  Generative Adversarial Networks (GANs) have been extremely successful in
various application domains such as computer vision, medicine, and natural
language processing. Moreover, transforming an object or person to a desired
shape become a well-studied research in the GANs. GANs are powerful models for
learning complex distributions to synthesize semantically meaningful samples.
However, there is a lack of comprehensive review in this field, especially lack
of a collection of GANs loss-variant, evaluation metrics, remedies for diverse
image generation, and stable training. Given the current fast GANs development,
in this survey, we provide a comprehensive review of adversarial models for
image synthesis. We summarize the synthetic image generation methods, and
discuss the categories including image-to-image translation, fusion image
generation, label-to-image mapping, and text-to-image translation. We organize
the literature based on their base models, developed ideas related to
architectures, constraints, loss functions, evaluation metrics, and training
datasets. We present milestones of adversarial models, review an extensive
selection of previous works in various categories, and present insights on the
development route from the model-based to data-driven methods. Further, we
highlight a range of potential future research directions. One of the unique
features of this review is that all software implementations of these GAN
methods and datasets have been collected and made available in one place at
https://github.com/pshams55/GAN-Case-Study.
