3D Virtual-Try-On: The Avatar at Center Stage

Surville, Jean-Marc; Moncoutie, Thierry

doi:10.15221/13.056

Cited by 7 publications

(1 citation statement)

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“…Other line of research leverages 3D models and high fidelity garment data to render garments on various body shapes with high level of accuracy [12,[20][21][22][23][24]. However, such approaches mostly depend on accurate 3D scans or digitized production design data that are costly or challenging to acquire.…”

Section: Related Workmentioning

confidence: 99%

FitGAN: Fit- and Shape-Realistic Generative Adversarial Networks for Fashion

Pecenakova¹,

Karessli²,

Shirvany³

2022

Preprint

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Amidst the rapid growth of fashion e-commerce, remote fitting of fashion articles remains a complex and challenging problem and a main driver of customers' frustration. Despite the recent advances in 3D virtual try-on solutions, such approaches still remain limited to a very narrow -if not only a handful -selection of articles, and often for only one size of those fashion items. Other state-of-the-art approaches that aim to support customers find what fits them online mostly require a high level of customer engagement and privacy-sensitive data (such as height, weight, age, gender, belly shape, etc.), or alternatively need images of customers' bodies in tight clothing. They also often lack the ability to produce fit and shape aware visual guidance at scale, coming up short by simply advising which size to order that would best match a customer's physical body attributes, without providing any information on how the garment may fit and look. Contributing towards taking a leap forward and surpassing the limitations of current approaches, we present FitGAN, a generative adversarial model that explicitly accounts for garments' entangled size and fit characteristics of online fashion at scale. Conditioned on the fit and shape of the articles, our model learns disentangled item representations and generates realistic images reflecting the true fit and shape properties of fashion articles. Through experiments on real world data at scale, we demonstrate how our approach is capable of synthesizing visually realistic and diverse fits of fashion items and explore its ability to control fit and shape of images for thousands of online garments.

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Section: Related Workmentioning

confidence: 99%