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arXiv · 1711.10742

Pipeline Generative Adversarial Networks for Facial Images Generation with Multiple Attributes

Ziqiang Zheng·Zhibin Yu·Haiyong Zheng·Chao Wang·Nan Wang
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Abstract

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.

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Ziqiang Zheng, Zhibin Yu, Haiyong Zheng, Chao Wang, Nan Wang. 2017-11-29. Pipeline Generative Adversarial Networks for Facial Images Generation with Multiple Attributes. https://arxiv.org/abs/1711.10742

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