Appears in collection : 2019 - T1 - WS1 - Variational methods and optimization in imaging

Generative models, and in particular adversarial ones, are becoming prevalent in computer vision as they enable enhancing artistic creation, inspire designers, prove usefulness in semi-supervised learning or robotics applications. An important prerequisite towards intelligent behavior is the ability to anticipate future events. Predicting the appearance of future video frames is a proxy task towards pursuing this ability. We will present how generative adversarial networks (GANs) can help, and novel approaches predicting in higher level feature spaces of semantic segmentations. In a second part, we will see how to develop the abilities of GANs to deviate from training examples to generate novel images. Finally, as a limitation of GANs is the production of raw images of low resolution, we present solutions to produce vectorized results.