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Real-Time Intermediate Flow Estimation for Video Frame Interpolation


This project is the implement of Real-Time Intermediate Flow Estimation for Video Frame Interpolation. Currently, our model can run 30+FPS for 2X 720p interpolation on a 2080Ti GPU. It supports arbitrary-timestep interpolation between a pair of images.

2022.7.4 - Our paper is accepted by ECCV2022 🎉. Thanks to all relevant authors, contributors and users!

From 2020 to 2022, we submitted RIFE for five submissions(rejected by CVPR21 ICCV21 AAAI22 CVPR22). Thanks to all anonymous reviewers, your suggestions have helped to significantly improve the paper!

ECCV Poster | ECCV 5-min presentation | 论文中文介绍 | rebuttal (224->222)

YouTube | BiliBili | Colab | Tutorial

Pinned Software: RIFE-App | FlowFrames | SVFI (中文)

16X interpolation results from two input images:

Demo Demo


Flowframes | SVFI(中文) | Waifu2x-Extension-GUI | Autodesk Flame | SVP | MPV_lazy | enhancr

RIFE-App(Paid) | Steam-VFI(Paid)

We are not responsible for and participating in the development of above software. According to the open source license, we respect the commercial behavior of other developers.

VapourSynth-RIFE | RIFE-ncnn-vulkan | VapourSynth-RIFE-ncnn-Vulkan

If you are a developer, welcome to follow Practical-RIFE, which aims to make RIFE more practical for users by adding various features and design new models with faster speed.

You may check this pull request for supporting macOS.

CLI Usage


git clone
cd ECCV2022-RIFE
pip3 install -r requirements.txt


Video Frame Interpolation

You can use our demo video or your own video.

python3 --exp=1 --video=video.mp4 

(generate video_2X_xxfps.mp4)

python3 --exp=2 --video=video.mp4

(for 4X interpolation)

python3 --exp=1 --video=video.mp4 --scale=0.5

(If your video has very high resolution such as 4K, we recommend set --scale=0.5 (default 1.0). If you generate disordered pattern on your videos, try set --scale=2.0. This parameter control the process resolution for optical flow model.)

python3 --exp=2 --img=input/

(to read video from pngs, like input/0.png ... input/612.png, ensure that the png names are numbers)

python3 --exp=2 --video=video.mp4 --fps=60

(add slomo effect, the audio will be removed)

python3 --video=video.mp4 --montage --png

(if you want to montage the origin video and save the png format output)

Optical Flow Estimation

You may refer to #278.

Image Interpolation

python3 --img img0.png img1.png --exp=4

(2^4=16X interpolation results) After that, you can use pngs to generate mp4:

ffmpeg -r 10 -f image2 -i output/img%d.png -s 448x256 -c:v libx264 -pix_fmt yuv420p output/slomo.mp4 -q:v 0 -q:a 0

You can also use pngs to generate gif:

ffmpeg -r 10 -f image2 -i output/img%d.png -s 448x256 -vf "split[s0][s1];[s0]palettegen=stats_mode=single[p];[s1][p]paletteuse=new=1" output/slomo.gif

Run in docker

Place the pre-trained models in train_log/\*.pkl (as above)

Building the container:

docker build -t rife -f docker/Dockerfile .

Running the container:

docker run --rm -it -v $PWD:/host rife:latest inference_video --exp=1 --video=untitled.mp4 --output=untitled_rife.mp4
docker run --rm -it -v $PWD:/host rife:latest inference_img --img img0.png img1.png --exp=4

Using gpu acceleration (requires proper gpu drivers for docker):

docker run --rm -it --gpus all -v /dev/dri:/dev/dri -v $PWD:/host rife:latest inference_video --exp=1 --video=untitled.mp4 --output=untitled_rife.mp4


Download RIFE model or RIFE_m model reported by our paper.

UCF101: Download UCF101 dataset at ./UCF101/ucf101_interp_ours/

Vimeo90K: Download Vimeo90K dataset at ./vimeo_interp_test

MiddleBury: Download MiddleBury OTHER dataset at ./other-data and ./other-gt-interp

HD: Download HD dataset at ./HD_dataset. We also provide a google drive download link.

python3 benchmark/
# "PSNR: 35.282 SSIM: 0.9688"
python3 benchmark/
# "PSNR: 35.615 SSIM: 0.9779"
python3 benchmark/
# "IE: 1.956"
python3 benchmark/
# "PSNR: 32.14"

# RIFE_m
python3 benchmark/
# "PSNR: 22.96(544*1280), 31.87(720p), 34.25(1080p)"

Training and Reproduction

Download Vimeo90K dataset.

We use 16 CPUs, 4 GPUs and 20G memory for training:

python3 -m torch.distributed.launch --nproc_per_node=4 --world_size=4

Revision History

2021.3.18 arXiv: Modify the main experimental data, especially the runtime related issues.

2021.8.12 arXiv: Remove pre-trained model dependency and propose privileged distillation scheme for frame interpolation. Remove census loss supervision.

2021.11.17 arXiv: Support arbitrary-time frame interpolation, aka RIFEm and add more experiments.


We sincerely recommend some related papers:

CVPR22 - Optimizing Video Prediction via Video Frame Interpolation

CVPR22 - Video Frame Interpolation with Transformer

CVPR22 - IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation

CVPR23 - A Dynamic Multi-Scale Voxel Flow Network for Video Prediction

CVPR23 - Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation


If you think this project is helpful, please feel free to leave a star or cite our paper:

  title={Real-Time Intermediate Flow Estimation for Video Frame Interpolation},
  author={Huang, Zhewei and Zhang, Tianyuan and Heng, Wen and Shi, Boxin and Zhou, Shuchang},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},


Optical Flow: ARFlow pytorch-liteflownet RAFT pytorch-PWCNet

Video Interpolation: DVF TOflow SepConv DAIN CAIN MEMC-Net SoftSplat BMBC EDSC EQVI