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 …

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 …

This paper focuses on domain generalization (DG), the task of learning from multiple source
domains a model that generalizes well to unseen domains. A main challenge for DG is that …

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

Despite that convolutional neural networks (CNN) have recently demonstrated high-quality
reconstruction for single-image super-resolution (SR), recovering natural and realistic …

Person re-identification (re-ID) models trained on one domain often fail to generalize well to
another. In our attempt, we present a``learning via translation''framework. In the baseline, we …

Gatys et al. recently introduced a neural algorithm that renders a content image in the style
of another image, achieving so-called style transfer. However, their framework requires a …

Universal style transfer aims to transfer arbitrary visual styles to content images. Existing
feed-forward based methods, while enjoying the inference efficiency, are mainly limited by …

Arbitrary style transfer aims to synthesize a content image with the style of an image to
create a third image that has never been seen before. Recent arbitrary style transfer …