A State-of-the-Art Review on Image Synthesis with Generative Adversarial Networks

Purpose: The remarkable performance of Generative Adversarial Networks (GANs) in various applications has made them a popular subject in computer vision research, and they have also shown remarkable success in picture synthesis tasks. Materials and Methods: Image processing, synthesis, generation, semantic editing, translation, super-resolution, inpainting, and cartoon creation are all areas covered in this article's presentation of the most recent GAN research. To demonstrate how they have improved the result, they analyze the methods used by these applications and describe them. Findings: Insights into GAN research and a presentation of GAN-based applications in diverse contexts are the goals of this paper (Anon, 2022). Implications to Theory, Practice and Policy: Following this, we will go over some of the difficulties encountered by GANs and provide solutions to these issues. We also discuss potential future areas of study for GANs, including video creation, 3D face reconstruction, and facial animation synthesis.