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

• URL: http://arxiv.org/abs/2001.06265v1
• Date: Fri, 17 Jan 2020 12:33:54 GMT
• Title: SieveNet: A Unified Framework for Robust Image-Based Virtual Try-On
• Authors: Surgan Jandial, Ayush Chopra, Kumar Ayush, Mayur Hemani, Abhijeet Kumar, and Balaji Krishnamurthy
• Abstract summary: SieveNet is a framework for robust image-based virtual try-on. We introduce a multi-stage coarse-to-fine warping network to better model fine-grained intricacies. We also introduce a try-on cloth conditioned segmentation mask prior to improve the texture transfer network.
• Score: 14.198545992098309
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 dueling triplet loss strategy for training the texture translation network which further improves the quality of the generated try-on results. 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.

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