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.

翻译：视频帧插值（VFI）是一项具有挑战性的任务，旨在生成视频中两个连续帧之间的中间帧。现有的基于学习的VFI方法已取得显著成功，但由于训练数据集中运动分布的局限性，其泛化能力仍受到限制。本文提出了一种新颖的基于优化的VFI方法，能够在测试时自适应未见过的运动模式。该方法基于循环一致性自适应策略，利用视频帧间的运动特征。我们同时引入了一种轻量级适配器，可嵌入现有预训练VFI模型的运动估计模块中，以提升自适应效率。在多个基准数据集上的大量实验表明，我们的方法能够增强双帧VFI模型的性能，并超越现有最先进方法，甚至包括那些使用了额外输入的方法。