SieveNet: A Unified Framework for Robust Image-Based Virtual Try-On

Jandial, Surgan and Chopra, Ayush and Ayush, Kumar and Hemani, Mayur and Kumar, Abhijeet and Krishnamurthy, Balaji (2020) SieveNet: A Unified Framework for Robust Image-Based Virtual Try-On. In: 2020 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2020, 1- 5 March 2020, Snowmass Village.

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Abstract

Image-based virtual try-on for fashion has gained considerable attention recently. The task requires trying on a clothing item on a target model image. An efficient framework for this is composed of two stages: (1) warping (transforming) the try-on cloth to align with the pose and shape of the target model, and (2) a texture transfer module to seamlessly integrate the warped try-on cloth onto the target model image. Existing methods suffer from artifacts and distortions in their try-on output. In this work, we present SieveNet, a framework for robust image-based virtual try-on. Firstly, we introduce a multi-stage coarse-to-fine warping network to better model fine grained intricacies (while transforming the try-on cloth) and train it with a novel perceptual geometric matching loss. Next, we introduce a try-on cloth conditioned segmentation mask prior to improve the texture transfer network. Finally, we also introduce a duelling triplet loss strategy for training the texture translation network which further improves the quality of generated try-on result. We present extensive qualitative and quantitative evaluations of each component of the proposed pipeline and show significant performance improvements against the current state-of-the-art method. © 2020 IEEE.

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IITH Creators:

IITH Creators	ORCiD

Item Type:

Conference or Workshop Item (Paper)

Uncontrolled Keywords:

Coarse to fine; Geometric matching; Quantitative evaluation; Segmentation masks; State-of-the-art methods; Texture transfer; Unified framework; Virtual try-on

Subjects:

Computer science
Computer science > Special computer methods

Divisions:

Department of Computer Science & Engineering

Depositing User:

. LibTrainee 2021

Date Deposited:

23 Nov 2022 07:26

Last Modified:

23 Nov 2022 07:26

URI:

http://raiithold.iith.ac.in/id/eprint/11330