  Studies of virtual try-on (VITON) have been shown their effectiveness in
utilizing the generative neural network for virtually exploring fashion
products, and some of recent researches of VITON attempted to synthesize human
image wearing given multiple types of garments (e.g., top and bottom clothes).
However, when replacing the top and bottom clothes of the target human,
numerous wearing styles are possible with a certain combination of the clothes.
In this paper, we address the problem of variation in wearing style when
simultaneously replacing the top and bottom clothes of the model. We introduce
Wearing-Guide VITON (i.e., WG-VITON) which utilizes an additional input binary
mask to control the wearing styles of the generated image. Our experiments show
that WG-VITON effectively generates an image of the model wearing given top and
bottom clothes, and create complicated wearing styles such as partly tucking in
the top to the bottom
