ECCV2022-RIFE/README.md

# RIFE - Real Time Video Interpolation
## [arXiv](https://arxiv.org/abs/2011.06294) | [YouTube](https://www.youtube.com/results?search_query=rife+interpolation&sp=CAM%253D) | [BiliBili](https://search.bilibili.com/all?keyword=SVFI&order=stow&duration=0&tids_1=0) | [Colab](https://colab.research.google.com/github/hzwer/arXiv2020-RIFE/blob/main/Colab_demo.ipynb) | [Tutorial](https://www.youtube.com/watch?v=gf_on-dbwyU&feature=emb_title)

16X interpolation results from two input images: 

![Demo](./demo/I2_slomo_clipped.gif)
![Demo](./demo/D2_slomo_clipped.gif)

## Introduction
This project is the implement of [RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation](https://arxiv.org/abs/2011.06294). 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. 

## Software
[Flowframes](https://nmkd.itch.io/flowframes) | [Squirrel-RIFE(中文)](https://github.com/YiWeiHuang-stack/Squirrel-Video-Frame-Interpolation) | [Waifu2x-Extension-GUI](https://github.com/AaronFeng753/Waifu2x-Extension-GUI) | [Autodesk Flame](https://vimeo.com/505942142) | [SVP](https://www.svp-team.com/wiki/RIFE_AI_interpolation) 

[RIFE-App(Paid)](https://grisk.itch.io/rife-app) | [Steam-VFI(Paid)](https://store.steampowered.com/app/1692080/SVFI/) 

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](https://github.com/HolyWu/vs-rife) | [RIFE-ncnn-vulkan](https://github.com/nihui/rife-ncnn-vulkan) | [VapourSynth-RIFE-ncnn-Vulkan](https://github.com/HomeOfVapourSynthEvolution/VapourSynth-RIFE-ncnn-Vulkan)

If you are a developer, welcome to follow [Practical-RIFE](https://github.com/hzwer/Practical-RIFE), which aims to make RIFE more practical for users by adding various features and design new models with faster speed.
## CLI Usage

### Installation

```
git clone git@github.com:hzwer/arXiv2020-RIFE.git
cd arXiv2020-RIFE
pip3 install -r requirements.txt
```

* Download the pretrained **HD** models from [here](https://drive.google.com/file/d/1APIzVeI-4ZZCEuIRE1m6WYfSCaOsi_7_/view?usp=sharing). (百度网盘链接:https://pan.baidu.com/share/init?surl=u6Q7-i4Hu4Vx9_5BJibPPA 密码:hfk3，把压缩包解开后放在 train_log/\*)

* Unzip and move the pretrained parameters to train_log/\*

* This model is not reported by our paper, for our paper model please refer to [evaluation](https://github.com/hzwer/arXiv2020-RIFE#evaluation).

### Run

**Video Frame Interpolation**

You can use our [demo video](https://drive.google.com/file/d/1i3xlKb7ax7Y70khcTcuePi6E7crO_dFc/view?usp=sharing) or your own video. 
```
python3 inference_video.py --exp=1 --video=video.mp4 
```
(generate video_2X_xxfps.mp4)
```
python3 inference_video.py --exp=2 --video=video.mp4
```
(for 4X interpolation)
```
python3 inference_video.py --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 inference_video.py --exp=2 --img=input/
```
(to read video from pngs, like input/0.png ... input/612.png, ensure that the png names are numbers)
```
python3 inference_video.py --exp=2 --video=video.mp4 --fps=60
```
(add slomo effect, the audio will be removed)
```
python3 inference_video.py --video=video.mp4 --montage --png
```
(if you want to montage the origin video and save the png format output)

**Image Interpolation**

```
python3 inference_img.py --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
```

## Evaluation
Download [RIFE model](https://drive.google.com/file/d/1h42aGYPNJn2q8j_GVkS_yDu__G_UZ2GX/view?usp=sharing) or [RIFE_m model](https://drive.google.com/file/d/147XVsDXBfJPlyct2jfo9kpbL944mNeZr/view?usp=sharing) reported by our paper.

**UCF101**: Download [UCF101 dataset](https://liuziwei7.github.io/projects/VoxelFlow) at ./UCF101/ucf101_interp_ours/

**Vimeo90K**: Download [Vimeo90K dataset](http://toflow.csail.mit.edu/) at ./vimeo_interp_test

**MiddleBury**: Download [MiddleBury OTHER dataset](https://vision.middlebury.edu/flow/data/) at ./other-data and ./other-gt-interp

**HD**: Download [HD dataset](https://github.com/baowenbo/MEMC-Net) at ./HD_dataset. We also provide a [google drive download link](https://drive.google.com/file/d/1iHaLoR2g1-FLgr9MEv51NH_KQYMYz-FA/view?usp=sharing).
```
# RIFE
python3 benchmark/UCF101.py
# "PSNR: 35.282 SSIM: 0.9688"
python3 benchmark/Vimeo90K.py
# "PSNR: 35.615 SSIM: 0.9779"
python3 benchmark/MiddleBury_Other.py
# "IE: 1.956"
python3 benchmark/HD.py
# "PSNR: 32.14"

# RIFE_m
python3 benchmark/HD_multi_4X.py
# "PSNR: 22.96(544*1280), 31.87(720p), 34.25(1080p)"
```

## Training and Reproduction
Download [Vimeo90K dataset](http://toflow.csail.mit.edu/).

We use 16 CPUs, 4 GPUs and 20G memory for training: 
```
python3 -m torch.distributed.launch --nproc_per_node=4 train.py --world_size=4
```

## Citation

```
@article{huang2020rife,
  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},
  year={2020}
}
```

## Reference

Optical Flow:
[ARFlow](https://github.com/lliuz/ARFlow)  [pytorch-liteflownet](https://github.com/sniklaus/pytorch-liteflownet)  [RAFT](https://github.com/princeton-vl/RAFT)  [pytorch-PWCNet](https://github.com/sniklaus/pytorch-pwc)

Video Interpolation: 
[DVF](https://github.com/lxx1991/pytorch-voxel-flow)  [TOflow](https://github.com/Coldog2333/pytoflow)  [SepConv](https://github.com/sniklaus/sepconv-slomo)  [DAIN](https://github.com/baowenbo/DAIN)  [CAIN](https://github.com/myungsub/CAIN)  [MEMC-Net](https://github.com/baowenbo/MEMC-Net)   [SoftSplat](https://github.com/sniklaus/softmax-splatting)  [BMBC](https://github.com/JunHeum/BMBC)  [EDSC](https://github.com/Xianhang/EDSC-pytorch)  [EQVI](https://github.com/lyh-18/EQVI)

## Sponsor
Many thanks to [Grisk](https://grisk.itch.io/rife-app).

感谢支持 Paypal Sponsor: https://www.paypal.com/paypalme/hzwer

<img width="160" alt="image" src="https://cdn.luogu.com.cn/upload/image_hosting/5h3609p1.png"><img width="160" alt="image" src="https://cdn.luogu.com.cn/upload/image_hosting/yi3kcwnw.png">