  Given an image of a target person and an image of another person wearing a
garment, we automatically generate the target person in the given garment. At
the core of our method is a pose-conditioned StyleGAN2 latent space
interpolation, which seamlessly combines the areas of interest from each image,
i.e., body shape, hair, and skin color are derived from the target person,
while the garment with its folds, material properties, and shape comes from the
garment image. By automatically optimizing for interpolation coefficients per
layer in the latent space, we can perform a seamless, yet true to source,
merging of the garment and target person. Our algorithm allows for garments to
deform according to the given body shape, while preserving pattern and material
details. Experiments demonstrate state-of-the-art photo-realistic results at
high resolution ($512\times 512$).
