State-of-the-art parametric and non-parametric style transfer approaches are prone to either
distorted local style patterns due to global statistics alignment, or unpleasing artifacts …

Remarkable achievements have been attained with Generative Adversarial Networks
(GANs) in image-to-image translation. However, due to a tremendous amount of parameters …

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 …

Single domain generalization aims to train a generalizable model with only one source
domain to perform well on arbitrary unseen target domains. Image augmentation based on …

The field of neural style transfer has experienced a surge of research exploring different
avenues ranging from optimization-based approaches and feed-forward models to meta …

When stylizing 3D scenes, current methods need to render the full-resolution images from
different views and use the style loss, which is proposed for 2D style transfer and needs to …

Research has shown that convolutional neural networks for object recognition are
vulnerable to changes in depiction because learning is biased towards the low-level …

We propose a novel style transfer method to quickly create a new visual product with a nice
appearance for industrial designers' reference. Given a source product, a target product, and …

Completely automated public turing test to tell computers and humans apart (CAPTCHA) is
widely used to prevent malicious automated attacks on various online services. Text-and …

In this paper, we present the texture reformer, a fast and universal neural-based framework
for interactive texture transfer with user-specified guidance. The challenges lie in three …