# Unsupervised Video Interpolation Using Cycle Consistency

## TL;DR

This paper introduces an unsupervised method for high frame rate video synthesis from low frame rate videos using cycle consistency and pseudo supervision, enabling domain adaptation without additional data.

## Contribution

It proposes a novel unsupervised cycle consistency approach combined with pseudo supervision to improve video interpolation models without requiring ground truth intermediate frames.

## Key findings

- Achieves comparable results to supervised methods using cycle consistency alone.
- Effectively adapts pre-trained models to new domains with no extra data.
- Improves PSNR on benchmark datasets, demonstrating practical effectiveness.

## Abstract

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.