PSGAN: Pose-Robust Spatial-Aware GAN for Customizable Makeup Transfer

by   Wentao Jiang, et al.
National University of Singapore
Beihang University

We propose a novel Pose-robust Spatial-aware GAN (PSGAN) for transferring the makeup style from a reference image to a source image. Previous GAN-based methods often fail in cases with variant poses and expressions. Also, they cannot adjust the shade of makeup or specify the part of transfer. To address these issues, the proposed PSGAN includes a Makeup Distillation Network to distill the makeup style of the reference image into two spatial-aware makeup matrices. Then an Attentive Makeup Morphing module is introduced to specify how a pixel in the source image is morphed from the reference image. The pixelwise correspondence is built upon both the relative position features and visual features. Based on the morphed makeup matrices, a De-makeup Re-makeup Network performs makeup transfer. By incorporating the above novelties, our PSGAN not only achieves state-of-the-art results on the existing datasets, but also is able to perform the customizable part-by-part, shade controllable and pose-robust makeup transfer.


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Code Repositories


PyTorch code for "PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer" (CVPR 2020 Oral)

view repo


PyTorch implementation of PSGAN: Pose and Expression Robust Spatial-Aware GAN for Customizable Makeup Transfer

view repo

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