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 \(3\times \) larger than publicly available datasets for image-based virtual try-on and features high-resolution paired images (\(1024 \times 768\)) 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.
F. Landi—Now at Huawei Technologies, Amsterdam Research Center, the Netherlands.
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Acknowledgments
We thank CINECA, the Italian Supercomputing Center, for providing computational resources. This work has been supported by the PRIN project “CREATIVE: CRoss-modal understanding and gEnerATIon of Visual and tExtual content” (CUP B87G22000460001), co-funded by the Italian Ministry of University and Research.
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Morelli, D., Fincato, M., Cornia, M., Landi, F., Cesari, F., Cucchiara, R. (2022). Dress Code: High-Resolution Multi-category Virtual Try-On. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13668. Springer, Cham. https://doi.org/10.1007/978-3-031-20074-8_20
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