Learning Residual Images for Face Attribute Manipulation
This addresses the inverse problem of modifying face images based on attributes, which is challenging for applications in image editing and synthesis, though it appears incremental as it builds on existing GAN frameworks.
The paper tackles the problem of face attribute manipulation by learning residual images, which represent pixel differences before and after manipulation, enabling efficient modifications with minimal pixel changes. Experiments demonstrate that residual images can be effectively learned and used for attribute manipulations while preserving details in irrelevant areas.
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.