Generative Adversarial Networks are proved to be efficient on various kinds of image generation tasks. However, it is still a challenge if we want to generate images precisely. Many researchers focus on how to generate images with one attribute. But image generation under multiple attributes is still a tough work. In this paper, we try to generate a variety of face images under multiple constraints using a pipeline process. The Pip-GAN (Pipeline Generative Adversarial Network) we present employs a pipeline network structure which can generate a complex facial image step by step using a neutral face image. We applied our method on two face image databases and demonstrate its ability to generate convincing novel images of unseen identities under multiple conditions previously.
@article{arxiv.1711.10742,
title = {Pipeline Generative Adversarial Networks for Facial Images Generation with Multiple Attributes},
author = {Ziqiang Zheng and Zhibin Yu and Haiyong Zheng and Chao Wang and Nan Wang},
journal= {arXiv preprint arXiv:1711.10742},
year = {2017}
}