  We propose a Leaked Motion Video Predictor (LMVP) to predict future frames by
capturing the spatial and temporal dependencies from given inputs. The motion
is modeled by a newly proposed component, motion guider, which plays the role
of both learner and teacher. Specifically, it {\em learns} the temporal
features from real data and {\em guides} the generator to predict future
frames. The spatial consistency in video is modeled by an adaptive filtering
network. To further ensure the spatio-temporal consistency of the prediction, a
discriminator is also adopted to distinguish the real and generated frames.
Further, the discriminator leaks information to the motion guider and the
generator to help the learning of motion. The proposed LMVP can effectively
learn the static and temporal features in videos without the need for human
labeling. Experiments on synthetic and real data demonstrate that LMVP can
yield state-of-the-art results.
