Image-based virtual try-on strives to transfer the appearance of a clothing
item onto the image of a target person. Prior work focuses mainly on upper-body
clothes (e.g. t-shirts, shirts, and tops) and neglects full-body or lower-body
items. This shortcoming arises from a main factor: current publicly available
datasets for image-based virtual try-on do not account for this variety, thus
limiting progress in the field. To address this deficiency, we introduce Dress
Code, which contains images of multi-category clothes. Dress Code is more than
3x larger than publicly available datasets for image-based virtual try-on and
features high-resolution paired images (1024x768) with front-view, full-body
reference models. To generate HD try-on images with high visual quality and
rich in details, we propose to learn fine-grained discriminating features.
Specifically, we leverage a semantic-aware discriminator that makes predictions
at pixel-level instead of image- or patch-level. Extensive experimental
evaluation demonstrates that the proposed approach surpasses the baselines and
state-of-the-art competitors in terms of visual quality and quantitative
results. The Dress Code dataset is publicly available at
this https URL.

本文旨在解决针对全身或下半身衣物的图像虚拟试穿的局限性，介绍了 Dress Code 数据集，提出了一种基于语义感知判别器的像素级预测方法，为生成高细节和视觉质量的高清试穿图像提供了新的思路，并在视觉质量和定量评估方面超过了基线和最先进的竞争对手。