  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.
