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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 targetpose while maintaining the source appearance. Given the inefficiency ofstandard CNNs in handling large spatial transformation, we propose astructure-aware flow based method for high-quality person image generation.Specifically, instead of learning the complex overall pose changes of humanbody, we decompose the human body into different semantic parts (e.g., head,torso, and legs) and apply different networks to predict the flow fields forthese parts separately. Moreover, we carefully design the network modules toeffectively capture the local and global semantic correlations of featureswithin and among the human parts respectively. Extensive experimental resultsshow that our method can generate high-quality results under large posediscrepancy and outperforms state-of-the-art methods in both qualitative andquantitative comparisons.

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