Generative Adversarial Networks (GANs) have made a dramatic leap in
high-fidelity image synthesis and stylized face generation. Recently, a
layer-swapping mechanism has been developed to improve the stylization
performance. However, this method is incapable of fitting arbitrary styles in a
single model and requires hundreds of style-consistent training images for each
style. To address the above issues, we propose BlendGAN for arbitrary stylized
face generation by leveraging a flexible blending strategy and a generic
artistic dataset. Specifically, we first train a self-supervised style encoder
on the generic artistic dataset to extract the representations of arbitrary
styles. In addition, a weighted blending module (WBM) is proposed to blend face
and style representations implicitly and control the arbitrary stylization
effect. By doing so, BlendGAN can gracefully fit arbitrary styles in a unified
model while avoiding case-by-case preparation of style-consistent training
images. To this end, we also present a novel large-scale artistic face dataset
AAHQ. Extensive experiments demonstrate that BlendGAN outperforms
state-of-the-art methods in terms of visual quality and style diversity for
both latent-guided and reference-guided stylized face synthesis.

提出了一种称为 BlendGAN 的方法，使用灵活的混合策略和通用的艺术数据集进行任意样式的面部生成，使用自监督样式编码器从艺术数据集中提取表示，并提出了一种加权混合模块 (WBM) 来隐式地混合面部和样式表示。通过这样做，BlendGAN 可以优雅地适合统一模型中的任意样式，并避免对样式一致性训练图像逐案例准备。