Paper Info
Title
Towards Photo-Realistic Virtual Try-On by Adaptively Generating↔Preserving Image Content
Abstract
Image visual try-on aims at transferring a target clothes image onto a reference person, and has become a hot topic in recent years. Prior arts usually focus on preserving the character of a clothes image (e.g. texture, logo, embroidery) when warping it to arbitrary human pose. However, it remains a big challenge to generate photo-realistic try-on images when large occlusions and human poses are presented in the reference person. To address this issue, we propose a novel visual try-on network, namely Adaptive Content Generating and Preserving Network (ACGPN). In particular, ACGPN first predicts semantic layout of the reference image that will be changed after try-on (e.g.long sleeve shirt→arm, arm→jacket), and then determines whether its image content needs to be generated or preserved according to the predicted semantic layout, leading to photo-realistic try-on and rich clothes details. ACGPN generally involves three major modules. First, a semantic layout generation module utilizes semantic segmentation of the reference image to progressively predict the desired semantic layout after try-on. 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. In comparison to the state-of-the-art methods, ACGPN can generate photo-realistic images with much better perceptual quality and richer fine-details.
Year
DOI
Venue
2020
10.1109/CVPR42600.2020.00787
CVPR
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
24
6
Name
Order
Citations
PageRank
Yang Han18611.17
Ruimao Zhang232518.86
Xiaobao Guo300.34
Wei Liu44041204.19
Wangmeng Zuo53833173.11
Ping Luo62540111.68