  Existing video prediction methods mainly rely on observing multiple
historical frames or focus on predicting the next one-frame. In this work, we
study the problem of generating consecutive multiple future frames by observing
one single still image only. We formulate the multi-frame prediction task as a
multiple time step flow (multi-flow) prediction phase followed by a
flow-to-frame synthesis phase. The multi-flow prediction is modeled in a
variational probabilistic manner with spatial-temporal relationships learned
through 3D convolutions. The flow-to-frame synthesis is modeled as a generative
process in order to keep the predicted results lying closer to the manifold
shape of real video sequence. Such a two-phase design prevents the model from
directly looking at the high-dimensional pixel space of the frame sequence and
is demonstrated to be more effective in predicting better and diverse results.
Extensive experimental results on videos with different types of motion show
that the proposed algorithm performs favorably against existing methods in
terms of quality, diversity and human perceptual evaluation.
