Everyone is a Cartoonist: Selfie Cartoonization with Attentive Adversarial Networks
This work addresses the specific challenge of cartoonizing selfies, which is important for users interested in artistic image transformation, but it is incremental as it builds on existing image translation techniques.
The paper tackles the problem of selfie cartoonization by proposing a Generative Adversarial Network (scGAN) with an attentive adversarial network to emphasize facial regions and ignore low-level details, achieving results that outperform state-of-the-art methods in generating different cartoon styles.
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.