Pipeline Generative Adversarial Networks for Facial Images Generation with Multiple Attributes

2017/11/29 by Ziqiang Zheng, Zhibin Yu, Zheng, Ziqiang +7
Computer Science · #Advanced Image Processing Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Face recognition and analysis #Generative Adversarial Networks and Image Synthesis

paper · pdf · doi:10.48550/arxiv.1711.10742

openalex publication_date 2017/11/29 · openalex created_date 2017/12/22 · openalex updated_date 2026/07/28

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.

Citations

• Conditional Generative Adversarial Nets
• Energy-based Generative Adversarial Network
• Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling
• Conditional Image Synthesis With Auxiliary Classifier GANs
• Improved Training of Wasserstein GANs
• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
• Towards Large-Pose Face Frontalization in the Wild
• Language Generation with Recurrent Generative Adversarial Networks without Pre-training
• Photographic Image Synthesis with Cascaded Refinement Networks
• Deep Generative Image Models using a Laplacian Pyramid of Adversarial\n Networks

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• FaceFeat-GAN: a Two-Stage Approach for Identity-Preserving Face Synthesis
• Differential Generative Adversarial Networks: Synthesizing Non-linear Facial Variations with Limited Number of Training Data
• Editable Generative Adversarial Networks: Generating and Editing Faces Simultaneously