  To synthesize high-quality person images with arbitrary poses is challenging.
In this paper, we propose a novel Multi-scale Conditional Generative
Adversarial Networks (MsCGAN), aiming to convert the input conditional person
image to a synthetic image of any given target pose, whose appearance and the
texture are consistent with the input image. MsCGAN is a multi-scale
adversarial network consisting of two generators and two discriminators. One
generator transforms the conditional person image into a coarse image of the
target pose globally, and the other is to enhance the detailed quality of the
synthetic person image through a local reinforcement network. The outputs of
the two generators are then merged into a synthetic, discriminant and
high-resolution image. On the other hand, the synthetic image is downsampled to
multiple resolutions as the input to multi-scale discriminator networks. The
proposed multi-scale generators and discriminators handling different levels of
visual features can benefit to synthesizing high-resolution person images with
realistic appearance and texture. Experiments are conducted on the Market-1501
and DeepFashion datasets to evaluate the proposed model, and both qualitative
and quantitative results demonstrate the superior performance of the proposed
MsCGAN.
