WAIT: Feature Warping for Animation to Illustration video Translation using GANs
Samet Hicsonmez, Nermin Samet, Fidan Samet, Oguz Bakir, Emre Akbas,, Pinar Duygulu

TL;DR
This paper introduces WAIT, a novel GAN-based method for stylizing videos from unordered sets of images, ensuring temporal consistency without relying on traditional optical flow or temporal predictors.
Contribution
We propose a new generator with feature warping layers that maintains temporal coherence in video stylization from unordered image sets, simplifying and speeding up the process.
Findings
Effective in stylizing animation videos with illustrations
Outperforms existing methods in qualitative and quantitative evaluations
Works well across multiple datasets
Abstract
In this paper, we explore a new domain for video-to-video translation. Motivated by the availability of animation movies that are adopted from illustrated books for children, we aim to stylize these videos with the style of the original illustrations. Current state-of-the-art video-to-video translation models rely on having a video sequence or a single style image to stylize an input video. We introduce a new problem for video stylizing where an unordered set of images are used. This is a challenging task for two reasons: i) we do not have the advantage of temporal consistency as in video sequences; ii) it is more difficult to obtain consistent styles for video frames from a set of unordered images compared to using a single image. Most of the video-to-video translation methods are built on an image-to-image translation model, and integrate additional networks such as optical flow, or…
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Taxonomy
TopicsGenerative Adversarial Networks and Image Synthesis · Cancer-related molecular mechanisms research · Multimodal Machine Learning Applications
