Normalization techniques are essential for accelerating the training and improving the
generalization of deep neural networks (DNNs), and have successfully been used in various …

Creativity Support Tools (CSTs) have become an integral part of artistic creation. The range
of CST technologies is broad—from fabricators to generative algorithms to robots. The …

Can a generative model be trained to produce images from a specific domain, guided only
by a text prompt, without seeing any image? In other words: can an image generator be …

We present a method for transferring the artistic features of an arbitrary style image to a 3D
scene. Previous methods that perform 3D stylization on point clouds or meshes are sensitive …

Fast arbitrary neural style transfer has attracted widespread attention from academic,
industrial and art communities due to its flexibility in enabling various applications. Existing …

Abstract 3D scene stylization aims at generating stylized images of the scene from arbitrary
novel views following a given set of style examples, while ensuring consistency when …

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 …

Content and style (CS) disentanglement is a fundamental problem and critical challenge of
style transfer. Existing approaches based on explicit definitions (eg, Gram matrix) or implicit …

Abstract 3D style transfer aims to render stylized novel views of a 3D scene with multi-view
consistency. However, most existing work suffers from a three-way dilemma over accurate …