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FeatureFlow

★ 151 Open GitHub ↗

A state-of-the-art Video Frame Interpolation Method using feature flows blending. （CVPR 2020)

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# FeatureFlow

[Paper](https://github.com/CM-BF/FeatureFlow/blob/master/paper/FeatureFlow.pdf) | [Supp](https://github.com/CM-BF/FeatureFlow/blob/master/paper/Supp.pdf)

A state-of-the-art Video Frame Interpolation Method using deep semantic flows blending.

FeatureFlow: Robust Video Interpolation via Structure-to-texture Generation (IEEE Conference on Computer Vision and Pattern Recognition 2020)

## To Do List
- [x] Preprint
- [x] Training code

## Table of Contents

1. [Requirements](#requirements)
1. [Demos](#video-demos)
1. [Installation](#installation)
1. [Pre-trained Model](#pre-trained-model)
1. [Download Results](#download-results)
1. [Evaluation](#evaluation)
1. [Test your video](#test-your-video)
1. [Training](#training)
1. [Citation](#citation)

## Requirements

* Ubuntu
* PyTorch (>=1.1) 
* Cuda (>=10.0) & Cudnn (>=7.0)
* mmdet 1.0rc (from https://github.com/open-mmlab/mmdetection.git)
* visdom (not necessary)
* NVIDIA GPU

Ps: `requirements.txt` is provided, but do not use it directly. It is just for reference because it contains another project's dependencies.

## Video demos

Click the picture to Download one of them or click [Here(Google)](https://drive.google.com/open?id=1QUYoplBNjaWXJZPO90NiwQwqQz7yF7TX) or [Here(Baidu)](https://pan.baidu.com/s/1J9seoqgC2p9zZ7pegMlH1A)(key: oav2) to download **360p demos**.

**360p demos**(including comparisons):

[<img width="320" height="180" src="https://github.com/CM-BF/FeatureFlow/blob/master/data/figures/youtube.png"/>](https://github.com/CM-BF/storage/tree/master/videos/youtube.mp4 "video1")
[<img width="320" height="180" src="https://github.com/CM-BF/FeatureFlow/blob/master/data/figures/check_all.png"/>](https://github.com/CM-BF/storage/tree/master/videos/check_all.mp4 "video2")
[<img width="320" height="180" src="https://github.com/CM-BF/FeatureFlow/blob/master/data/figures/tianqi_all.png"/>](https://github.com/CM-BF/storage/tree/master/videos/tianqi_all.mp4 "video3")
[<img width="320" height="180" src="https://github.com/CM-BF/FeatureFlow/blob/master/data/figures/video.png"/>](https://github.com/CM-BF/storage/tree/master/videos/video.mp4 "video4")
[<img width="320" height="180" src="https://github.com/CM-BF/FeatureFlow/blob/master/data/figures/shana.png"/>](https://github.com/CM-BF/storage/tree/master/videos/shana.mp4 "video5")

**720p demos**:

[<img width="320" height="180" src="https://github.com/CM-BF/FeatureFlow/blob/master/data/figures/SYA_1.png"/>](https://github.com/CM-BF/storage/tree/master/videos/SYA_1.mp4 "video6")
[<img width="320" height="180" src="https://github.com/CM-BF/FeatureFlow/blob/master/data/figures/SYA_2.png"/>](https://github.com/CM-BF/storage/tree/master/videos/SYA_2.mp4 "video7")

## Installation
* clone this repo
* git clone https://github.com/open-mmlab/mmdetection.git
* install mmdetection: please follow the guidence in its github
```bash
$ cd mmdetection
$ pip install -r requirements/build.txt
$ pip install "git+https://github.com/cocodataset/cocoapi.git#subdirectory=PythonAPI"
$ pip install -v -e .  # or "python setup.py develop"
$ pip list | grep mmdet
```
* Download [test set](http://data.csail.mit.edu/tofu/testset/vimeo_interp_test.zip)
```bash
$ unzip vimeo_interp_test.zip
$ cd vimeo_interp_test
$ mkdir sequences
$ cp target/* sequences/ -r
$ cp input/* sequences/ -r
```
* Download BDCN's pre-trained model:bdcn_pretrained_on_bsds500.pth to ./model/bdcn/final-model/

Ps: For your convenience, you can only download the bdcn_pretrained_on_bsds500.pth: [Google Drive](https://drive.google.com/file/d/1Zot0P8pSBawnehTE32T-8J0rjYq6CSAT/view?usp=sharing) or all of the pre-trained bdcn models its authors provided: [Google Drive](https://drive.google.com/file/d/1CmDMypSlLM6EAvOt5yjwUQ7O5w-xCm1n/view). For a Baidu Cloud link, you can resort to BDCN's GitHub repository.

```
$ pip install scikit-image visdom tqdm prefetch-generator
```

## Pre-trained Model

[Google Drive](https://drive.google.com/open?id=1S8C0chFV6Bip6W9lJdZkog0T3xiNxbEx)

[Baidu Cloud](https://pan.baidu.com/s/1LxVw-89f3GX5r0mZ6wmsJw): ae4x

Place FeFlow.ckpt to ./checkpoints/.

## Download Results

[Google Drive](https://drive.google.com/open?id=1OtrExUiyIBJe0D6_ZwDfztqJBqji4lmt)

[Baidu Cloud](https://pan.baidu.com/s/1BaJJ82nSKagly6XZ8KNtAw): pc0k

## Evaluation
```bash
$ CUDA_VISIBLE_DEVICES=0 python eval_Vimeo90K.py --checkpoint ./checkpoints/FeFlow.ckpt --dataset_root ~/datasets/videos/vimeo_interp_test --visdom_env test --vimeo90k --imgpath ./results/
```

## Test your video
```bash
$ CUDA_VISIBLE_DEVICES=0 python sequence_run.py --checkpoint checkpoints/FeFlow.ckpt --video_path ./yourvideo.mp4 --t_interp 4 --slow_motion
```
`--t_interp` sets frame multiples, only power of 2(2,4,8...) are supported. Use flag `--slow_motion` to slow down the video which maintains the original fps.

The output video will be saved as output.mp4 in your working diractory.

## Training

Training Code **train.py** is available now. I can't run it for comfirmation now because I've left the Lab, but I'm sure it will work with right argument settings.

```bash
$ CUDA_VISIBLE_DEVICES=0,1 python train.py <arguments>
```

* Please read the **arguments' help** carefully to fully control the **two-step training**.
* Pay attention to the `--GEN_DE` which is the flag to set the model to Stage-I or Stage-II.
* 2 GPUs is necessary for training or the small batch\_size will cause training process crash.
* Deformable CNN is not stable enough so that you may face training crash sometimes(I didn't fix the random seed), but it can be detected soon after the beginning of running by visualizing results using Visdom. 
* Visdom visualization codes[line 75, 201-216 and 338-353] are included which is good for viewing training process and checking crash.

## Citation
```
@InProceedings{Gui_2020_CVPR,
author = {Gui, Shurui and Wang, Chaoyue and Chen, Qihua and Tao, Dacheng},
title = {FeatureFlow: Robust Video Interpolation via Structure-to-Texture Generation},
booktitle = {The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2020}
}
```

## Contact
[Shurui Gui](mailto:citrinegui@gmail.com); [Chaoyue Wang](mailto:chaoyue.wang@sydney.edu.au)

## License
See [MIT License](https://github.com/CM-BF/FeatureFlow/blob/master/LICENSE)

FeatureFlow

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