This project is a modified implementation of Real-Time Intermediate Flow Estimation for Video Frame Interpolation, developed within a paper that is currently under review. More details will be added once the paper will be accepted.
You may check this pull request for supporting macOS.
git clone [email protected]:StefanoSanvitoGroup/RIFE-3D-tom
cd RIFE-3D-tom
pip3 install -r requirements.txt
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Download the pretrained HD models from here, made available by the RIFE developers
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Unzip and move the pretrained parameters to RIFE-3D-tom/train_log/
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Please note that other pretrained models are available within the RIFE and PracticalRIFE Github pages
Image Interpolation
python3 inference_imgNEW.py --in_folder '{input_folder}' --add '{num_frames}' --out_folder '{output_folder}' --out_format '{output_format}'
Where:
- input_folder is the path to the folder containing the frame sequence that you want to augment
- output_folder is the path where the new sequence of frames will be saved
- num_frames is the number of additional frames that you want to generate between every two frames (please choose 1, 3 or 7)
- output_format is the format (i.e png, tif) in which you want the frames to be saved; if not speicfied, the same format as the input will be used
Copy the pretrained model to RIFE-3D-tom/train_log_original/, the fine tuned model will be saved in RIFE-3D-tom/train_log/
The dataset used for fine tunining should have the following structure: ...
!torchrun train_NEW.py --epoch='{number_of_epochs}' --world_size=1