  A central challenge of video prediction lies where the system has to reason
the objects' future motions from image frames while simultaneously maintaining
the consistency of their appearances across frames. This work introduces an
end-to-end trainable two-stream video prediction framework, Motion-Matrix-based
Video Prediction (MMVP), to tackle this challenge. Unlike previous methods that
usually handle motion prediction and appearance maintenance within the same set
of modules, MMVP decouples motion and appearance information by constructing
appearance-agnostic motion matrices. The motion matrices represent the temporal
similarity of each and every pair of feature patches in the input frames, and
are the sole input of the motion prediction module in MMVP. This design
improves video prediction in both accuracy and efficiency, and reduces the
model size. Results of extensive experiments demonstrate that MMVP outperforms
state-of-the-art systems on public data sets by non-negligible large margins
(about 1 db in PSNR, UCF Sports) in significantly smaller model sizes (84% the
size or smaller).
