Robust 3D Garment Digitization from Monocular 2D Images for 3D Virtual Try-On Systems

Parameswaran, Sandeep N.; Babar, Sadbhavana; Garg, Vikram; Srivastava, Astitva; Sharma, Avinash

doi:10.1109/wacv51458.2022.00148

Cited by 13 publications

(6 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…FastkNN [152] FCNN [56] FNN [32] Fuzzylogic [106,113] GAN [71,75,99,129,134,141,155,251] GA-RF [65] GaussianMixtureModels [109,118] gradientboostingdecisiontree [42] GraphCNN [40,122] GraphDCNN [89] GRU [230] GSN [87] H-CNN [39,157] HOG [153] HypergraphNN [80] Imageprocessing [119,120,130,132,135,136,147] k-center [117] k-means [114,203,232,244] k-medoids [115] Kneser-Ney [34] kNN [54,137,222,235,237,250] LAC [107,116] Lassoregression …”

Section: Methods Citationsmentioning

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%

“…DeepFashion3D [135] DeepFashion-C [57] DressCode [129] FashionAI2018 [72] FashionDNA [88] Fashionista [108] FashionLandmarkdetection [57] FashionMNIST [39,74,87] Fashion-MNIST [58,62,65,68] FashionVC [166] Feidegger [123] FindFashion [40] GoogleAnalytics [212] ImageNet [47,56,63] Image-Net [157] iPER [141] Kaggle [60,125,173] LookBook [75] MovingFashion [51] MPV [134,140] POG [159] Table 7. Cont.…”

Section: Databases Citationsmentioning

confidence: 99%

See 2 more Smart Citations

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

Goti,

Querejeta-Lomas,

Almeida

et al. 2023

Mathematics

View full text Add to dashboard Cite

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.

show abstract

Section: Methods Citationsmentioning

confidence: 99%

Section: R-cnn [59]mentioning

confidence: 99%

Section: Databases Citationsmentioning

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

View full text Add to dashboard Cite

show abstract

“…They considered that the measurements of the clothes are known, which is incorrect as they have to detect the measurements from the image. Sahib Majithia et al in [15] and [12] stated that the main issue they faced was the requirement to present a trustworthy 3D garment digitization technology that can function well with real-world fashion; they performed high-quality texture mapping from an input catalogue photo to UV map panels. In [16] and [17], They began by anticipating a small group of 2D landmarks along the edge of the garment, then used these landmarks to transfer texture using a thin-plate spline on UV map panels.…”

Section: Related Workmentioning

confidence: 99%

Fit Moi: Online Virtual Fitting Room With Texture Identification And Recommendation System

AbdElminaam,

mohamed,

osama

et al. 2023

Journal of Computing and Communication

View full text Add to dashboard Cite

Virtual fitting rooms have attracted the sights of the market need in the past two years; The world faced a colossal pandemic that led everything to be online. This proposed framework aims to solve the online shopping problem using deep learning algorithms and techniques. Users face significant issues, whether finding the perfect fit, recognizing the fabric, or styling their outfits. As a result, merchants' profit is impacted, resulting in a high percentage of returns. This proposed model aims to generate a 3D human model. Based on the measurements extracted from the user's images and the user's weight and height for more accurate results. The user can see how simulated 3D clothes will look on his 3D body model, and Then he will know the exact size that fits him and can view the texture. Finally, the user will have styling tips recommended according to his chosen outfit.

show abstract

“…Virtual Try-on. Virtual try-on approaches can be categorized into 3D-based [7,21,30,38] and image-based methods [9,17,22,23,28,29,32,40,41,51]. Imagebased methods are more promising because of their lightweight nature and the ability to generate reasonable results using large-scale try-on datasets.…”

Section: Related Workmentioning

confidence: 99%

Impact of user-generated travel posts on travel decisions: A comparative study on Weibo and Xiaohongshu

Wang

Huang²,

Liu-Lastres³

2022

Annals of Tourism Research Empirical Insights

View full text Add to dashboard Cite

Robust 3D Garment Digitization from Monocular 2D Images for 3D Virtual Try-On Systems

Cited by 13 publications

References 43 publications

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

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

Fit Moi: Online Virtual Fitting Room With Texture Identification And Recommendation System

Impact of user-generated travel posts on travel decisions: A comparative study on Weibo and Xiaohongshu

Contact Info

Product

Resources

About