GAN-Based Facial Attractiveness Enhancement
This is an incremental improvement for facial beautification applications in computer vision.
The authors tackled facial attractiveness enhancement using a GAN-based framework that preserves identity and fidelity, achieving state-of-the-art results compared to Beholder-GAN and its enhanced version.
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.