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Tensorflow 2 implementation of our high quality frame interpolation neural network. We present a unified single-network approach that doesn't use additional pre-trained networks, like optical flow or depth, and yet achieve state-of-the-art results. We use a multi-scale feature extractor that shares the same convolution weights across the scales. Our model is trainable from frame triplets alone.
FILM: Frame Interpolation for Large Motion
Fitsum Reda, Janne Kontkanen, Eric Tabellion, Deqing Sun, Caroline Pantofaru, Brian Curless
Google Research
Technical Report 2022.
FILM transforms near-duplicate photos into a slow motion footage that look like it is shot with a video camera.
Try the interpolation model with the replicate web demo at
- Get Frame Interpolation source codes
> git clone https://github.com/google-research/frame-interpolation frame_interpolation
- Optionally, pull the recommended Docker base image
> docker pull gcr.io/deeplearning-platform-release/tf2-gpu.2-6:latest
- Install dependencies
> pip install -r frame_interpolation/requirements.txt
> apt-get install ffmpeg
- Create a directory where you can keep large files. Ideally, not in this directory.
> mkdir <pretrained_models>
- Download pre-trained TF2 Saved Models from
google drive
and put into
<pretrained_models>.
The downloaded folder should have the following structure:
pretrained_models/
├── film_net/
│ ├── L1/
│ ├── VGG/
│ ├── Style/
├── vgg/
│ ├── imagenet-vgg-verydeep-19.mat
The following instructions run the interpolator on the photos provided in frame_interpolation/photos.
To generate an intermediate photo from the input near-duplicate photos, simply run:
> python3 -m frame_interpolation.eval.interpolator_test \
--frame1 frame_interpolation/photos/one.png \
--frame2 frame_interpolation/photos/two.png \
--model_path <pretrained_models>/film_net/Style/saved_model \
--output_frame frame_interpolation/photos/middle.png \
This will produce the sub-frame at t=0.5 and save as
'frame_interpolation/photos/middle.png'.
Takes in a set of directories identified by a glob (--pattern). Each directory is expected to contain at least two input frames, with each contiguous frame pair treated as an input to generate in-between frames.
> python3 -m frame_interpolation.eval.interpolator_cli \
--pattern "frame_interpolation/photos" \
--model_path <pretrained_models>/film_net/Style/saved_model \
--times_to_interpolate 6 \
--output_video
You will find the interpolated frames (including the input frames) in 'frame_interpolation/photos/interpolated_frames/', and the interpolated video at 'frame_interpolation/photos/interpolated.mp4'.
The number of frames is determined by --times_to_interpolate, which controls
the number of times the frame interpolator is invoked. When the number of frames
in a directory is 2, the number of output frames will be
2^times_to_interpolate+1.
We use Vimeo-90K as our main training dataset. For quantitative evaluations, we rely on commonly used benchmark datasets, specifically:
The training and benchmark evaluation scripts expect the frame triplets in the
TFRecord storage format.
We have included scripts that encode the relevant frame triplets into a
tf.train.Example
data format, and export to a TFRecord file.
You can use the commands python3 -m frame_interpolation.datasets.create_<dataset_name>_tfrecord --help for more information.
For example, run the command below to create a TFRecord for the Middlebury-other
dataset. Download the images and point --input_dir to the unzipped folder path.
> python3 -m frame_interpolation.datasets.create_middlebury_tfrecord \
--input_dir=<root folder of middlebury-other> \
--output_tfrecord_filepath=<output tfrecord filepath>
Below are our training gin configuration files for the different loss function:
frame_interpolation/training/
├── config/
│ ├── film_net-L1.gin
│ ├── film_net-VGG.gin
│ ├── film_net-Style.gin
To launch a training, simply pass the configuration filepath to the desired
experiment.
By default, it uses all visible GPUs for training. To debug or train
on a CPU, append --mode cpu.
> python3 -m frame_interpolation.training.train \
--gin_config frame_interpolation/training/config/<config filename>.gin \
--base_folder <base folder for all training runs> \
--label <descriptive label for the run>
- When training finishes, the folder structure will look like this:
<base_folder>/
├── <label>/
│ ├── config.gin
│ ├── eval/
│ ├── train/
│ ├── saved_model/
Optionally, to build a SavedModel format from a trained checkpoints folder, you can use this command:
> python3 -m frame_interpolation.training.build_saved_model_cli \
--base_folder <base folder of training sessions> \
--label <the name of the run>
- By default, a SavedModel is created when the training loop ends, and it will be saved at
<base_folder>/<label>/<saved_model>.
Below, we provided the evaluation gin configuration files for the benchmarks we have considered:
frame_interpolation/eval/
├── config/
│ ├── middlebury.gin
│ ├── ucf101.gin
│ ├── vimeo_90K.gin
│ ├── xiph_2K.gin
│ ├── xiph_4K.gin
To run an evaluation, simply pass the configuration file of the desired evaluation dataset.
If a GPU is visible, it runs on it.
> python3 -m frame_interpolation.eval.eval_cli -- \
--gin_config frame_interpolation/eval/config/<eval_dataset>.gin \
--model_path <pretrained_models>/film_net/L1/saved_model
The above command will produce the PSNR and SSIM scores presented in the paper.
If you find this implementation useful in your works, please acknowledge it appropriately by citing:
@inproceedings{reda2022film,
title = {Frame Interpolation for Large Motion},
author = {Fitsum Reda and Janne Kontkanen and Eric Tabellion and Deqing Sun and Caroline Pantofaru and Brian Curless},
booktitle = {arXiv},
year = {2022}
}
@misc{film-tf,
title = {Tensorflow 2 Implementation of "FILM: Frame Interpolation for Large Scene Motion"},
author = {Fitsum Reda and Janne Kontkanen and Eric Tabellion and Deqing Sun and Caroline Pantofaru and Brian Curless},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/google-research/frame-interpolation}}
}
Contact: Fitsum Reda (fitsum@google.com)
We would like to thank Richard Tucker, Jason Lai and David Minnen. We would also like to thank Jamie Aspinall for the imagery included in this repository.
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