Dress Code: High-Resolution Multi-Category Virtual Try-On
Abstract: 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 https://github.com/aimagelab/dress-code.
Citation:
Morelli, Davide; Fincato, Matteo; Cornia, Marcella; Landi, Federico; Cesari, Fabio; Cucchiara, Rita "Dress Code: High-Resolution Multi-Category Virtual Try-On" Proceeding of the European Conference on Computer Vision (Lecture Notes in Computer Science), vol. 13668, Tel Aviv, pp. 345 -362 , October 23-27, 2022, 2022 DOI: 10.1007/978-3-031-20074-8_20
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- DOI: 10.1007/978-3-031-20074-8_20