  The ability to produce convincing textural details is essential for the
fidelity of synthesized person images. However, existing methods typically
follow a ``warping-based'' strategy that propagates appearance features through
the same pathway used for pose transfer. However, most fine-grained features
would be lost due to down-sampling, leading to over-smoothed clothes and
missing details in the output images. In this paper we presents RATE-Net, a
novel framework for synthesizing person images with sharp texture details. The
proposed framework leverages an additional texture enhancing module to extract
appearance information from the source image and estimate a fine-grained
residual texture map, which helps to refine the coarse estimation from the pose
transfer module. In addition, we design an effective alternate updating
strategy to promote mutual guidance between two modules for better shape and
appearance consistency. Experiments conducted on DeepFashion benchmark dataset
have demonstrated the superiority of our framework compared with existing
networks.
