Garment transfer shows great potential in realistic applications with the
goal of transfering outfits across different people images. However, garment
transfer between images with heavy misalignments or severe occlusions still
remains as a challenge. In this work, we propose Complementary Transfering
Network (CT-Net) to adaptively model different levels of geometric changes and
transfer outfits between different people. In specific, CT-Net consists of
three modules: 1) A complementary warping module first estimates two
complementary warpings to transfer the desired clothes in different
granularities. 2) A layout prediction module is proposed to predict the target
layout, which guides the preservation or generation of the body parts in the
synthesized images. 3) A dynamic fusion module adaptively combines the
advantages of the complementary warpings to render the garment transfer
results. Extensive experiments conducted on DeepFashion dataset demonstrate
that our network synthesizes high-quality garment transfer images and
significantly outperforms the state-of-art methods both qualitatively and
quantitatively.

该论文提出了 Complementary Transfering Network（CT-Net）模型，其中包括了互补配准模块、布局预测模块、动态融合模块等三部分，通过在 DeepFashion 数据集上的实验，证明了本研究的高质量服装迁移图像合成能力，并在品质和数量上显著优于现有方法。