  Video frame extrapolation is a task to predict future frames when the past
frames are given. Unlike previous studies that usually have been focused on the
design of modules or construction of networks, we propose a novel
Extrapolative-Interpolative Cycle (EIC) loss using pre-trained frame
interpolation module to improve extrapolation performance. Cycle-consistency
loss has been used for stable prediction between two function spaces in many
visual tasks. We formulate this cycle-consistency using two mapping functions;
frame extrapolation and interpolation. Since it is easier to predict
intermediate frames than to predict future frames in terms of the object
occlusion and motion uncertainty, interpolation module can give guidance signal
effectively for training the extrapolation function. EIC loss can be applied to
any existing extrapolation algorithms and guarantee consistent prediction in
the short future as well as long future frames. Experimental results show that
simply adding EIC loss to the existing baseline increases extrapolation
performance on both UCF101 and KITTI datasets.
