Generative Cooperative Network for Person Image Generation

Abstract

Person image generation is to synthesize realistic pedestrian images that follow the same distribution as the given dataset. Previous attempts can be generally categorized into two classes: some methods use human pose information as guidance and others try to generate person images from scratch. The former is to transfer the pose of a source image to a reference pose. The generated person image have the same identity as the source image. The latter takes a random noise from latent space as input, and the real person images are only used as references for the discriminator. While pose-guided person image generation is widely studied, generating-from-scratch methods are also worth exploring because they can synthesize person image with new identity, which is a useful manner of data augmentation. These two types of generating methods have their different advantages and disadvantages, and sometimes they are complementary. In this work, the authors design a Generative Cooperative Network (GCN) to jointly train two types of GANs. The two GANs serve different purposes, and can learn from each other during the cooperative learning procedure. The proposed approach is verified on public datasets, and the results show that our GCN improves the performance of the baseline methods. Comparisons with state-of-the-art methods also prove the effectiveness of the proposed method.