Real-time video frame interpolation (VFI) is very useful in video processing,
media players, and display devices. We propose RIFE, a Real-time Intermediate
Flow Estimation algorithm for VFI. To realize a high-quality flow-based VFI
method, RIFE uses a neural network named IFNet that can estimate the
intermediate flows end-to-end with much faster speed. A privileged distillation
scheme is designed for stable IFNet training and improve the overall
performance. RIFE does not rely on pre-trained optical flow models and can
support arbitrary-timestep frame interpolation with the temporal encoding
input. Experiments demonstrate that RIFE achieves state-of-the-art performance
on several public benchmarks. Compared with the popular SuperSlomo and DAIN
methods, RIFE is 4--27 times faster and produces better results. Furthermore,
RIFE can be extended to wider applications thanks to temporal encoding. The
code is available at this https URL

RIFE 是一种基于神经网络 IFNet 的实时中间流估计算法，可在视频处理、媒体播放器和显示设备中实现高质量流媒体帧内插值，并且不需要预训练的光流模型。该算法在多个公共基准测试中取得了最先进的性能，与现有的 SuperSlomo 和 DAIN 方法相比，RIFE 速度更快，结果更好。