PIVTONS: Pose Invariant Virtual Try-On Shoe with Conditional Image Completion

Chou, Chao-Te; Lee, Cheng‐Han; Zhang, Kaipeng; Lee, Hu-Cheng; Hsu, Winston H.

doi:10.1007/978-3-030-20876-9_41

Cited by 12 publications

(6 citation statements)

References 17 publications

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“…The fit of virtual clothing is frequently evaluated using neural networks [144], including CNN [140] and GAN variations [141]. The employment of alternative algorithms, such as the naive Bayes (NB) algorithm for classification [149], the Viola-Jones (VJ) algorithm for body tracking [148], or the fuzzy neural network (FNN) [32], has also been the subject of some research.…”

Section: Customer Satisfactionmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Goti,

Querejeta-Lomas,

Almeida

et al. 2023

Mathematics

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Many industries, including healthcare, banking, the auto industry, education, and retail, have already undergone significant changes because of artificial intelligence (AI). Business-to-Customer (B2C) e-commerce has considerably increased the use of AI in recent years. The purpose of this research is to examine the significance and impact of AI in the realm of fashion e-commerce. To that end, a systematic review of the literature is carried out, in which data from the Web Of Science and Scopus databases were used to analyze 219 publications on the subject. The articles were first categorized using AI techniques. In the realm of fashion e-commerce, they were divided into two categories. These categorizations allowed for the identification of research gaps in the use of AI. These gaps offer potential and possibilities for further research.

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Section: Customer Satisfactionmentioning

confidence: 99%

“…Summary of purposes with references. Sentimentanalysis [42,44,[169][170][171][172][173][174][175][176][177][178][179][180] Virtualfitting [32,[129][130][131][132][133][134][135][136][138][139][140][141][142][143][144][145][146][149][150][151][152] Table 7. Summary of databases with references.…”

Section: R-cnn [59]mentioning

confidence: 99%

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Goti,

Querejeta-Lomas,

Almeida

et al. 2023

Mathematics

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“…Issenhuth et al [2019] incorporates adversarial loss in Wang et al [2018a] to further improve image quality. PIVTONS [Chou et al 2018] applies a similar concept on shoes rather than tops and shirts. Dong et al [2019] extends Han et al [2018] to synthesize try on in various body poses.…”

Section: Related Workmentioning

confidence: 99%

TryOnGAN

Lewis

Varadharajan

Kemelmacher-Shlizerman

2021

ACM Trans. Graph.

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Given a pair of images---target person and garment on another person---we automatically generate the target person in the given garment. Previous methods mostly focused on texture transfer via paired data training, while overlooking body shape deformations, skin color, and seamless blending of garment with the person. This work focuses on those three components, while also not requiring paired data training. We designed a pose conditioned StyleGAN2 architecture with a clothing segmentation branch that is trained on images of people wearing garments. Once trained, we propose a new layered latent space interpolation method that allows us to preserve and synthesize skin color and target body shape while transferring the garment from a different person. We demonstrate results on high resolution 512 × 512 images, and extensively compare to state of the art in try-on on both latent space generated and real images.

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“…Recent breakthroughs in deep generative models, especially Variational Autoencoders (VAEs) [27], Generative Adversarial Networks (GANs) [12], and their variants [22,40,7,28], open a new door to a myriad of fashion applications in computer vision, including fashion design [25,49], language-guided fashion synthesis [73,47,13], virtual try-on systems [15,59,5], clothing-based appearance transfer [44,69], etc. Unlike generating images of rigid objects, fashion synthesis is more complicated as it involves multiple clothing items that form a compatible outfit.…”

Section: Introductionmentioning

confidence: 99%

Compatible and Diverse Fashion Image Inpainting

Han,

Wu,

Huang

et al. 2019

Preprint

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PIVTONS: Pose Invariant Virtual Try-On Shoe with Conditional Image Completion

Cited by 12 publications

References 17 publications

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

Artificial Intelligence in Business-to-Customer Fashion Retail: A Literature Review

TryOnGAN

Compatible and Diverse Fashion Image Inpainting

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