  Pose Guided Human Image Synthesis (PGHIS) is a challenging task of
transforming a human image from the reference pose to a target pose while
preserving its style. Most existing methods encode the texture of the whole
reference human image into a latent space, and then utilize a decoder to
synthesize the image texture of the target pose. However, it is difficult to
recover the detailed texture of the whole human image. To alleviate this
problem, we propose a method by decoupling the human body into several parts
(\eg, hair, face, hands, feet, \etc) and then using each of these parts to
guide the synthesis of a realistic image of the person, which preserves the
detailed information of the generated images. In addition, we design a
multi-head attention-based module for PGHIS. Because most convolutional neural
network-based methods have difficulty in modeling long-range dependency due to
the convolutional operation, the long-range modeling capability of attention
mechanism is more suitable than convolutional neural networks for pose transfer
task, especially for sharp pose deformation. Extensive experiments on
Market-1501 and DeepFashion datasets reveal that our method almost outperforms
other existing state-of-the-art methods in terms of both qualitative and
quantitative metrics.
