  The task of image-based virtual try-on aims to transfer a target clothing
item onto the corresponding region of a person, which is commonly tackled by
fitting the item to the desired body part and fusing the warped item with the
person. While an increasing number of studies have been conducted, the
resolution of synthesized images is still limited to low (e.g., 256x192), which
acts as the critical limitation against satisfying online consumers. We argue
that the limitation stems from several challenges: as the resolution increases,
the artifacts in the misaligned areas between the warped clothes and the
desired clothing regions become noticeable in the final results; the
architectures used in existing methods have low performance in generating
high-quality body parts and maintaining the texture sharpness of the clothes.
To address the challenges, we propose a novel virtual try-on method called
VITON-HD that successfully synthesizes 1024x768 virtual try-on images.
Specifically, we first prepare the segmentation map to guide our virtual try-on
synthesis, and then roughly fit the target clothing item to a given person's
body. Next, we propose ALIgnment-Aware Segment (ALIAS) normalization and ALIAS
generator to handle the misaligned areas and preserve the details of 1024x768
inputs. Through rigorous comparison with existing methods, we demonstrate that
VITON-HD highly sur-passes the baselines in terms of synthesized image quality
both qualitatively and quantitatively.
