Video frame interpolation (VFI) is a challenging task that aims to generate
intermediate frames between two consecutive frames in a video. Existing
learning-based VFI methods have achieved great success, but they still suffer
from limited generalization ability due to the limited motion distribution of
training datasets. In this paper, we propose a novel optimization-based VFI
method that can adapt to unseen motions at test time. Our method is based on a
cycle-consistency adaptation strategy that leverages the motion characteristics
among video frames. We also introduce a lightweight adapter that can be
inserted into the motion estimation module of existing pre-trained VFI models
to improve the efficiency of adaptation. Extensive experiments on various
benchmarks demonstrate that our method can boost the performance of two-frame
VFI models, outperforming the existing state-of-the-art methods, even those
that use extra input.

本文提出了一种基于优化的视频帧插值方法，该方法利用循环一致性自适应策略，结合现有预训练的插值模型中的运动估计模块，以适应测试时的未见过的运动，实现提升性能。经过各种基准测试的详尽实验表明，该方法可以提升两帧插值模型的性能，优于现有最先进的方法，即使它们使用额外的输入。