DeepAI
Log In Sign Up

SLGAN: Style- and Latent-guided Generative Adversarial Network for Desirable Makeup Transfer and Removal

09/16/2020
by   Daichi Horita, et al.
0

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.

READ FULL TEXT

page 1

page 3

page 6

page 7

page 8

05/15/2020

Generative Adversarial Networks for photo to Hayao Miyazaki style cartoons

This paper takes on the problem of transferring the style of cartoon ima...
03/27/2020

Local Facial Makeup Transfer via Disentangled Representation

Facial makeup transfer aims to render a non-makeup face image in an arbi...
05/18/2022

3D Segmentation Guided Style-based Generative Adversarial Networks for PET Synthesis

Potential radioactive hazards in full-dose positron emission tomography ...
05/17/2021

Style-Restricted GAN: Multi-Modal Translation with Style Restriction Using Generative Adversarial Networks

Unpaired image-to-image translation using Generative Adversarial Network...
10/22/2021

BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation

Generative Adversarial Networks (GANs) have made a dramatic leap in high...
07/16/2021

Painting Style-Aware Manga Colorization Based on Generative Adversarial Networks

Japanese comics (called manga) are traditionally created in monochrome f...
04/20/2019

Everyone is a Cartoonist: Selfie Cartoonization with Attentive Adversarial Networks

Selfie and cartoon are two popular artistic forms that are widely presen...