  In this paper, we propose a multi-stage and high-resolution model for image
synthesis that uses fine-grained attributes and masks as input. With a
fine-grained attribute, the proposed model can detailedly constrain the
features of the generated image through rich and fine-grained semantic
information in the attribute. With mask as prior, the model in this paper is
constrained so that the generated images conform to visual senses, which will
reduce the unexpected diversity of samples generated from the generative
adversarial network. This paper also proposes a scheme to improve the
discriminator of the generative adversarial network by simultaneously
discriminating the total image and sub-regions of the image. In addition, we
propose a method for optimizing the labeled attribute in datasets, which
reduces the manual labeling noise. Extensive quantitative results show that our
image synthesis model generates more realistic images.
