  A virtual try-on method takes a product image and an image of a model and
produces an image of the model wearing the product. Most methods essentially
compute warps from the product image to the model image and combine using image
generation methods. However, obtaining a realistic image is challenging because
the kinematics of garments is complex and because outline, texture, and shading
cues in the image reveal errors to human viewers. The garment must have
appropriate drapes; texture must be warped to be consistent with the shape of a
draped garment; small details (buttons, collars, lapels, pockets, etc.) must be
placed appropriately on the garment, and so on. Evaluation is particularly
difficult and is usually qualitative.
  This paper uses quantitative evaluation on a challenging, novel dataset to
demonstrate that (a) for any warping method, one can choose target models
automatically to improve results, and (b) learning multiple coordinated
specialized warpers offers further improvements on results. Target models are
chosen by a learned embedding procedure that predicts a representation of the
products the model is wearing. This prediction is used to match products to
models. Specialized warpers are trained by a method that encourages a second
warper to perform well in locations where the first works poorly. The warps are
then combined using a U-Net. Qualitative evaluation confirms that these
improvements are wholesale over outline, texture shading, and garment details.
