Image visual try-on aims at transferring a target clothing image onto a
reference person, and has become a hot topic in recent years. Prior arts
usually focus on preserving the character of a clothing 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$\rightarrow$arm, arm$\rightarrow$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 clothing
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 clothing images 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.

本文提出了一种新型的可视化试衣神经网络，名为自适应内容生成与保存网络（ACGPN），它首先预测变换后的参考图像的语义布局，然后根据预测的语义布局决定其图像内容是否需要生成或保留，从而实现了更加具有感知质量和丰富细节的逼真试穿图像。