Arbitrary style transfer (AST) and domain generalization (DG) are important yet challenging
visual learning tasks, which can be cast as a feature distribution matching problem. With the …

The goal of image style transfer is to render an image with artistic features guided by a style
reference while maintaining the original content. Owing to the locality in convolutional neural …

Although existing artistic style transfer methods have achieved significant improvement with
deep neural networks, they still suffer from artifacts such as disharmonious colors and …

Universal style transfer retains styles from reference images in content images. While
existing methods have achieved state-of-the-art style transfer performance, they are not …

In this work, we propose a new feed-forward arbitrary style transfer method, referred to as
StyleFormer, which can simultaneously fulfill fine-grained style diversity and semantic …

We introduce a simple and versatile framework for image-to-image translation. We unearth
the importance of normalization layers, and provide a carefully designed two-stream …

Abstract Neural Radiance Fields (NeRF) is a revolutionary approach for rendering scenes
by sampling a single ray per pixel and it has demonstrated impressive capabilities in novel …

Diffusion models (DMs) have recently gained attention with state-of-the-art performance in
text-to-image synthesis. Abiding by the tradition in deep learning, DMs are trained and …

Artistic style transfer is an image editing task that aims at repainting everyday photographs
with learned artistic styles. Existing methods learn styles from either a single style example …

Recent studies have shown remarkable success in universal style transfer which transfers
arbitrary visual styles to content images. However, existing approaches suffer from the …