JoJoGAN: One Shot Face Stylization.

While there have been recent advances in few-shot image stylization, these methods fail to capture stylistic details that are obvious to humans. Details such as the shape of the eyes, the boldness of the lines, are especially difficult for a model to learn, especially so under a limited data setting. In this work, we aim to perform one-shot image stylization that gets the details right. Given a reference style image, we approximate paired real data using GAN inversion and finetune a pretrained StyleGAN using that approximate paired data. We then encourage the StyleGAN to generalize so that the learned style can be applied to all other images.