Generative models have made remarkable progress in generating realistic images and videos of high quality. Specifically, video generation entails a number of challenges w.r.t. complexity and computation, associated to the simultaneous modeling of appearance, as well as motion.

I will talk about our work related to design of generative models, which allow for realistic generation of face images and videos. We have placed emphasis on disentangling motion from appearance and have learned motion representations directly from RGB, without structural representations such as facial landmarks or 3D meshes. We have aimed at constructing motion as linear displacement of codes in the latent space.  While highly intriguing, video generation has thrusted upon us the imminent danger of deepfakes, which can offer unprecedented levels of increasingly realistic manipulated videos. Deepfakes pose an imminent security threat to us all, and to date, deepfakes are able to mislead face recognition systems, as well as humans. Hence, it is beneficial to design generation and detection methods in parallel.