Disentangled Cycle Consistency for Highly-realistic Virtual Try-On

• URL: http://arxiv.org/abs/2103.09479v2
• Date: Fri, 19 Mar 2021 08:08:17 GMT
• Title: Disentangled Cycle Consistency for Highly-realistic Virtual Try-On
• Authors: Chongjian Ge, Yibing Song, Yuying Ge, Han Yang, Wei Liu and Ping Luo
• Abstract summary: Image virtual try-on replaces the clothes on a person image with a desired in-shop clothes image. Existing methods formulate virtual try-on as either in-painting or cycle consistency. We propose a Disentangled Cycle-consistency Try-On Network (DCTON)
• Score: 34.97658860425598
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Image virtual try-on replaces the clothes on a person image with a desired in-shop clothes image. It is challenging because the person and the in-shop clothes are unpaired. Existing methods formulate virtual try-on as either in-painting or cycle consistency. Both of these two formulations encourage the generation networks to reconstruct the input image in a self-supervised manner. However, existing methods do not differentiate clothing and non-clothing regions. A straight-forward generation impedes virtual try-on quality because of the heavily coupled image contents. In this paper, we propose a Disentangled Cycle-consistency Try-On Network (DCTON). The DCTON is able to produce highly-realistic try-on images by disentangling important components of virtual try-on including clothes warping, skin synthesis, and image composition. To this end, DCTON can be naturally trained in a self-supervised manner following cycle consistency learning. Extensive experiments on challenging benchmarks show that DCTON outperforms state-of-the-art approaches favorably.

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