  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.
