 64 papers

TitleStatusHype
Coherent Online Video Style Transfer—0
Content-style disentangled representation for controllable artistic image stylization and generation—0
Context-Aware Text-Based Binary Image Stylization and Synthesis—0
Co-Attention for Conditioned Image Matching—0
Digging Into Self-Supervised Learning of Feature Descriptors—0
Distribution Aligned Multimodal and Multi-Domain Image Stylization—0
Image Steganography based on Style Transfer—0
Image Stylization for Robust Features—0
Image Stylization: From Predefined to Personalized—0
Multi-Attribute Guided Painting Generation—0
MultiStyleGAN: Multiple One-shot Image Stylizations using a Single GAN—0
Realization RGBD Image Stylization—0
Reference-Conditioned Super-Resolution by Neural Texture Transfer—0
SRAGAN: Saliency Regularized and Attended Generative Adversarial Network for Chinese Ink-wash Painting Generation—0
StyleTRF: Stylizing Tensorial Radiance Fields—0
Achieving Domain Generalization in Underwater Object Detection by Domain Mixup and Contrastive Learning—0
PTGCF: Printing Texture Guided Color Fusion for Impressionism Oil Painting Style Rendering—0
The Role of Text-to-Image Models in Advanced Style Transfer Applications: A Case Study with DALL-E 3—0
ToonifyGB: StyleGAN-based Gaussian Blendshapes for 3D Stylized Head Avatars—0
Touch and Go: Learning from Human-Collected Vision and Touch—0
UMFA: A photorealistic style transfer method based on U-Net and multi-layer feature aggregation—0
WaSt-3D: Wasserstein-2 Distance for Scene-to-Scene Stylization on 3D Gaussians—0
Learning Graph Neural Networks for Image Style Transfer—0
Learning Visual Styles from Audio-Visual Associations—0
LISA: Localized Image Stylization with Audio via Implicit Neural Representation—0
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