Structure-aware Person Image Generation with Pose Decomposition and Semantic Correlation

In this paper we tackle the problem of pose guided person image generation, which aims to transfer a person image from the source pose to a novel target pose while maintaining the source appearance. Given the inefficiency of standard CNNs in handling large spatial transformation, we propose a structure-aware flow based method for high-quality person image generation. Specifically, instead of learning the complex overall pose changes of human body, we decompose the human body into different semantic parts (e. g. , head, torso, and legs) and apply different networks to predict the flow fields for these parts separately. Moreover, we carefully design the network modules to effectively capture the local and global semantic correlations of features within and among the human parts respectively. Extensive experimental results show that our method can generate high-quality results under large pose discrepancy and outperforms state-of-the-art methods in both qualitative and quantitative comparisons.

Authors

• Jilin Tang
• Yi Yuan Douyin Vision
• Tianjia Shao State Key Lab of CAD&CG, Zhejiang University
• Yong Liu
• Mengmeng Wang College of Computer Science and Technology, Zhejiang University of Technology SGIT AI Lab, State Grid Corporation of China
• Kun Zhou State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China

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