  Most existing virtual try-on applications require clean clothes images.
Instead, we present a novel virtual Try-On network, M2E-Try On Net, which
transfers the clothes from a model image to a person image without the need of
any clean product images. To obtain a realistic image of person wearing the
desired model clothes, we aim to solve the following challenges: 1) non-rigid
nature of clothes - we need to align poses between the model and the user; 2)
richness in textures of fashion items - preserving the fine details and
characteristics of the clothes is critical for photo-realistic transfer; 3)
variation of identity appearances - it is required to fit the desired model
clothes to the person identity seamlessly. To tackle these challenges, we
introduce three key components, including the pose alignment network (PAN), the
texture refinement network (TRN) and the fitting network (FTN). Since it is
unlikely to gather image pairs of input person image and desired output image
(i.e. person wearing the desired clothes), our framework is trained in a
self-supervised manner to gradually transfer the poses and textures of the
model's clothes to the desired appearance. In the experiments, we verify on the
Deep Fashion dataset and MVC dataset that our method can generate
photo-realistic images for the person to try-on the model clothes. Furthermore,
we explore the model capability for different fashion items, including both
upper and lower garments.
