Abstract
Virtual try-on has recently emerged in computer vision and multimedia communities with the development of architectures that can generate realistic images of a target person wearing a custom garment. This research interest is motivated by the large role played by e-commerce and online shopping in our society. Indeed, the virtual try-on task can offer many opportunities to improve the efficiency of preparing fashion catalogs and to enhance the online user experience. The problem is far to be solved: current architectures do not reach sufficient accuracy with respect to manually generated images and can only be trained on image pairs with a limited variety. Existing virtual try-on datasets have two main limits: they contain only female models, and all the images are available only in low resolution. This not only affects the generalization capabilities of the trained architectures but makes the deployment to real applications impractical. To overcome these issues, we present Dress Code, a new dataset for virtual try-on that contains high-resolution images of a large variety of upper-body clothes and both male and female models. Leveraging this enriched dataset, we propose a new model for virtual try-on capable of generating high-quality and photo-realistic images using a three-stage pipeline. The first two stages perform two different geometric transformations to warp the desired garment and make it fit into the target person’s body pose and shape. Then, we generate the new image of that same person wearing the try-on garment using a generative network. We test the proposed solution on the most widely used dataset for this task as well as on our newly collected dataset and demonstrate its effectiveness when compared to current state-of-the-art methods. Through extensive analyses on our Dress Code dataset, we show the adaptability of our model, which can generate try-on images even with a higher resolution.
- [1] . 2019. Robust cloth warping via multi-scale patch adversarial loss for virtual try-on framework. In Proceedings of the ICCV Workshops.Google Scholar
Cross Ref
- [2] . 2018. A note on the inception score. In Proceedings of the ICML Workshops.Google Scholar
- [3] . 2020. CLOTH3D: Clothed 3D humans. In Proceedings of the ECCV.Google Scholar
Digital Library
- [4] . 2018. Demystifying MMD GANs. In Proceedings of the ICLR.Google Scholar
- [5] . 1989. Principal warps: Thin-plate splines and the decomposition of deformations. IEEE Trans. PAMI 11, 6 (1989), 567–585. Google Scholar
Digital Library
- [6] . 2017. Realtime multi-person 2D pose estimation using part affinity fields. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [7] . 2019. Context-aware visual compatibility prediction. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [8] . 2009. ImageNet: A large-scale hierarchical image database. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [9] . 2018. Soft-gated warping-GAN for pose-guided person image synthesis. In Proceedings of the NeurIPS. Google Scholar
Digital Library
- [10] . 2019. Towards multi-pose guided virtual try-on network. In Proceedings of the ICCV.Google Scholar
Cross Ref
- [11] . 2019. FW-GAN: Flow-navigated warping GAN for video virtual try-on. In Proceedings of the ICCV.Google Scholar
Cross Ref
- [12] . 2020. Fashion compatibility modeling through a multi-modal try-on-guided scheme. In Proceedings of the ACM SIGIR. Google Scholar
Digital Library
- [13] . 2020. VITON-GT: An image-based virtual try-on model with geometric transformations. In Proceedings of the ICPR.Google Scholar
- [14] . 2019. DeepFashion2: A versatile benchmark for detection, pose estimation, segmentation and re-identification of clothing images. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [15] . 2014. Generative adversarial nets. In Proceedings of the NeurIPS. Google Scholar
Digital Library
- [16] . 2012. Drape: Dressing any person. ACM Trans. Graph. 31, 4 (2012), 1–10. Google Scholar
Digital Library
- [17] . 2018. DensePose: Dense human pose estimation in the wild. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [18] . 2015. Where to buy it: Matching street clothing photos in online shops. In Proceedings of the ICCV. Google Scholar
Digital Library
- [19] . 2014. Subspace clothing simulation using adaptive bases. ACM Trans. Graph. 33, 4 (2014), 1–9. Google Scholar
Digital Library
- [20] . 2019. ClothFlow: A flow-based model for clothed person generation. In Proceedings of the ICCV.Google Scholar
Cross Ref
- [21] . 2018. VITON: An image-based virtual try-on network. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [22] . 2017. GANs trained by a two time-scale update rule converge to a nash equilibrium. In Proceedings of the NeurIPS. Google Scholar
Digital Library
- [23] . 2018. Creating capsule wardrobes from fashion images. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [24] . 2019. Fit-me: Image-based virtual try-on with arbitrary poses. In Proceedings of the ICIP.Google Scholar
Cross Ref
- [25] . 2019. FashionOn: Semantic-guided image-based virtual try-on with detailed human and clothing information. In Proceedings of the ACM Multimedia. Google Scholar
Digital Library
- [26] . 2017. Image-to-image translation with conditional adversarial networks. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [27] . 2020. Do not mask what you do not need to mask: A parser-free virtual try-on. In Proceedings of the ECCV.Google Scholar
Digital Library
- [28] . 2019. LA-VITON: A network for looking-attractive virtual try-on. In Proceedings of the ICCV Workshops.Google Scholar
Cross Ref
- [29] . 2020. SieveNet: A unified framework for robust image-based virtual try-on. In Proceedings of the WACV.Google Scholar
Cross Ref
- [30] . 2017. The conditional analogy GAN: Swapping fashion articles on people images. In Proceedings of the ICCV Workshops.Google Scholar
Cross Ref
- [31] . 2016. Perceptual losses for real-time style transfer and super-resolution. In Proceedings of the ECCV.Google Scholar
Cross Ref
- [32] . 2019. A style-based generator architecture for generative adversarial networks. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [33] . 2020. Analyzing and improving the image quality of StyleGAN. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [34] . 2015. Adam: A method for stochastic optimization. In Proceedings of the ICLR.Google Scholar
- [35] . 2019. Fashion retrieval via graph reasoning networks on a similarity pyramid. In Proceedings of the ICCV.Google Scholar
Cross Ref
- [36] . 2021. ShineOn: Illuminating design choices for practical video-based virtual clothing try-on. In Proceedings of the WACV Workshops.Google Scholar
Cross Ref
- [37] . 2021. VOGUE: Try-on by stylegan interpolation optimization. Retrieved from https://arXiv:2101.02285.Google Scholar
- [38] . 2019. Self-correction for human parsing. Retrieved from https://arXiv:1910.09777.Google Scholar
- [39] . 2015. Deep human parsing with active template regression. IEEE Trans. PAMI 37, 12 (2015), 2402–2414. Google Scholar
Digital Library
- [40] . 2016. DeepFashion: Powering robust clothes recognition and retrieval with rich annotations. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [41] . 2019. Unsupervised part-based disentangling of object shape and appearance. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [42] . 2018. Disentangled person image generation. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [43] . 2020. Learning to dress 3D people in generative clothing. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [44] . 2014. A complete system for garment segmentation and color classification. Mach. Vision Appl. 25, 4 (2014), 955–969. Google Scholar
Digital Library
- [45] . 2020. CloTH-VTON: Clothing three-dimensional reconstruction for hybrid image-based virtual try-ON. In Proceedings of the ACCV.Google Scholar
- [46] . 2020. CP-VTON+: Clothing shape and texture preserving image-based virtual try-on. In Proceedings of the CVPR Workshops.Google Scholar
- [47] . 2020. Learning to transfer texture from clothing images to 3d humans. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [48] . 2021. FashionSearch++: Improving consumer-to-shop clothes retrieval with hard negatives. In Proceedings of the Italian Information Retrieval Workshop.Google Scholar
- [49] . 2020. Image based virtual try-on network from unpaired data. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [50] . 2007. Object retrieval with large vocabularies and fast spatial matching. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [51] . 2017. ClothCap: Seamless 4D clothing capture and retargeting. ACM Trans. Graph. 36, 4 (2017), 1–15. Google Scholar
Digital Library
- [52] . 2020. GarmentGAN: Photo-realistic adversarial fashion transfer. Retrieved from https://arXiv:2003.01894.Google Scholar
- [53] . 2018. SwapNet: Image based garment transfer. In Proceedings of the ECCV.Google Scholar
Cross Ref
- [54] . 2017. Convolutional neural network architecture for geometric matching. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [55] . 2015. U-Net: Convolutional networks for biomedical image segmentation. In Proceedings of the MICCAI.Google Scholar
Cross Ref
- [56] . 2016. Improved techniques for training GANs. In Proceedings of the NeurIPS. Google Scholar
Digital Library
- [57] . 2015. Very deep convolutional networks for large-scale image recognition. In Proceedings of the ICLR.Google Scholar
- [58] . 2019. Compatibility modeling: Data and knowledge applications for clothing matching. Synth. Lect. Info. Conc. Retriev. Serv. 11, 3 (2019), 1–138.Google Scholar
Cross Ref
- [59] . 2016. Rethinking the inception architecture for computer vision. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [60] . 2020. SIZER: A dataset and model for parsing 3D clothing and learning size sensitive 3D clothing. In Proceedings of the ECCV.Google Scholar
Digital Library
- [61] . 2018. Learning type-aware embeddings for fashion compatibility. In Proceedings of the ECCV.Google Scholar
Cross Ref
- [62] . 2018. Toward characteristic-preserving image-based virtual try-on network. In Proceedings of the ECCV.Google Scholar
Cross Ref
- [63] . 2018. High-resolution image synthesis and semantic manipulation with conditional GANs. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [64] . 2018. Attentive fashion grammar network for fashion landmark detection and clothing category classification. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [65] . 2019. M2E-try on net: Fashion from model to everyone. In Proceedings of the ACM Multimedia. Google Scholar
Digital Library
- [66] . 2017. Fashion-MNIST: A novel image dataset for benchmarking machine learning algorithms. Retrieved from https://arXiv:1708.07747.Google Scholar
- [67] . 2013. Paper doll parsing: Retrieving similar styles to parse clothing items. In Proceedings of the ICCV. Google Scholar
Digital Library
- [68] . 2020. Towards photo-realistic virtual try-on by adaptively generating-preserving image content. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [69] . 2019. Generating high-resolution fashion model images wearing custom outfits. In Proceedings of the ICCV Workshops.Google Scholar
Cross Ref
- [70] . 2019. VTNFP: An image-based virtual try-on network with body and clothing feature preservation. In Proceedings of the ICCV.Google Scholar
Cross Ref
- [71] . 2018. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the CVPR.Google Scholar
Cross Ref
- [72] . 2020. Deep Fashion3D: A dataset and benchmark for 3D garment reconstruction from single images. In Proceedings of the ECCV.Google Scholar
Digital Library
Index Terms
Transform, Warp, and Dress: A New Transformation-guided Model for Virtual Try-on
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