18 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.
These leaderboards are used to track progress in Image Stylization
Most implemented papers
Instance Normalization: The Missing Ingredient for Fast Stylization
It this paper we revisit the fast stylization method introduced in Ulyanov et.
A Closed-form Solution to Photorealistic Image Stylization
Photorealistic image stylization concerns transferring style of a reference photo to a content photo with the constraint that the stylized photo should remain photorealistic.
A Style-Aware Content Loss for Real-time HD Style Transfer
These and our qualitative results ranging from small image patches to megapixel stylistic images and videos show that our approach better captures the subtle nature in which a style affects content.
Copy the Old or Paint Anew? An Adversarial Framework for (non-) Parametric Image Stylization
Parametric generative deep models are state-of-the-art for photo and non-photo realistic image stylization.
APDrawingGAN: Generating Artistic Portrait Drawings From Face Photos With Hierarchical GANs
Moreover, artists tend to use different strategies to draw different facial features and the lines drawn are only loosely related to obvious image features.
Image Super-Resolution by Neural Texture Transfer
Reference-based super-resolution (RefSR), on the other hand, has proven to be promising in recovering high-resolution (HR) details when a reference (Ref) image with similar content as that of the LR input is given.
Frustratingly Simple Domain Generalization via Image Stylization
Convolutional Neural Networks (CNNs) show impressive performance in the standard classification setting where training and testing data are drawn i. i. d.
WISE: Whitebox Image Stylization by Example-based Learning
Image-based artistic rendering can synthesize a variety of expressive styles using algorithmic image filtering.
EDICT: Exact Diffusion Inversion via Coupled Transformations
EDICT enables mathematically exact inversion of real and model-generated images by maintaining two coupled noise vectors which are used to invert each other in an alternating fashion.
Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesis
The recent work of Gatys et al., who characterized the style of an image by the statistics of convolutional neural network filters, ignited a renewed interest in the texture generation and image stylization problems.