  Video prediction has been considered a difficult problem because the video
contains not only high-dimensional spatial information but also complex
temporal information. Video prediction can be performed by finding features in
recent frames, and using them to generate approximations to upcoming frames. We
approach this problem by disentangling spatial and temporal features in videos.
We introduce a mutual suppression network (MSnet) which are trained in an
adversarial manner and then produces spatial features which are free of motion
information, and motion features with no spatial information. MSnet then uses
motion-guided connection within an encoder-decoder-based architecture to
transform spatial features from a previous frame to the time of an upcoming
frame. We show how MSnet can be used for video prediction using disentangled
representations. We also carry out experiments to assess the effectiveness of
our method to disentangle features. MSnet obtains better results than other
recent video prediction methods even though it has simpler encoders.
