In my opinion, the future of GANs will be characterized by the following:

• Open acceptance of GANs and their applications by the research community.
• Impressive results—GANs have so far shown very impressive results on tasks that were difficult to perform using conventional methods. Transforming low-resolution images to high-resolution images, for example, was previously quite a challenging task and was generally carried out using CNNs. GAN architectures, such as SRGANs or pix2pix, have shown the potential of GANs for this application, while the StackGAN network has proved useful for text-to-image synthesis tasks. Nowadays, anyone can create an SRGAN network and train it on their own images.
• Advancements in deep learning techniques.
• GANs being used in commercial applications.
• Maturation of the training process of GANs.