  This paper introduces a novel deep learning framework for image animation.
Given an input image with a target object and a driving video sequence
depicting a moving object, our framework generates a video in which the target
object is animated according to the driving sequence. This is achieved through
a deep architecture that decouples appearance and motion information. Our
framework consists of three main modules: (i) a Keypoint Detector unsupervisely
trained to extract object keypoints, (ii) a Dense Motion prediction network for
generating dense heatmaps from sparse keypoints, in order to better encode
motion information and (iii) a Motion Transfer Network, which uses the motion
heatmaps and appearance information extracted from the input image to
synthesize the output frames. We demonstrate the effectiveness of our method on
several benchmark datasets, spanning a wide variety of object appearances, and
show that our approach outperforms state-of-the-art image animation and video
generation methods. Our source code is publicly available.
