From 10e7e19b580673d1c80f8842e2407f8a40907a52 Mon Sep 17 00:00:00 2001 From: hzwer <598460606@163.com> Date: Tue, 17 Nov 2020 16:17:03 +0800 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1b8ea6b..3874416 100644 --- a/README.md +++ b/README.md @@ -13,7 +13,7 @@ Our model can run 30+FPS for 2X 720p interpolation on a 2080Ti GPU. Currently ou ![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 for Video Frame Interpolation (VFI). Most existing methods first estimate the bi-directional optical flows and then linearly combine 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 images. With the more precise flows and our simplified fusion process, RIFE can improve interpolation quality and have much better speed. 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 ```