  We propose a deep video prediction model conditioned on a single image and an
action class. To generate future frames, we first detect keypoints of a moving
object and predict future motion as a sequence of keypoints. The input image is
then translated following the predicted keypoints sequence to compose future
frames. Detecting the keypoints is central to our algorithm, and our method is
trained to detect the keypoints of arbitrary objects in an unsupervised manner.
Moreover, the detected keypoints of the original videos are used as
pseudo-labels to learn the motion of objects. Experimental results show that
our method is successfully applied to various datasets without the cost of
labeling keypoints in videos. The detected keypoints are similar to
human-annotated labels, and prediction results are more realistic compared to
the previous methods.
