BiT

🚀 [CVPR2023] Blur Interpolation Transformer for Real-World Motion from Blur

by Zhihang Zhong, Mingdeng Cao, Xiang Ji, Yinqiang Zheng, and Imari Sato

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TL;DR:

Our proposed method, BiT, is a powerful transformer-based technique for arbitrary factor blur interpolation, which achieves state-of-the-art performance.

In addition, we present the first real-world dataset for benchmarking blur interpolation methods.

Preparation

Download data

Please download the synthesized Adobe240 dataset from their original repo.

Our real-world dataset RBI can be downloaded from here.

Download checkpoints

Please download the corresponding checkpoints from here.

Conda environment installation:

conda create -n BiT python=3.8
conda activate BiT
pip install torch==1.12.1+cu116 torchvision==0.13.1+cu116 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu116
pip install -r requirements.txt

Train

Train on Adobe240

Train BiT on Adobe240 (BiT+ is same as BiT but with more training epochs):

python -m torch.distributed.launch --nproc_per_node=8 train_bit.py --config ./configs/bit_adobe240.yaml

Train BiT++ on Adobe240 (P.S., need to load a pretrained BiT checkpoint. Please set the path of the checkpoint in the config file, i.e., "./configs/bit++_adobe240.yaml"):

python -m torch.distributed.launch --nproc_per_node=8 train_bit.py --config ./configs/bit++_adobe240.yaml

Train on RBI

Train BiT on RBI:

python -m torch.distributed.launch --nproc_per_node=8 train_bit.py --config ./configs/bit_rbi.yaml

Train BiT++ on RBI (P.S., need to load a pretrained BiT checkpoint. Please set the path of the checkpoint in the config file, i.e., "./configs/bit++_rbi.yaml"):

python -m torch.distributed.launch --nproc_per_node=8 train_bit.py --config ./configs/bit++_rbi.yaml

Test

Test on Adobe240

Test BiT++ on Adobe240:

CUDA_VISIBLE_DEVICES=0 ./tools/test/test_bit_adobe240.sh ./checkpoints/bit++_adobe240/cfg.yaml ./checkpoints/bit++_adobe240/latest.ckpt ./results/bit++_adobe240/ /home/zhong/Dataset/Adobe_240fps_dataset/Adobe_240fps_blur/

[Optional] Test BiT on Adobe240:

CUDA_VISIBLE_DEVICES=0 ./tools/test/test_bit_adobe240.sh ./checkpoints/bit_adobe240/cfg.yaml ./checkpoints/bit_adobe240/latest.ckpt ./results/bit_adobe240/ /home/zhong/Dataset/Adobe_240fps_dataset/Adobe_240fps_blur/

Test on RBI

Test BiT++ on RBI:

CUDA_VISIBLE_DEVICES=0 ./tools/test/test_bit_rbi.sh ./checkpoints/bit++_rbi/cfg.yaml ./checkpoints/bit++_rbi/latest.ckpt ./results/bit++_rbi/

[Optional] Test BiT on RBI:

CUDA_VISIBLE_DEVICES=0 ./tools/test/test_bit_rbi.sh ./checkpoints/bit_rbi/cfg.yaml ./checkpoints/bit_rbi/latest.ckpt ./results/bit_rbi/

Inference

Inference with BiT++:

sh ./tools/inference/inference.sh ./checkpoints/bit++_adobe240/cfg.yaml ./checkpoints/bit++_adobe240/latest.ckpt ./demo/00777.png ./demo/00785.png ./demo/00793.png ./demo/bit++_results/ 45

[Optional] Inference with BiT:

sh ./tools/inference/inference.sh ./checkpoints/bit_adobe240/cfg.yaml ./checkpoints/bit_adobe240/latest.ckpt ./demo/00777.png ./demo/00785.png ./demo/00793.png ./demo/bit_results/ 45

Citation

If you find this repository useful, please consider citing:

@inproceedings{zhong2023blur,
  title={Blur Interpolation Transformer for Real-World Motion from Blur},
  author={Zhong, Zhihang and Cao, Mingdeng and Ji, Xiang and Zheng, Yinqiang and Sato, Imari},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={5713--5723},
  year={2023}
}
@inproceedings{zhong2022animation,
  title={Animation from blur: Multi-modal blur decomposition with motion guidance},
  author={Zhong, Zhihang and Sun, Xiao and Wu, Zhirong and Zheng, Yinqiang and Lin, Stephen and Sato, Imari},
  booktitle={Computer Vision--ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23--27, 2022, Proceedings, Part XIX},
  pages={599--615},
  year={2022},
  organization={Springer}
}

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BiT