Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SLGAN: Style- and Latent-guided Generative Adversarial Network for
  Desirable Makeup Transfer and Removal   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.

10
The categories of this paper are: q-bio
29
The categories of this paper are: funct-an
15
The categories of this paper are: nucl-ex
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph