Real-Time Intermediate Flow Estimation for Video Frame Interpolation

Real-time video <PRE_TAG>frame interpolation</POST_TAG> (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 <PRE_TAG>frame interpolation</POST_TAG> with the <PRE_TAG>temporal encoding</POST_TAG> 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 <PRE_TAG>temporal encoding</POST_TAG>. The code is available at https://github.com/megvii-research/ECCV2022-RIFE.