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 …

Radiomics converts medical images into mineable data via a high-throughput extraction of
quantitative features used for clinical decision support. However, these radiomic features are …

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 …

Rendering the semantic content of an image in different styles is a difficult image processing
task. Arguably, a major limiting factor for previous approaches has been the lack of image …

We study the problem of transferring a sample in one domain to an analog sample in
another domain. Given two related domains, S and T, we would like to learn a generative …

There has recently been great interest in neural rendering methods. Some approaches use
3D geometry reconstructed with Multi‐View Stereo (MVS) but cannot recover from the errors …

Zero-shot artistic style transfer is an important image synthesis problem aiming at
transferring arbitrary style into content images. However, the trade-off between the …

Training a feed-forward network for the fast neural style transfer of images has proven
successful, but the naive extension of processing videos frame by frame is prone to …

Thoroughly revised, this third edition focuses on modern techniques used to generate
synthetic three-dimensional images in a fraction of a second. With the advent of …

We propose a method to create plausible geometric and texture style variations of 3D
objects in the quest to democratize 3D content creation. Given a pair of textured source and …