GAN-Based Facial Attractiveness Enhancement
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.
Code (1)
Tasks
Generative Adversarial NetworkSimilar Papers 제목 키워드 기반
FA-GANs: Facial Attractiveness Enhancement with Generative Adversarial Networks on Frontal Faces
Facial attractiveness enhancement has been an interesting application in Computer Vision and Graphics over these years. It aims to generate a more attractive face via manipulations on image and geometry structure while p…
Diffusion-based Facial Aesthetics Enhancement with 3D Structure Guidance
Facial Aesthetics Enhancement (FAE) aims to improve facial attractiveness by adjusting the structure and appearance of a facial image while preserving its identity as much as possible. Most existing methods adopted deep …
Face ModelUnderstanding Beauty via Deep Facial Features
The concept of beauty has been debated by philosophers and psychologists for centuries, but most definitions are subjective and metaphysical, and deficit in accuracy, generality, and scalability. In this paper, we presen…
Generative Adversarial NetworkSCUT-FBP: A Benchmark Dataset for Facial Beauty Perception
In this paper, a novel face dataset with attractiveness ratings, namely, the SCUT-FBP dataset, is developed for automatic facial beauty perception. This dataset provides a benchmark to evaluate the performance of differe…
Deep LearningLightweight Facial Attractiveness Prediction Using Dual Label Distribution
Facial attractiveness prediction (FAP) aims to assess facial attractiveness automatically based on human aesthetic perception. Previous methods using deep convolutional neural networks have improved the performance, but …
Prediction