# Everyone is a Cartoonist: Selfie Cartoonization with Attentive Adversarial Networks

## TL;DR

This paper introduces scGAN, a novel self-supervised attentive adversarial network for selfie cartoonization that emphasizes facial features and produces diverse, high-quality cartoon styles, outperforming existing methods.

## Contribution

The paper proposes a new attentive adversarial network with a cycle-like architecture and multiple loss functions specifically designed for selfie cartoonization.

## Key findings

- Outperforms state-of-the-art cartoonization methods.
- Capable of generating diverse cartoon styles.
- Effectively emphasizes facial features and reduces artifacts.

## Abstract

Selfie and cartoon are two popular artistic forms that are widely presented in our daily life. Despite the great progress in image translation/stylization, few techniques focus specifically on selfie cartoonization, since cartoon images usually contain artistic abstraction (e.g., large smoothing areas) and exaggeration (e.g., large/delicate eyebrows). In this paper, we address this problem by proposing a selfie cartoonization Generative Adversarial Network (scGAN), which mainly uses an attentive adversarial network (AAN) to emphasize specific facial regions and ignore low-level details. More specifically, we first design a cycle-like architecture to enable training with unpaired data. Then we design three losses from different aspects. A total variation loss is used to highlight important edges and contents in cartoon portraits. An attentive cycle loss is added to lay more emphasis on delicate facial areas such as eyes. In addition, a perceptual loss is included to eliminate artifacts and improve robustness of our method. Experimental results show that our method is capable of generating different cartoon styles and outperforms a number of state-of-the-art methods.