The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks
(CNNs) in creating artistic imagery by separating and recombining image content and style …

The standard approach to tackling computer vision problems is to train deep convolutional
neural network (CNN) models using large-scale image datasets that are representative of …

The diffusion-based generative models have achieved remarkable success in text-based
image generation. However, since it contains enormous randomness in generation …

Neural implicit fields are powerful for representing 3D scenes and generating high-quality
novel views, but it remains challenging to use such implicit representations for creating a 3D …

In this work, we tackle the challenging problem of arbitrary image style transfer using a novel
style feature representation learning method. A suitable style representation, as a key …

This paper presents a stylized novel view synthesis method. Applying state-of-the-art
stylization methods to novel views frame by frame often causes jittering artifacts due to the …

Abstract Neural Style Transfer has shown very exciting results enabling new forms of image
manipulation. Here we extend the existing method to introduce control over spatial location …

This essay discusses whether computers, using Artificial Intelligence (AI), could create art.
First, the history of technologies that automated aspects of art is surveyed, including …

Recently, methods have been proposed that perform texture synthesis and style transfer by
using convolutional neural networks (eg Gatys et al.[2015, 2016]). These methods are …

Significant progress has been made with image stylization using deep learning, especially
with generative adversarial networks (GANs). However, existing methods fail to produce …