BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation

• URL: http://arxiv.org/abs/2110.11728v1
• Date: Fri, 22 Oct 2021 12:00:27 GMT
• Title: BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation
• Authors: Mingcong Liu, Qiang Li, Zekui Qin, Guoxin Zhang, Pengfei Wan, Wen Zheng
• Abstract summary: We propose BlendGAN for arbitrary stylized face generation. 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.
• Score: 9.370501805054344
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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