论文标题
基于gan的面部吸引力增强
GAN-Based Facial Attractiveness Enhancement
论文作者
论文摘要
我们提出了一个基于生成对抗网络（GAN）的生成框架，以增强面部吸引力，同时保持面部身份和高保真性。给定肖像图像作为输入，应用了梯度下降以恢复该生成框架可以使用的潜在向量，以合成与输入图像相似的图像，基于相应恢复的潜在矢量基于Interfacegan的相应恢复的潜在矢量的操作使该框架启用此框架以实现面部图像美化。本文将我们的系统与旁观者gan和我们提出的结果增强版本进行了比较。事实证明，我们的框架获得了最先进的吸引力增强结果。该代码可从https://github.com/zoezhou1999/beautifybasedangan获得。
We propose a generative framework based on generative adversarial network (GAN) to enhance facial attractiveness while preserving facial identity and high-fidelity. Given a portrait image as input, having applied gradient descent to recover a latent vector that this generative framework can use to synthesize an image resemble to the input image, beauty semantic editing manipulation on the corresponding recovered latent vector based on InterFaceGAN enables this framework to achieve facial image beautification. This paper compared our system with Beholder-GAN and our proposed result-enhanced version of Beholder-GAN. It turns out that our framework obtained state-of-art attractiveness enhancement results. The code is available at https://github.com/zoezhou1999/BeautifyBasedOnGAN.
