Fine-Tuning StyleGAN2 For Cartoon Face Generation

TL;DR

This paper introduces a method for fine-tuning StyleGAN2 to generate cartoon faces, effectively preserving source image structure and producing realistic images with limited data, addressing challenges in data imbalance and resource requirements.

Contribution

Proposes a novel fine-tuning approach for StyleGAN2 tailored for cartoon face generation, improving structure preservation and realism on unbalanced datasets.

Findings

Effective structure preservation in generated images

High-quality cartoon face generation from limited data

Stable training with simple fine-tuning techniques

Abstract

Recent studies have shown remarkable success in the unsupervised image to image (I2I) translation. However, due to the imbalance in the data, learning joint distribution for various domains is still very challenging. Although existing models can generate realistic target images, it's difficult to maintain the structure of the source image. In addition, training a generative model on large data in multiple domains requires a lot of time and computer resources. To address these limitations, we propose a novel image-to-image translation method that generates images of the target domain by finetuning a stylegan2 pretrained model. The stylegan2 model is suitable for unsupervised I2I translation on unbalanced datasets; it is highly stable, produces realistic images, and even learns properly from limited data when applied with simple fine-tuning techniques. Thus, in this paper, we propose new methods to preserve the structure of the source images and generate realistic images in the target domain. The code and results are available at https://github.com/happy-jihye/Cartoon-StyleGan2