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Learning Residual Images for Face Attribute Manipulation

Wei Shen, Rujie Liu

Face attributes are interesting due to their detailed description of human faces. Unlike prior research working on attribute prediction, we address an inverse and more challenging problem called face attribute manipulation which aims at modifying a face image according to a given attribute value. In order to obtain an efficient representation for the manipulation, we propose to learn the corresponding residual image which is defined as the difference between images after and before the manipulation. Using the residual image, the manipulation can be operated efficiently with modest pixel modification. The framework of our approach is based on the Generative Adversarial Network. It consists of two image transformation networks imitating the attribute manipulation and its dual operation and a shared discriminative network distinguishing the generated images from different reference images. We also apply dual learning to allow the two transformation networks to learn from each other. Experiments show that the learned residual images successfully simulate the manipulations and the generated images retain most of the details in attribute-irrelevant areas.

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