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Abstract: We propose RIFE, a Real-time Intermediate Flow Estimation algorithm, with applications to Video Frame Interpolation (VFI). Most existing flow estimation methods first estimate the bi-directional optical flows, and then linearly combine them to approximate intermediate flows, leading to artifacts on motion boundaries. We design a neural network named IFNet, that can directly estimate the intermediate flows from images. When interpolating videos, we can warp the frames according to the estimated intermediate flows and employ a fusion process to compute final results. Based on our proposed leakage distillation loss, RIFE can be trained in an end-to-end fashion. Experiments demonstrate that our method is significantly faster than existing VFI methods and can achieve state-of-the-art performance on public benchmarks.

Image demos

16x interpolation results using only two images

Demo Demo

Video demos

We are consistenly working on improving the models generalization to videos with various appearance.

24 FPS -> 96 FPS

How to cite

  title={RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation},
  author={Huang, Zhewei and Zhang, Tianyuan and Heng, Wen and Shi, Boxin and Zhou, Shuchang},
  journal={arXiv preprint arXiv:2011.06294},

Want to generate your own videos?

[colab]: Try our colab notebook, upload your own videos or images, and run! Or your can visit our github repo

[Apps]: There is a new windows app which intergrated our algorithm, you can download it for free: FlowFrames

If you have any interesting samples you’d like to share, please email Tianyuan Zhang @


You can reach us at: Zhewei Huang @, Tianyuan zhang @