White-Box Cartoonization Using An Extended GAN Framework

TL;DR

This paper introduces a novel white-box GAN framework for controllable cartoonization of images and videos, decomposing images into surface, structure, and texture representations for high-quality, flexible cartoon image generation.

Contribution

It extends existing GAN models to enable controllable cartoonization by decomposing images into multiple representations, improving flexibility and image quality.

Findings

Achieves high-quality cartoon images with preserved clarity and colors.

Allows user control over cartoonization parameters.

Outperforms previous methods in maintaining image features.

Abstract

In the present study, we propose to implement a new framework for estimating generative models via an adversarial process to extend an existing GAN framework and develop a white-box controllable image cartoonization, which can generate high-quality cartooned images/videos from real-world photos and videos. The learning purposes of our system are based on three distinct representations: surface representation, structure representation, and texture representation. The surface representation refers to the smooth surface of the images. The structure representation relates to the sparse colour blocks and compresses generic content. The texture representation shows the texture, curves, and features in cartoon images. Generative Adversarial Network (GAN) framework decomposes the images into different representations and learns from them to generate cartoon images. This decomposition makes the framework more controllable and flexible which allows users to make changes based on the required output. This approach overcomes any previous system in terms of maintaining clarity, colours, textures, shapes of images yet showing the characteristics of cartoon images.