  With the increasing development of garment manufacturing industry, the method
of combining neural network with industry to reduce product redundancy has been
paid more and more attention.In order to reduce garment redundancy and achieve
personalized customization, more researchers have appeared in the field of
virtual trying on.They try to transfer the target clothing to the reference
figure, and then stylize the clothes to meet user's requirements for
fashion.But the biggest problem of virtual try on is that the shape and motion
blocking distort the clothes, causing the patterns and texture on the clothes
to be impossible to restore. This paper proposed a new stylized virtual try on
network, which can not only retain the authenticity of clothing texture and
pattern, but also obtain the undifferentiated stylized try on. The network is
divided into three sub-networks, the first is the user image, the front of the
target clothing image, the semantic segmentation image and the posture heat map
to generate a more detailed human parsing map. Second, UV position map and
dense correspondence are used to map patterns and textures to the deformed
silhouettes in real time, so that they can be retained in real time, and the
rationality of spatial structure can be guaranteed on the basis of improving
the authenticity of images. Third,Stylize and adjust the generated virtual try
on image. Through the most subtle changes, users can choose the texture, color
and style of clothing to improve the user's experience.
