  Image-based virtual try-on aims to synthesize an image of a person wearing a
given clothing item. To solve the task, the existing methods warp the clothing
item to fit the person's body and generate the segmentation map of the person
wearing the item before fusing the item with the person. However, when the
warping and the segmentation generation stages operate individually without
information exchange, the misalignment between the warped clothes and the
segmentation map occurs, which leads to the artifacts in the final image. The
information disconnection also causes excessive warping near the clothing
regions occluded by the body parts, so-called pixel-squeezing artifacts. To
settle the issues, we propose a novel try-on condition generator as a unified
module of the two stages (i.e., warping and segmentation generation stages). A
newly proposed feature fusion block in the condition generator implements the
information exchange, and the condition generator does not create any
misalignment or pixel-squeezing artifacts. We also introduce discriminator
rejection that filters out the incorrect segmentation map predictions and
assures the performance of virtual try-on frameworks. Experiments on a
high-resolution dataset demonstrate that our model successfully handles the
misalignment and occlusion, and significantly outperforms the baselines. Code
is available at https://github.com/sangyun884/HR-VITON.
