Virtual Try-on
23 papers with code • 3 benchmarks • 6 datasets
Virtual try-on of clothing or other items such as glasses and makeup. Most recent techniques use Generative Adversarial Networks.
Benchmarks
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DeepFashion
VITON
Greatest papers with code
Video Based Reconstruction of 3D People Models
This paper describes how to obtain accurate 3D body models and texture of arbitrary people from a single, monocular video in which a person is moving.
Towards Photo-Realistic Virtual Try-On by Adaptively Generating-Preserving Image Content
switchablenorms/DeepFashion_Try_On
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CVPR 2020
Second, a clothes warping module warps clothes image according to the generated semantic layout, where a second-order difference constraint is introduced to stabilize the warping process during training. Third, an inpainting module for content fusion integrates all information (e. g. reference image, semantic layout, warped clothes) to adaptively produce each semantic part of human body.
Towards Photo-Realistic Virtual Try-On by Adaptively Generating$\leftrightarrow$Preserving Image Content
switchablenorms/DeepFashion_Try_On
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First, a semantic layout generation module utilizes semantic segmentation of the reference image to progressively predict the desired semantic layout after try-on.
VITON: An Image-based Virtual Try-on Network
xthan/VITON
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CVPR 2018
We present an image-based VIirtual Try-On Network (VITON) without using 3D information in any form, which seamlessly transfers a desired clothing item onto the corresponding region of a person using a coarse-to-fine strategy.
Toward Characteristic-Preserving Image-based Virtual Try-On Network
sergeywong/cp-vton
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ECCV 2018
Second, to alleviate boundary artifacts of warped clothes and make the results more realistic, we employ a Try-On Module that learns a composition mask to integrate the warped clothes and the rendered image to ensure smoothness.
SwapNet: Garment Transfer in Single View Images
andrewjong/SwapNet
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ECCV 2018
Garment transfer is a challenging task that requires (i) disentangling the features of the clothing from the body pose and shape and (ii) realistic synthesis of the garment texture on the new body.
Ranked #1 on
Virtual Try-on
on FashionIQ
(using extra training data)
Parser-Free Virtual Try-on via Distilling Appearance Flows
geyuying/PF-AFN
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A recent pioneering work employed knowledge distillation to reduce the dependency of human parsing, where the try-on images produced by a parser-based method are used as supervisions to train a "student" network without relying on segmentation, making the student mimic the try-on ability of the parser-based model.
Learning to Transfer Texture from Clothing Images to 3D Humans
In this paper, we present a simple yet effective method to automatically transfer textures of clothing images (front and back) to 3D garments worn on top SMPL, in real time.
Down to the Last Detail: Virtual Try-on with Detail Carving
JDAI-CV/Down-to-the-Last-Detail-Virtual-Try-on-with-Detail-Carving
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However, existing methods can hardly preserve the details in clothing texture and facial identity (face, hair) while fitting novel clothes and poses onto a person.
CP-VTON+: Clothing Shape and Texture Preserving Image-Based Virtual Try-On
minar09/cp-vton-plus
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CVPRW 2020
Recently proposed Image-based virtual try-on (VTON) approaches have several challenges regarding diverse human poses and cloth styles.