Summary: The system attacks the problem of generating images that conform to a given source image driven by motion estimated from a given video. These transformations are composed from transformations with respect to a common reference configuration. The sparse transformations are converted with a CNN into dense motion and occlusion masks. Finally, the motion and occlusion are combined by another neural network with the input image to create the final output. Positive: The paper introduces the novel idea of first order motion and occlusion modeling to unsupervised image animation.

You know, there is is an old phrase that says'seeing is believing'. But in the world of'Deepfake' what we see is not always true. Let's define deep fake with the help of an example video. I used only a single stamp size photos of these people to create these videos. Yes, you heard it right.