  Given a still photograph, one can imagine how dynamic objects might move
against a static background. This idea has been actualized in the form of
cinemagraphs, where the motion of particular objects within a still image is
repeated, giving the viewer a sense of animation. In this paper, we learn
computational models that can generate cinemagraph sequences automatically
given a single image. To generate cinemagraphs, we explore combining generative
models with a recurrent neural network and deep Q-networks to enhance the power
of sequence generation. To enable and evaluate these models we make use of two
datasets, one synthetically generated and the other containing real video
generated cinemagraphs. Both qualitative and quantitative evaluations
demonstrate the effectiveness of our models on the synthetic and real datasets.
