An Efficient Style Virtual Try on Network for Clothing Business Industry
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Abstract (arXiv)
With the increasing development of garment manufacturing industry, the method of combining neural network with industry to reduce product redundancy has been paid more and more attention.In order to reduce garment redundancy and achieve personalized customization, more researchers have appeared in the field of virtual trying on.They try to transfer the target clothing to the reference figure, and then stylize the clothes to meet user's requirements for fashion.But the biggest problem of virtual try on is that the shape and motion blocking distort the clothes, causing the patterns and texture on the clothes to be impossible to restore. This paper proposed a new stylized virtual try on network, which can not only retain the authenticity of clothing texture and pattern, but also obtain the undifferentiated stylized try on. The network is divided into three sub-networks, the first is the user image, the front of the target clothing image, the semantic segmentation image and the posture heat map to generate a more detailed human parsing map. Second, UV position map and dense correspondence are used to map patterns and textures to the deformed silhouettes in real time, so that they can be retained in real time, and the rationality of spatial structure can be guaranteed on the basis of improving the authenticity of images. Third,Stylize and adjust the generated virtual try on image. Through the most subtle changes, users can choose the texture, color and style of clothing to improve the user's experience.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 29 of its 50 references that are in the map. First posted on arXiv 27 May 2021.
Builds on
29 of 50 references.
References · 29
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,510
- 2018Large Scale GAN Training for High Fidelity Natural Image Synthesiscited by 2,609
- 2016Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Networkcited by 1,898
- 2016Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,061
- 2017StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translationcited by 1,036
- 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 858
- 2016Generative Adversarial Text to Image Synthesiscited by 735
- 2018DensePose: Dense Human Pose Estimation In The Wildcited by 573
- 2018Video-to-Video Synthesiscited by 388
- 2017Universal Style Transfer via Feature Transformscited by 352
- 2017VITON: An Image-based Virtual Try-on Networkcited by 285
17 more
- 2017Pose Guided Person Image Generationcited by 251
- 2018Toward Characteristic-Preserving Image-based Virtual Try-On Networkcited by 192
- 2019Camera Distance-aware Top-down Approach for 3D Multi-person Pose Estimation from a Single RGB Imagecited by 131
- 2017CondenseNet: An Efficient DenseNet using Learned Group Convolutionscited by 121
- 2016Energy-based Generative Adversarial Networkcited by 119
- 2017Look into Person: Self-supervised Structure-sensitive Learning and A New Benchmark for Human Parsingcited by 109
- 2018Synthesizing Images of Humans in Unseen Posescited by 96
- 2019Towards Multi-pose Guided Virtual Try-on Networkcited by 95
- 2019AET vs. AED: Unsupervised Representation Learning by Auto-Encoding Transformations rather than Datacited by 77
- 2018Soft-Gated Warping-GAN for Pose-Guided Person Image Synthesiscited by 59
- 2018ELEGANT: Exchanging Latent Encodings with GAN for Transferring Multiple Face Attributescited by 50
- 2017The Conditional Analogy GAN: Swapping Fashion Articles on People Imagescited by 33
- 2019Guided Image-to-Image Translation with Bi-Directional Feature Transformationcited by 30
- 2017Fashion Forward: Forecasting Visual Style in Fashioncited by 24
- 2020SieveNet: A Unified Framework for Robust Image-Based Virtual Try-Oncited by 23
- 2018AMNet: Memorability Estimation with Attentioncited by 16
- 2017Unsupervised Visual Attribute Transfer with Reconfigurable Generative Adversarial Networkscited by 2
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.