  Learning to synthesize high frame rate videos via interpolation requires
large quantities of high frame rate training videos, which, however, are
scarce, especially at high resolutions. Here, we propose unsupervised
techniques to synthesize high frame rate videos directly from low frame rate
videos using cycle consistency. For a triplet of consecutive frames, we
optimize models to minimize the discrepancy between the center frame and its
cycle reconstruction, obtained by interpolating back from interpolated
intermediate frames. This simple unsupervised constraint alone achieves results
comparable with supervision using the ground truth intermediate frames. We
further introduce a pseudo supervised loss term that enforces the interpolated
frames to be consistent with predictions of a pre-trained interpolation model.
The pseudo supervised loss term, used together with cycle consistency, can
effectively adapt a pre-trained model to a new target domain. With no
additional data and in a completely unsupervised fashion, our techniques
significantly improve pre-trained models on new target domains, increasing PSNR
values from 32.84dB to 33.05dB on the Slowflow and from 31.82dB to 32.53dB on
the Sintel evaluation datasets.
