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A Unified Pyramid Recurrent Network for Video Frame Interpolation

This project is the official implementation of our CVPR 2023 paper, A Unified Pyramid Recurrent Network for Video Frame Interpolation.

Introduction

We present UPR-Net, a novel Unified Pyramid Recurrent Network for frame interpolation. Cast in a flexible pyramid framework, UPR-Net exploits lightweight recurrent modules for both bi-directional flow estimation and intermediate frame synthesis. At each pyramid level, it leverages estimated bi-directional flow to generate forward-warped representations for frame synthesis; across pyramid levels, it enables iterative refinement for both optical flow and intermediate frame. In particular, we show that our iterative synthesis strategy can significantly improve the robustness of frame interpolation on large motion cases. Despite being extremely lightweight (1.7M parameters), our base version of UPR-Net achieves excellent performance on a large range of benchmarks.

Python and Cuda environment

This code has been tested with PyTorch 1.6 and Cuda 10.2. It should also be compatible with higher versions of PyTorch and Cuda. Run the following command to initialize the environment:

conda create --name uprnet python=3.7
conda activate uprnet
conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch
pip3 install cupy_cuda102==9.4.0
pip3 install -r requirements.txt

In particular, CuPy package is required for running the forward warping operation (refer to softmax-splatting for details). If your Cuda version is lower than 10.2 (not lower than 9.2), we suggest to replace cudatoolkit=10.2 in above command with cudatoolkit=9.2, and replace cupy_cuda102==9.4.0 with cupy_cuda92==9.6.0.

Play with demo

We place trained model weights in checkpoints, and provide a script to test our frame interpolation model. Given two consecutive input frames, and the desired time step, run the following command, then you will obtain estimated bi-directional flow and interpolated frame in the ./demo/output directory.

python3 -m demo.interp_imgs \
--frame0 demo/images/beanbags0.png \
--frame1 demo/images/beanbags1.png \
--time_period 0.5

Here the time_period (float number in 0~1) indicates the time step of the intermediate frame you want to interpolate.

Training on Vimeo90K

By default, our model is trained on Vimeo90K. If you want to train our model, please download Vimeo90K.

Default training configuration

You can run the following command to train the base version of our UPR-Net:

CUDA_VISIBLE_DEVICES=0,1,2,3 python3 -m torch.distributed.launch \
    --nproc_per_node=4 --master_port=10000 -m tools.train \
        --world_size=4 \
        --data_root /path/to/vimeo_triplet \
        --train_log_root /path/to/train_log \
        --exp_name upr-base \
        --batch_size 8 \
        --nr_data_worker 2

Please (must) assign data_root with the path of vimeo_triplet for training, and (optionally) assign train_log_root with the path to save logs (trained weights and tensorboard logs). We do not recommend saving log files under dir of this codebase. If train_log_root is not explicitly assigned, all logs will be saved in ../upr-train-log by default. We also intentionally soft link train_log_root to ./train-log for convenience.

Some tips for training

• If you want to train large or LARGE versions of our UPR-Net, please assign the argument model_size as large or LARGE.

• If you have suspended the training, and want to restart the training from previous checkpoint, please assign the argument resume as True in the training command.

• By default, we set total batch_size as 32, and use 4 GPUs for distributed training, with each GPU processing 8 samples in a batch (batch_size is set as 8 in our training command). Therefore, if you use 2 GPUs for training, please set batch_size as 16 in the training command.

• You can view the training curve, interpolation, and optical flow using TensorBoard, by running command like tensorboard --logdir=./train-log/upr-base/tensorboard.

Benchmarking

Trained model weights

We have placed our trained model weights in ./checkpoints. The weights of base/large/LARGE versions of our UPR-Net are named as upr.pkl, upr_large.pkl, upr_llarge.pkl, respectively.

Benchmark datasets

We evaluate our UPR-Net series on Vimeo90K, UCF101, SNU-FILM, and 4K1000FPS.

If you want to train and benchmark our model, please download Vimeo90K, UCF101, SNU-FILM, 4K1000FPS.

Benchmarking scripts

We provide scripts to test frame interpolation accuracy on Vimeo90K, UCF101, SNU-FILM, and 4K1000FPS. You should configure the path to benchmark datasets when running these scripts.

python3 -m tools.benchmark_vimeo90k --data_root /path/to/vimeo_triplet/
python3 -m tools.benchmark_ucf101 --data_root /path/to/ucf101/
python3 -m tools.benchmark_snufilm --data_root /path/to/SNU-FILM/
python3 -m tools.benchmark_8x_4k1000fps --test_data_path /path/to/4k1000fps/test

By default, we test the base version of UPR-Net. To test the large/LARGE versions, please change corresponding arguments (model_size and model_file) in benchmarking scripts.

Additionally, run the following command can test our runtime.

python -m tools.runtime

Our benchmarking results

Our benchmarking results on UCF101, Vimeo90K, SNU-FILM are shown in below table. You can verify our results by running our benchmarking scripts. Runtime is measured with a single 2080TI GPU for interpolating two 640x480 frames.

Our benchmarking results on 4K1000FPS are shown in below table.

Acknowledgement

We borrow some codes from RIFE, softmax-splatting, and EBME. We thank the authors for their excellent work. When using our code, please also pay attention to the licenses of RIFE, softmax-splatting, and EBME.

Citation

@inproceedings{jin2023unified,
  title={A Unified Pyramid Recurrent Network for Video Frame Interpolation},
  author={Jin, Xin and Wu, Longhai and Chen, Jie and Chen, Youxin and Koo,
  Jayoon and Hahm, Cheul-hee},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern
  recognition},
  year={2023}
}