A pair of groundbreaking papers in computer vision open new vistas on possibilities in the realms of creating very real-looking natural images and synthesizing realistic, identity-preserving facial images. In CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training, presented this past October at ICCV 2017 in Venice, the team of researchers from Microsoft and the University of Science and Technology of China came up with a model for image generation based on a variational autoencoder generative adversarial network capable of synthesizing natural images in what are known as fine-grained categories. Fine-grained categories would include faces of specific individuals, say of celebrities, or real-world objects such as specific types of flowers or birds.