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Garment transfer shows great potential in realistic applications with thegoal of transfering outfits across different people images. However, garmenttransfer between images with heavy misalignments or severe occlusions stillremains as a challenge. In this work, we propose Complementary TransferingNetwork (CT-Net) to adaptively model different levels of geometric changes andtransfer outfits between different people. In specific, CT-Net consists ofthree modules: 1) A complementary warping module first estimates twocomplementary warpings to transfer the desired clothes in differentgranularities. 2) A layout prediction module is proposed to predict the targetlayout, which guides the preservation or generation of the body parts in thesynthesized images. 3) A dynamic fusion module adaptively combines theadvantages of the complementary warpings to render the garment transferresults. Extensive experiments conducted on DeepFashion dataset demonstratethat our network synthesizes high-quality garment transfer images andsignificantly outperforms the state-of-art methods both qualitatively andquantitatively.

CT-Net: Complementary Transfering Network for Garment Transfer with Arbitrary Geometric Changes