Pipeline Generative Adversarial Networks for Facial Images Generation with Multiple Attributes
This addresses the problem of precise multi-attribute image generation for facial images, which is an incremental improvement over single-attribute methods.
The paper tackles the challenge of generating facial images with multiple attributes by proposing Pip-GAN, a pipeline network that uses a neutral face image to produce complex images step-by-step, demonstrating its ability to generate convincing novel images of unseen identities under multiple conditions on two face image databases.
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.