  Imagining multiple consecutive frames given one single snapshot is
challenging, since it is difficult to simultaneously predict diverse motions
from a single image and faithfully generate novel frames without visual
distortions. In this work, we leverage an unsupervised variational model to
learn rich motion patterns in the form of long-term bi-directional flow fields,
and apply the predicted flows to generate high-quality video sequences. In
contrast to the state-of-the-art approach, our method does not require external
flow supervisions for learning. This is achieved through a novel module that
performs bi-directional flows prediction from a single image. In addition, with
the bi-directional flow consistency check, our method can handle occlusion and
warping artifacts in a principled manner. Our method can be trained end-to-end
based on arbitrarily sampled natural video clips, and it is able to capture
multi-modal motion uncertainty and synthesizes photo-realistic novel sequences.
Quantitative and qualitative evaluations over synthetic and real-world datasets
demonstrate the effectiveness of the proposed approach over the
state-of-the-art methods.
