Image Stylization
23 papers with code • 0 benchmarks • 0 datasets
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.
Benchmarks
These leaderboards are used to track progress in Image Stylization
Latest papers with no code
Implicit Style-Content Separation using B-LoRA
In this paper, we introduce B-LoRA, a method that leverages LoRA (Low-Rank Adaptation) to implicitly separate the style and content components of a single image, facilitating various image stylization tasks.
Realization RGBD Image Stylization
This research paper explores the application of style transfer in computer vision using RGB images and their corresponding depth maps.
StyleTRF: Stylizing Tensorial Radiance Fields
In this work, we present StyleTRF, a compact, quick-to-optimize strategy for stylized view generation using TensoRF.
Touch and Go: Learning from Human-Collected Vision and Touch
The ability to associate touch with sight is essential for tasks that require physically interacting with objects in the world.
LISA: Localized Image Stylization with Audio via Implicit Neural Representation
We present a novel framework, Localized Image Stylization with Audio (LISA) which performs audio-driven localized image stylization.
MultiStyleGAN: Multiple One-shot Image Stylizations using a Single GAN
Recent approaches for one-shot stylization such as JoJoGAN fine-tune a pre-trained StyleGAN2 generator on a single style reference image.
PTGCF: Printing Texture Guided Color Fusion for Impressionism Oil Painting Style Rendering
As a major branch of Non-Photorealistic Rendering (NPR), image stylization mainly uses the computer algorithms to render a photo into an artistic painting.
Learning Graph Neural Networks for Image Style Transfer
To this end, we develop an elaborated GNN model with content and style local patches as the graph vertices.
StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D Mutual Learning
We first pre-train a standard NeRF of the 3D scene to be stylized and replace its color prediction module with a style network to obtain a stylized NeRF.
Learning Visual Styles from Audio-Visual Associations
Our model learns to manipulate the texture of a scene to match a sound, a problem we term audio-driven image stylization.
