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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 1–50 of 64 papers

TitleStatusHype
OmniConsistency: Learning Style-Agnostic Consistency from Paired Stylization DataCode3
Implicit Style-Content Separation using B-LoRACode3
Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized StylizationCode3
DiffArtist: Towards Structure and Appearance Controllable Image StylizationCode2
EDICT: Exact Diffusion Inversion via Coupled TransformationsCode2
JoJoGAN: One Shot Face StylizationCode2
Frustratingly Simple Domain Generalization via Image StylizationCode1
ReDi: Efficient Learning-Free Diffusion Inference via Trajectory RetrievalCode1
WISE: Whitebox Image Stylization by Example-based LearningCode1
Controlling Geometric Abstraction and Texture for Artistic ImagesCode1
Learning A Low-Level Vision Generalist via Visual Task PromptCode1
Interactive Control over Temporal Consistency while Stylizing Video StreamsCode1
A Large-scale Film Style Dataset for Learning Multi-frequency Driven Film EnhancementCode1
DiffStyler: Controllable Dual Diffusion for Text-Driven Image StylizationCode1
StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual LearningCode1
Domain Enhanced Arbitrary Image Style Transfer via Contrastive LearningCode1
One-Shot Structure-Aware Stylized Image SynthesisCode1
Texture Underfitting for Domain Adaptation—0
Ada-adapter:Fast Few-shot Style Personlization of Diffusion Model with Pre-trained Image Encoder—0
Aesthetic Matters in Music Perception for Image Stylization: A Emotion-driven Music-to-Visual Manipulation—0
Aesthetics of Neural Network Art—0
AI-Driven Stylization of 3D Environments—0
Automatic Image Stylization Using Deep Fully Convolutional Networks—0
Balanced Image Stylization with Style Matching Score—0
CartoonRenderer: An Instance-based Multi-Style Cartoon Image Translator—0
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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