We propose RIFE, a Real-time Intermediate Flow Estimation algorithm for Video Frame Interpolation (VFI). Many recent flow-based VFI methods first estimate the bi-directional optical flows, then scale and reverse them to approximate intermediate flows, leading to artifacts on motion boundaries. RIFE uses a neural network named IFNet that can directly estimate the intermediate flows from coarse-to-fine with much better speed. We design a privileged distillation scheme for training intermediate flow model, which leads to a large performance improvement. RIFE does not rely on pre-trained optical flow models and can support arbitrary-timestep frame interpolation. Experiments demonstrate that RIFE achieves state-of-the-art performance on several public benchmarks. \url{https://github.com/hzwer/arXiv2020-RIFE}.

翻译：我们建议使用实时中流估算算法(RIFE ), 用于视频框架内插(VFI ) 。 许多最近的基于流动的VFI方法首先估计双向光学流,然后将其缩放并转换为近似中间流,从而在运动边界上产生文物。 RIFE 使用一个名为 IFNet 的神经网络,可以更快地直接估计从粗向松流流的中间流。我们为培训中间流模式设计了一个特别精良的蒸馏计划,这会导致很大的性能改进。 RIFE 不依赖预先培训的光学流模型,可以支持任意的分步框架内插。 实验表明,RIFE在几个公共基准上取得了最新业绩。\url{https://github.com/hzwer/arXiv20-RIFE}。