Abstract
Face attributes are interesting due to their detailed description of human faces. Unlike prior researches 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. Instead of manipulating the whole
image, we propose to learn the corresponding residual image defined as the difference between images before and
after the manipulation. In this way, 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 and a discriminative network. The transformation
networks are responsible for the attribute manipulation and
its dual operation and the discriminative network is used
to distinguish the generated images from real images. We
also apply dual learning to allow transformation networks
to learn from each other. Experiments show that residual
images can be effectively learned and used for attribute manipulations. The generated images remain most of the details in attribute-irrelevant areas.