  This paper considers the challenging task of long-term video interpolation.
Unlike most existing methods that only generate few intermediate frames between
existing adjacent ones, we attempt to speculate or imagine the procedure of an
episode and further generate multiple frames between two non-consecutive frames
in videos. In this paper, we present a novel deep architecture called
bidirectional predictive network (BiPN) that predicts intermediate frames from
two opposite directions. The bidirectional architecture allows the model to
learn scene transformation with time as well as generate longer video
sequences. Besides, our model can be extended to predict multiple possible
procedures by sampling different noise vectors. A joint loss composed of clues
in image and feature spaces and adversarial loss is designed to train our
model. We demonstrate the advantages of BiPN on two benchmarks Moving 2D Shapes
and UCF101 and report competitive results to recent approaches.
