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. Our project webpage is …

黄: 背景

赤: 課題

緑: 提案

Specifically, … : 提案手法の具体的な説明．Self-supervisedであること，generic artistic datasetを構築したことの２点が簡潔に述べられている

In addition, … : WBMという提案モジュールの説明．その役割3点を簡潔に説明

By doing so, … : 提案手法の効能が述べられている

To this end … : datasetを新たに構築したことが補足的に述べられている（dataset構築は本論文のメインの貢献ではないため補足的に）

Extensive experiments : 実験と結果について述べられている

総評: 課題⇒提案⇒結果のフレームワークに沿った文章のため分かりやすい．また，提案の説明の流れ（緑以降の文の流れ）もある種のフレームワークに沿っていると思われ，1文1文に存在意義がある無駄の無い文章に感じた．

https://arxiv.org/pdf/2110.11728.pdf