google-research / frame-interpolation

Frame Interpolation for Large Scene Motion

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Input

Output

Run time and cost

This model runs on Nvidia T4 GPU hardware. Predictions typically complete within 108 seconds. The predict time for this model varies significantly based on the inputs.

Readme

FILM: Frame Interpolation for Large Scene Motion

Paper | YouTube | Benchmark Scores

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.

Citation

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}}
}