SLGAN: Style- and Latent-guided Generative Adversarial Network for Desirable Makeup Transfer and Removal
arXiv:2009.07557
Abstract
There are five features to consider when using generative adversarial networks to apply makeup to photos of the human face. These features include (1) facial components, (2) interactive color adjustments, (3) makeup variations, (4) robustness to poses and expressions, and the (5) use of multiple reference images. Several related works have been proposed, mainly using generative adversarial networks (GAN). Unfortunately, none of them have addressed all five features simultaneously. This paper closes the gap with an innovative style- and latent-guided GAN (SLGAN). We provide a novel, perceptual makeup loss and a style-invariant decoder that can transfer makeup styles based on histogram matching to avoid the identity-shift problem. In our experiments, we show that our SLGAN is better than or comparable to state-of-the-art methods. Furthermore, we show that our proposal can interpolate facial makeup images to determine the unique features, compare existing methods, and help users find desirable makeup configurations.
9 pages, 9 figures
References in corpus (5)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- PyTorch: An Imperative Style, High-Performance Deep Learning Library
- A Learned Representation For Artistic Style
- Diversity-Sensitive Conditional Generative Adversarial Networks
- Style Transfer for Anime Sketches with Enhanced Residual U-net and Auxiliary Classifier GAN