AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation

This repository contains the official implementation of the following paper:

AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation
Zhen Li^*, Zuo-Liang Zhu^*, Ling-Hao Han, Qibin Hou, Chun-Le Guo, Ming-Ming Cheng
(* denotes equal contribution)
Nankai University
In CVPR 2023

[Paper] [Project Page] [Web demos] [Video]

AMT is a lightweight, fast, and accurate algorithm for Frame Interpolation. It aims to provide practical solutions for video generation from a few given frames (at least two frames).

• More examples can be found in our project page.

Web demos

Integrated into Hugging Face Spaces 🤗 using Gradio. Try out the Web Demo:

Try AMT to interpolate between two or more images at

Change Log

• Apr 20, 2023: Our code is publicly available.

Method Overview

For technical details, please refer to the method.md file, or read the full report on arXiv.

Dependencies and Installation

1. Clone Repo

   git clone https://github.com/MCG-NKU/AMT.git

2. Create Conda Environment and Install Dependencies

   conda env create -f environment.yaml
   conda activate amt

3. Download pretrained models for demos from Pretrained Models and place them to the pretrained folder

Quick Demo

Note that the selected pretrained model ([CKPT_PATH]) needs to match the config file ([CFG]).

Creating a video demo, increasing $n$ will slow down the motion in the video. (With $m$ input frames, [N_ITER] $=n$ corresponds to $2^n\times (m-1)+1$ output frames.)

python demos/demo_2x.py -c [CFG] -p [CKPT] -n [N_ITER] -i [INPUT] -o [OUT_PATH] -r [FRAME_RATE]
# e.g. [INPUT]
# -i could be a video / a regular expression / a folder contains multiple images
# -i demo.mp4 (video)/img_*.png (regular expression)/img0.png img1.png (images)/demo_input (folder)

# e.g. a simple usage
python demos/demo_2x.py -c cfgs/AMT-S.yaml -p pretrained/amt-s.pth -n 6 -i assets/quick_demo/img0.png assets/quick_demo/img1.png

• Note: Please enable --save_images for saving the output images (Save speed will be slowed down if there are too many output images)
• Input type supported: a video / a regular expression / multiple images / a folder containing input frames.
• Results are in the [OUT_PATH] (default is results/2x) folder.

Pretrained Models

Dataset	🔗 Download Links	Config file	Trained on	Arbitrary/Fixed
AMT-S	[Google Driver][Baidu Cloud]	[cfgs/AMT-S]	Vimeo90k	Fixed
AMT-L	[Google Driver][Baidu Cloud]	[cfgs/AMT-L]	Vimeo90k	Fixed
AMT-G	[Google Driver][Baidu Cloud]	[cfgs/AMT-G]	Vimeo90k	Fixed
AMT-S	[Google Driver][Baidu Cloud]	[cfgs/AMT-S_gopro]	GoPro	Arbitrary

Training and Evaluation

Please refer to develop.md to learn how to benchmark the AMT and how to train a new AMT model from scratch.

Citation

If you find our repo useful for your research, please consider citing our paper:

@inproceedings{licvpr23amt,
   title={AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation},
   author={Li, Zhen and Zhu, Zuo-Liang and Han, Ling-Hao and Hou, Qibin and Guo, Chun-Le and Cheng, Ming-Ming},
   booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
   year={2023}
}

License

This code is licensed under the Creative Commons Attribution-NonCommercial 4.0 International for non-commercial use only. Please note that any commercial use of this code requires formal permission prior to use.

Contact

For technical questions, please contact zhenli1031[AT]gmail.com and nkuzhuzl[AT]gmail.com.

For commercial licensing, please contact cmm[AT]nankai.edu.cn

Acknowledgement

We thank Jia-Wen Xiao, Zheng-Peng Duan, Rui-Qi Wu, and Xin Jin for proof reading. We thank Zhewei Huang for his suggestions.

Here are some great resources we benefit from:

• IFRNet and RIFE for data processing, benchmarking, and loss designs.
• RAFT, M2M-VFI, and GMFlow for inspirations.
• FILM for Web demo reference.

If you develop/use AMT in your projects, welcome to let us know. We will list your projects in this repository.

We also thank potential future contributors.