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 …

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

In this paper, we aim to devise a universally versatile style transfer method capable of
performing artistic, photo-realistic, and video style transfer jointly, without seeing videos …

Neural Style Transfer (NST) is a class of software algorithms that allows us to transform
scenes, change/edit the environment of a media with the help of a Neural Network. NST …

We study the problem of video-to-video synthesis, whose goal is to learn a mapping function
from an input source video (eg, a sequence of semantic segmentation masks) to an output …

Generating high-quality artistic portrait videos is an important and desirable task in computer
graphics and vision. Although a series of successful portrait image toonification models built …

Recent advances in video super-resolution have shown that convolutional neural networks
combined with motion compensation are able to merge information from multiple low …

Applying image processing algorithms independently to each frame of a video often leads to
undesired inconsistent results over time. Developing temporally consistent video-based …

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 …

Given a random pair of images, a universal style transfer method extracts the feel from a
reference image to synthesize an output based on the look of a content image. Recent …