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HWFI: Hybrid Warping Fusion for Video Frame Interpolation

HWFI is a method for video frame interpolation. It can synthesize intermediate frame at any time between two video frames

HWFI 是一个通用视频插帧方法。可以预测两帧中间任意时刻的中间帧。

Paper | Demo Video

Yu Li, Ye Zhu, Ruoteng Li, Xintao Wang, Yue Luo, Ying Shan

8X interpolation results using only two images

Usage

Installation

Download the scripts and pre-trained model in weights folder. Then install the required packages:

  • pytorch==1.2.0
  • cupy==7.3.0
  • kornia==0.1.4
  • torchvision
  • opencv-python
  • tqdm
  • scikit-image
  • imageio
  • PIL

A simply way is to run following script:

$  conda create -n HWFI python=3.6
$  conda activate HWFI
$  pip install -r requirement.txt

Run

Video Frame Interpolation

You can use your own video to process.

$ python slomo.py --model HWFI --gpu_ids 0 --resume weights/HWFI.pt.tar --sf 2 --video video_path --dataset VideoTest --extract_dir tmp_extract_video --folder_name 2X --output ./output_video

(generate 2X video)

$  python slomo.py --model HWFI --gpu_ids 0 --resume weights/HWFI.pt.tar --sf 4 --video video_path --dataset VideoTest --extract_dir tmp_extract_video --folder_name x4 --output ./output_video

(for 4X interpolation)

Image Interpolation

$ python test.py --model HWFI --gpu_ids 0 --resume weights/HWFI.pt.tar

This is an image interpolation example, input frames are in the folder examples.

Evaluation

Vimeo90K: Download Vimeo90K dataset at ./vimeo_triple

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

UCF101: Download UCF101 test dataset at ./ucf101

$ python eval.py --model HWFI --dataset VIMEO --val_file vimeo_triple/ --gpu_ids 0 --resume weights/HWFI.pt.tar --name vimeo
$ python eval.py --model HWFI --dataset Middlebury --val_file middlebury/ --gpu_ids 0 --resume weights/HWFI.pt.tar --name middlebury
$ python eval.py --model HWFI --dataset UCF101 --val_file ucf101/ --gpu_ids 0 --resume weights/HWFI.pt.tar --name ucf

Training

Vimeo90K: Download Vimeo90K dataset at ./vimeo_triple

$ python train.py --model HWFI --dataset VIMEO --batch_size 8 --gpu_ids 0 --train_file vimeo_triplet/ --val_file vimeo_triple/ --name exp0 --epoch 100 --optimizer Adamax --lr 0.001 --lr_scheduler MultiStepLR --lr_milestones 40 80

We also support multi-gpus training, such as 4 gpus:

$ python -m torch.distributed.launch --nproc_per_node=4 train.py --model HWFI --dataset VIMEO --batch_size 8 --gpu_ids 0 1 2 3 --train_file vimeo_triplet/ --val_file vimeo_triple/ --name exp0 --epoch 100 --optimizer Adamax --lr 0.001 --lr_scheduler MultiStepLR --lr_milestones 40 80

Pre-trained weights

  • HWFI.pt.tar (This model is trained on Vimeo90K and is used in our paper)
  • HWFI_general.pt.tar (This model is newly trained for general video frame interpolation)

Reference

pytorch-PWCNet SoftSplat