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 ![Demo](./demo/I2_slomo_clipped.gif)
 
 ## 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. 
+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. 
 
 ## Dependencies
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