  Human image generation is a very challenging task since it is affected by
many factors. Many human image generation methods focus on generating human
images conditioned on a given pose, while the generated backgrounds are often
blurred.In this paper,we propose a novel Partition-Controlled GAN to generate
human images according to target pose and background. Firstly, human poses in
the given images are extracted, and foreground/background are partitioned for
further use. Secondly, we extract and fuse appearance features, pose features
and background features to generate the desired images. Experiments on
Market-1501 and DeepFashion datasets show that our model not only generates
realistic human images but also produce the human pose and background as we
want. Extensive experiments on COCO and LIP datasets indicate the potential of
our method.
