├── CONTRIBUTING.md ├── LICENSE ├── README.md ├── WINDOWS_INSTALLATION.md ├── cog.yaml ├── datasets ├── create_middlebury_tfrecord.py ├── create_ucf101_tfrecord.py ├── create_vimeo90K_tfrecord.py ├── create_xiph_tfrecord.py └── util.py ├── eval ├── config │ ├── middlebury.gin │ ├── ucf101.gin │ ├── vimeo_90K.gin │ ├── xiph_2K.gin │ └── xiph_4K.gin ├── eval_cli.py ├── interpolator.py ├── interpolator_cli.py ├── interpolator_test.py └── util.py ├── losses ├── losses.py └── vgg19_loss.py ├── models └── film_net │ ├── feature_extractor.py │ ├── fusion.py │ ├── interpolator.py │ ├── options.py │ ├── pyramid_flow_estimator.py │ └── util.py ├── moment.gif ├── photos ├── one.png └── two.png ├── predict.py ├── requirements.txt └── training ├── augmentation_lib.py ├── build_saved_model_cli.py ├── config ├── film_net-L1.gin ├── film_net-Style.gin └── film_net-VGG.gin ├── data_lib.py ├── eval_lib.py ├── metrics_lib.py ├── model_lib.py ├── train.py └── train_lib.py /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # How to Contribute 2 | 3 | We'd love to accept your patches and contributions to this project. There are 4 | just a few small guidelines you need to follow. 5 | 6 | ## Contributor License Agreement 7 | 8 | Contributions to this project must be accompanied by a Contributor License 9 | Agreement (CLA). You (or your employer) retain the copyright to your 10 | contribution; this simply gives us permission to use and redistribute your 11 | contributions as part of the project. Head over to 12 | to see your current agreements on file or 13 | to sign a new one. 14 | 15 | You generally only need to submit a CLA once, so if you've already submitted one 16 | (even if it was for a different project), you probably don't need to do it 17 | again. 18 | 19 | ## Code Reviews 20 | 21 | All submissions, including submissions by project members, require review. We 22 | use GitHub pull requests for this purpose. Consult 23 | [GitHub Help](https://help.github.com/articles/about-pull-requests/) for more 24 | information on using pull requests. 25 | 26 | ## Community Guidelines 27 | 28 | This project follows 29 | [Google's Open Source Community Guidelines](https://opensource.google/conduct/). 30 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | 2 | Apache License 3 | Version 2.0, January 2004 4 | http://www.apache.org/licenses/ 5 | 6 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 7 | 8 | 1. Definitions. 9 | 10 | "License" shall mean the terms and conditions for use, reproduction, 11 | and distribution as defined by Sections 1 through 9 of this document. 12 | 13 | "Licensor" shall mean the copyright owner or entity authorized by 14 | the copyright owner that is granting the License. 15 | 16 | "Legal Entity" shall mean the union of the acting entity and all 17 | other entities that control, are controlled by, or are under common 18 | control with that entity. For the purposes of this definition, 19 | "control" means (i) the power, direct or indirect, to cause the 20 | direction or management of such entity, whether by contract or 21 | otherwise, or (ii) ownership of fifty percent (50%) or more of the 22 | outstanding shares, or (iii) beneficial ownership of such entity. 23 | 24 | "You" (or "Your") shall mean an individual or Legal Entity 25 | exercising permissions granted by this License. 26 | 27 | "Source" form shall mean the preferred form for making modifications, 28 | including but not limited to software source code, documentation 29 | source, and configuration files. 30 | 31 | "Object" form shall mean any form resulting from mechanical 32 | transformation or translation of a Source form, including but 33 | not limited to compiled object code, generated documentation, 34 | and conversions to other media types. 35 | 36 | "Work" shall mean the work of authorship, whether in Source or 37 | Object form, made available under the License, as indicated by a 38 | copyright notice that is included in or attached to the work 39 | (an example is provided in the Appendix below). 40 | 41 | "Derivative Works" shall mean any work, whether in Source or Object 42 | form, that is based on (or derived from) the Work and for which the 43 | editorial revisions, annotations, elaborations, or other modifications 44 | represent, as a whole, an original work of authorship. For the purposes 45 | of this License, Derivative Works shall not include works that remain 46 | separable from, or merely link (or bind by name) to the interfaces of, 47 | the Work and Derivative Works thereof. 48 | 49 | "Contribution" shall mean any work of authorship, including 50 | the original version of the Work and any modifications or additions 51 | to that Work or Derivative Works thereof, that is intentionally 52 | submitted to Licensor for inclusion in the Work by the copyright owner 53 | or by an individual or Legal Entity authorized to submit on behalf of 54 | the copyright owner. For the purposes of this definition, "submitted" 55 | means any form of electronic, verbal, or written communication sent 56 | to the Licensor or its representatives, including but not limited to 57 | communication on electronic mailing lists, source code control systems, 58 | and issue tracking systems that are managed by, or on behalf of, the 59 | Licensor for the purpose of discussing and improving the Work, but 60 | excluding communication that is conspicuously marked or otherwise 61 | designated in writing by the copyright owner as "Not a Contribution." 62 | 63 | "Contributor" shall mean Licensor and any individual or Legal Entity 64 | on behalf of whom a Contribution has been received by Licensor and 65 | subsequently incorporated within the Work. 66 | 67 | 2. Grant of Copyright License. Subject to the terms and conditions of 68 | this License, each Contributor hereby grants to You a perpetual, 69 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable 70 | copyright license to reproduce, prepare Derivative Works of, 71 | publicly display, publicly perform, sublicense, and distribute the 72 | Work and such Derivative Works in Source or Object form. 73 | 74 | 3. Grant of Patent License. Subject to the terms and conditions of 75 | this License, each Contributor hereby grants to You a perpetual, 76 | worldwide, non-exclusive, no-charge, royalty-free, irrevocable 77 | (except as stated in this section) patent license to make, have made, 78 | use, offer to sell, sell, import, and otherwise transfer the Work, 79 | where such license applies only to those patent claims licensable 80 | by such Contributor that are necessarily infringed by their 81 | Contribution(s) alone or by combination of their Contribution(s) 82 | with the Work to which such Contribution(s) was submitted. If You 83 | institute patent litigation against any entity (including a 84 | cross-claim or counterclaim in a lawsuit) alleging that the Work 85 | or a Contribution incorporated within the Work constitutes direct 86 | or contributory patent infringement, then any patent licenses 87 | granted to You under this License for that Work shall terminate 88 | as of the date such litigation is filed. 89 | 90 | 4. Redistribution. You may reproduce and distribute copies of the 91 | Work or Derivative Works thereof in any medium, with or without 92 | modifications, and in Source or Object form, provided that You 93 | meet the following conditions: 94 | 95 | (a) You must give any other recipients of the Work or 96 | Derivative Works a copy of this License; and 97 | 98 | (b) You must cause any modified files to carry prominent notices 99 | stating that You changed the files; and 100 | 101 | (c) You must retain, in the Source form of any Derivative Works 102 | that You distribute, all copyright, patent, trademark, and 103 | attribution notices from the Source form of the Work, 104 | excluding those notices that do not pertain to any part of 105 | the Derivative Works; and 106 | 107 | (d) If the Work includes a "NOTICE" text file as part of its 108 | distribution, then any Derivative Works that You distribute must 109 | include a readable copy of the attribution notices contained 110 | within such NOTICE file, excluding those notices that do not 111 | pertain to any part of the Derivative Works, in at least one 112 | of the following places: within a NOTICE text file distributed 113 | as part of the Derivative Works; within the Source form or 114 | documentation, if provided along with the Derivative Works; or, 115 | within a display generated by the Derivative Works, if and 116 | wherever such third-party notices normally appear. The contents 117 | of the NOTICE file are for informational purposes only and 118 | do not modify the License. You may add Your own attribution 119 | notices within Derivative Works that You distribute, alongside 120 | or as an addendum to the NOTICE text from the Work, provided 121 | that such additional attribution notices cannot be construed 122 | as modifying the License. 123 | 124 | You may add Your own copyright statement to Your modifications and 125 | may provide additional or different license terms and conditions 126 | for use, reproduction, or distribution of Your modifications, or 127 | for any such Derivative Works as a whole, provided Your use, 128 | reproduction, and distribution of the Work otherwise complies with 129 | the conditions stated in this License. 130 | 131 | 5. Submission of Contributions. Unless You explicitly state otherwise, 132 | any Contribution intentionally submitted for inclusion in the Work 133 | by You to the Licensor shall be under the terms and conditions of 134 | this License, without any additional terms or conditions. 135 | Notwithstanding the above, nothing herein shall supersede or modify 136 | the terms of any separate license agreement you may have executed 137 | with Licensor regarding such Contributions. 138 | 139 | 6. Trademarks. This License does not grant permission to use the trade 140 | names, trademarks, service marks, or product names of the Licensor, 141 | except as required for reasonable and customary use in describing the 142 | origin of the Work and reproducing the content of the NOTICE file. 143 | 144 | 7. Disclaimer of Warranty. Unless required by applicable law or 145 | agreed to in writing, Licensor provides the Work (and each 146 | Contributor provides its Contributions) on an "AS IS" BASIS, 147 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or 148 | implied, including, without limitation, any warranties or conditions 149 | of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A 150 | PARTICULAR PURPOSE. You are solely responsible for determining the 151 | appropriateness of using or redistributing the Work and assume any 152 | risks associated with Your exercise of permissions under this License. 153 | 154 | 8. Limitation of Liability. In no event and under no legal theory, 155 | whether in tort (including negligence), contract, or otherwise, 156 | unless required by applicable law (such as deliberate and grossly 157 | negligent acts) or agreed to in writing, shall any Contributor be 158 | liable to You for damages, including any direct, indirect, special, 159 | incidental, or consequential damages of any character arising as a 160 | result of this License or out of the use or inability to use the 161 | Work (including but not limited to damages for loss of goodwill, 162 | work stoppage, computer failure or malfunction, or any and all 163 | other commercial damages or losses), even if such Contributor 164 | has been advised of the possibility of such damages. 165 | 166 | 9. Accepting Warranty or Additional Liability. While redistributing 167 | the Work or Derivative Works thereof, You may choose to offer, 168 | and charge a fee for, acceptance of support, warranty, indemnity, 169 | or other liability obligations and/or rights consistent with this 170 | License. However, in accepting such obligations, You may act only 171 | on Your own behalf and on Your sole responsibility, not on behalf 172 | of any other Contributor, and only if You agree to indemnify, 173 | defend, and hold each Contributor harmless for any liability 174 | incurred by, or claims asserted against, such Contributor by reason 175 | of your accepting any such warranty or additional liability. 176 | 177 | END OF TERMS AND CONDITIONS 178 | 179 | APPENDIX: How to apply the Apache License to your work. 180 | 181 | To apply the Apache License to your work, attach the following 182 | boilerplate notice, with the fields enclosed by brackets "[]" 183 | replaced with your own identifying information. (Don't include 184 | the brackets!) The text should be enclosed in the appropriate 185 | comment syntax for the file format. We also recommend that a 186 | file or class name and description of purpose be included on the 187 | same "printed page" as the copyright notice for easier 188 | identification within third-party archives. 189 | 190 | Copyright [yyyy] [name of copyright owner] 191 | 192 | Licensed under the Apache License, Version 2.0 (the "License"); 193 | you may not use this file except in compliance with the License. 194 | You may obtain a copy of the License at 195 | 196 | http://www.apache.org/licenses/LICENSE-2.0 197 | 198 | Unless required by applicable law or agreed to in writing, software 199 | distributed under the License is distributed on an "AS IS" BASIS, 200 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 201 | See the License for the specific language governing permissions and 202 | limitations under the License. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # FILM: Frame Interpolation for Large Motion 2 | 3 | ### [Website](https://film-net.github.io/) | [Paper](https://arxiv.org/pdf/2202.04901.pdf) | [Google AI Blog](https://ai.googleblog.com/2022/10/large-motion-frame-interpolation.html) | [Tensorflow Hub Colab](https://www.tensorflow.org/hub/tutorials/tf_hub_film_example) | [YouTube](https://www.youtube.com/watch?v=OAD-BieIjH4)
4 | 5 | The official 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.
6 | 7 | [FILM: Frame Interpolation for Large Motion](https://arxiv.org/abs/2202.04901)
8 | [Fitsum Reda](https://fitsumreda.github.io/)1, [Janne Kontkanen](https://scholar.google.com/citations?user=MnXc4JQAAAAJ&hl=en)1, [Eric Tabellion](http://www.tabellion.org/et/)1, [Deqing Sun](https://deqings.github.io/)1, [Caroline Pantofaru](https://scholar.google.com/citations?user=vKAKE1gAAAAJ&hl=en)1, [Brian Curless](https://homes.cs.washington.edu/~curless/)1,2
9 | 1Google Research, 2University of Washington
10 | In ECCV 2022. 11 | 12 | ![A sample 2 seconds moment.](https://github.com/googlestaging/frame-interpolation/blob/main/moment.gif) 13 | FILM transforms near-duplicate photos into a slow motion footage that look like it is shot with a video camera. 14 | 15 | ## Web Demo 16 | 17 | Integrated into [Hugging Face Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/johngoad/frame-interpolation) 18 | 19 | Try the interpolation model with the replicate web demo at 20 | [![Replicate](https://replicate.com/google-research/frame-interpolation/badge)](https://replicate.com/google-research/frame-interpolation) 21 | 22 | Try FILM to interpolate between two or more images with the PyTTI-Tools at [![PyTTI-Tools:FILM](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.sandbox.google.com/github/pytti-tools/frame-interpolation/blob/main/PyTTI_Tools_FiLM-colab.ipynb#scrollTo=-7TD7YZJbsy_) 23 | 24 | An alternative Colab for running FILM on arbitrarily more input images, not just on two images, [![FILM-Gdrive](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1NuaPPSvUhYafymUf2mEkvhnEtpD5oihs) 25 | 26 | ## Change Log 27 | * **Nov 28, 2022**: Upgrade `eval.interpolator_cli` for **high resolution frame interpolation**. `--block_height` and `--block_width` determine the total number of patches (`block_height*block_width`) to subdivide the input images. By default, both arguments are set to 1, and so no subdivision will be done. 28 | * **Mar 12, 2022**: Support for Windows, see [WINDOWS_INSTALLATION.md](https://github.com/google-research/frame-interpolation/blob/main/WINDOWS_INSTALLATION.md). 29 | * **Mar 09, 2022**: Support for **high resolution frame interpolation**. Set `--block_height` and `--block_width` in `eval.interpolator_test` to extract patches from the inputs, and reconstruct the interpolated frame from the iteratively interpolated patches. 30 | 31 | ## Installation 32 | 33 | * Get Frame Interpolation source codes 34 | 35 | ``` 36 | git clone https://github.com/google-research/frame-interpolation 37 | cd frame-interpolation 38 | ``` 39 | 40 | * Optionally, pull the recommended Docker base image 41 | 42 | ``` 43 | docker pull gcr.io/deeplearning-platform-release/tf2-gpu.2-6:latest 44 | ``` 45 | 46 | * If you do not use Docker, set up your NVIDIA GPU environment with: 47 | * [Anaconda Python 3.9](https://www.anaconda.com/products/individual) 48 | * [CUDA Toolkit 11.2.1](https://developer.nvidia.com/cuda-11.2.1-download-archive) 49 | * [cuDNN 8.1.0](https://developer.nvidia.com/rdp/cudnn-download) 50 | 51 | * Install frame interpolation dependencies 52 | 53 | ``` 54 | pip3 install -r requirements.txt 55 | sudo apt-get install -y ffmpeg 56 | ``` 57 | 58 | ### See [WINDOWS_INSTALLATION](https://github.com/google-research/frame-interpolation/blob/main/WINDOWS_INSTALLATION.md) for Windows Support 59 | 60 | ## Pre-trained Models 61 | 62 | * Create a directory where you can keep large files. Ideally, not in this 63 | directory. 64 | 65 | ``` 66 | mkdir -p 67 | ``` 68 | 69 | * Download pre-trained TF2 Saved Models from 70 | [google drive](https://drive.google.com/drive/folders/1q8110-qp225asX3DQvZnfLfJPkCHmDpy?usp=sharing) 71 | and put into ``. 72 | 73 | The downloaded folder should have the following structure: 74 | 75 | ``` 76 | / 77 | ├── film_net/ 78 | │ ├── L1/ 79 | │ ├── Style/ 80 | │ ├── VGG/ 81 | ├── vgg/ 82 | │ ├── imagenet-vgg-verydeep-19.mat 83 | ``` 84 | 85 | ## Running the Codes 86 | 87 | The following instructions run the interpolator on the photos provided in 88 | 'frame-interpolation/photos'. 89 | 90 | ### One mid-frame interpolation 91 | 92 | To generate an intermediate photo from the input near-duplicate photos, simply run: 93 | 94 | ``` 95 | python3 -m eval.interpolator_test \ 96 | --frame1 photos/one.png \ 97 | --frame2 photos/two.png \ 98 | --model_path /film_net/Style/saved_model \ 99 | --output_frame photos/output_middle.png 100 | ``` 101 | 102 | This will produce the sub-frame at `t=0.5` and save as 'photos/output_middle.png'. 103 | 104 | ### Many in-between frames interpolation 105 | 106 | It takes in a set of directories identified by a glob (--pattern). Each directory 107 | is expected to contain at least two input frames, with each contiguous frame 108 | pair treated as an input to generate in-between frames. Frames should be named such that when sorted (naturally) with `natsort`, their desired order is unchanged. 109 | 110 | ``` 111 | python3 -m eval.interpolator_cli \ 112 | --pattern "photos" \ 113 | --model_path /film_net/Style/saved_model \ 114 | --times_to_interpolate 6 \ 115 | --output_video 116 | ``` 117 | 118 | You will find the interpolated frames (including the input frames) in 119 | 'photos/interpolated_frames/', and the interpolated video at 120 | 'photos/interpolated.mp4'. 121 | 122 | The number of frames is determined by `--times_to_interpolate`, which controls 123 | the number of times the frame interpolator is invoked. When the number of frames 124 | in a directory is `num_frames`, the number of output frames will be 125 | `(2^times_to_interpolate+1)*(num_frames-1)`. 126 | 127 | ## Datasets 128 | 129 | We use [Vimeo-90K](http://data.csail.mit.edu/tofu/dataset/vimeo_triplet.zip) as 130 | our main training dataset. For quantitative evaluations, we rely on commonly 131 | used benchmark datasets, specifically: 132 | 133 | * [Vimeo-90K](http://data.csail.mit.edu/tofu/testset/vimeo_interp_test.zip) 134 | * [Middlebury-Other](https://vision.middlebury.edu/flow/data) 135 | * [UCF101](https://people.cs.umass.edu/~hzjiang/projects/superslomo/UCF101_results.zip) 136 | * [Xiph](https://github.com/sniklaus/softmax-splatting/blob/master/benchmark.py) 137 | 138 | ### Creating a TFRecord 139 | 140 | The training and benchmark evaluation scripts expect the frame triplets in the 141 | [TFRecord](https://www.tensorflow.org/tutorials/load_data/tfrecord) storage format.
142 | 143 | We have included scripts that encode the relevant frame triplets into a 144 | [tf.train.Example](https://www.tensorflow.org/api_docs/python/tf/train/Example) 145 | data format, and export to a TFRecord file.
146 | 147 | You can use the commands `python3 -m 148 | datasets.create__tfrecord --help` for more information. 149 | 150 | For example, run the command below to create a TFRecord for the Middlebury-other 151 | dataset. Download the [images](https://vision.middlebury.edu/flow/data) and point `--input_dir` to the unzipped folder path. 152 | 153 | ``` 154 | python3 -m datasets.create_middlebury_tfrecord \ 155 | --input_dir= \ 156 | --output_tfrecord_filepath= \ 157 | --num_shards=3 158 | ``` 159 | 160 | The above command will output a TFRecord file with 3 shards as `@3`. 161 | 162 | ## Training 163 | 164 | Below are our training gin configuration files for the different loss function: 165 | 166 | ``` 167 | training/ 168 | ├── config/ 169 | │ ├── film_net-L1.gin 170 | │ ├── film_net-VGG.gin 171 | │ ├── film_net-Style.gin 172 | ``` 173 | 174 | To launch a training, simply pass the configuration filepath to the desired 175 | experiment.
176 | By default, it uses all visible GPUs for training. To debug or train 177 | on a CPU, append `--mode cpu`. 178 | 179 | ``` 180 | python3 -m training.train \ 181 | --gin_config training/config/.gin \ 182 | --base_folder \ 183 | --label 184 | ``` 185 | 186 | * When training finishes, the folder structure will look like this: 187 | 188 | ``` 189 | / 190 | ├──