Learning Residual Images for Face Attribute Manipulation

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.

References in corpus (3)

• Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
• DRAW: A Recurrent Neural Network For Image Generation
• Dual Learning for Machine Translation

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• An Introduction to Image Synthesis with Generative Adversarial Nets
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• ACE: Adapting to Changing Environments for Semantic Segmentation
• CounteRGAN: Generating Realistic Counterfactuals with Residual Generative Adversarial Nets