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Image Stylization
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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.
논문
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ToonifyGB: StyleGAN-based Gaussian Blendshapes for 3D Stylized Head Avatars
Balanced Image Stylization with Style Matching Score
Multiscale style transfer based on a Laplacian pyramid for traditional Chinese painting
Aesthetic Matters in Music Perception for Image Stylization: A Emotion-driven Music-to-Visual Manipulation
Content-style disentangled representation for controllable artistic image stylization and generation
Structure-Aware Stylized Image Synthesis for Robust Medical Image Segmentation
The Role of Text-to-Image Models in Advanced Style Transfer Applications: A Case Study with DALL-E 3
AI-Driven Stylization of 3D Environments
WaSt-3D: Wasserstein-2 Distance for Scene-to-Scene Stylization on 3D Gaussians
MagicStyle: Portrait Stylization Based on Reference Image
Learning A Low-Level Vision Generalist via Visual Task Prompt
DiffArtist: Towards Structure and Appearance Controllable Image Stylization
Ada-adapter:Fast Few-shot Style Personlization of Diffusion Model with Pre-trained Image Encoder
SRAGAN: Saliency Regularized and Attended Generative Adversarial Network for Chinese Ink-wash Painting Generation
LeGO: Leveraging a Surface Deformation Network for Animatable Stylized Face Generation with One Example
Implicit Style-Content Separation using B-LoRA
Deformable One-shot Face Stylization via DINO Semantic Guidance
One-Shot Structure-Aware Stylized Image Synthesis
Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized Stylization
Controlling Geometric Abstraction and Texture for Artistic Images
Realization RGBD Image Stylization
Instant Photorealistic Neural Radiance Fields Stylization
StyO: Stylize Your Face in Only One-shot
ReDi: Efficient Learning-Free Diffusion Inference via Trajectory Retrieval
A Large-scale Film Style Dataset for Learning Multi-frequency Driven Film Enhancement
Interactive Control over Temporal Consistency while Stylizing Video Streams
StyleTRF: Stylizing Tensorial Radiance Fields
EDICT: Exact Diffusion Inversion via Coupled Transformations
Touch and Go: Learning from Human-Collected Vision and Touch
LISA: Localized Image Stylization with Audio via Implicit Neural Representation
DiffStyler: Controllable Dual Diffusion for Text-Driven Image Stylization
MultiStyleGAN: Multiple One-shot Image Stylizations using a Single GAN
WISE: Whitebox Image Stylization by Example-based Learning
PTGCF: Printing Texture Guided Color Fusion for Impressionism Oil Painting Style Rendering
Learning Graph Neural Networks for Image Style Transfer
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