Reviews: First Order Motion Model for Image Animation

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.