Content-Based Attention Network for Person Image Generation

Author:

Liu Xiongfei1,Li Bengao1,Chen Xin2,Zhang Haiyan1,Zhan Shu1ORCID

Affiliation:

1. School of Computer and Information Engineering, Hefei University of Technology, Hefei, Anhui, P. R. China

2. The Second Hospital of Anhui Medical University, Hefei, Anhui, P. R. China

Abstract

This paper proposes a novel method for person image generation with arbitrary target pose. Given a person image and an arbitrary target pose, our proposed model can synthesize images with the same person but different poses. The Generative Adversarial Networks (GANs) are the major part of the proposed model. Different from the traditional GANs, we add attention mechanism to the generator in order to generate realistic-looking images, we also use content reconstruction with a pretrained VGG16 Net to keep the content consistency between generated images and target images. Furthermore, we test our model on DeepFashion and Market-1501 datasets. The experimental results show that the proposed network performs favorably against state-of-the-art methods.

Funder

National Nature Science Foundation of China Grand

Publisher

World Scientific Pub Co Pte Lt

Subject

Electrical and Electronic Engineering,Hardware and Architecture,Electrical and Electronic Engineering,Hardware and Architecture

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