# FastRIFE: Optimization of Real-Time Intermediate Flow Estimation for Video Frame Interpolation

arXiv: https://arxiv.org/abs/2105.13482 · HTML: https://mlsanity.com/paper/2105.13482/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 35 references that are in the map.
- First posted on arXiv 27 May 2021.

## Builds on (most cited first, 17 of 17)

- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [MegaDepth: Learning Single-View Depth Prediction from Internet Photos](https://mlsanity.com/paper/1804.00607/) (2018)
- [Video Enhancement with Task-Oriented Flow](https://mlsanity.com/paper/1711.09078/) (2017)
- [Optical Flow Estimation using a Spatial Pyramid Network](https://mlsanity.com/paper/1611.00850/) (2016)
- [Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation](https://mlsanity.com/paper/1712.00080/) (2017)
- [Softmax Splatting for Video Frame Interpolation](https://mlsanity.com/paper/2003.05534/) (2020)
- [cuDNN: Efficient Primitives for Deep Learning](https://mlsanity.com/paper/1410.0759/) (2014)
- [Depth-Aware Video Frame Interpolation](https://mlsanity.com/paper/1904.00830/) (2019)
- [Video Frame Interpolation via Adaptive Separable Convolution](https://mlsanity.com/paper/1708.01692/) (2017)
- [Video Frame Synthesis using Deep Voxel Flow](https://mlsanity.com/paper/1702.02463/) (2017)
- [DeepStereo: Learning to Predict New Views from the World's Imagery](https://mlsanity.com/paper/1506.06825/) (2015)
- [Single-Image Depth Perception in the Wild](https://mlsanity.com/paper/1604.03901/) (2016)
- [Context-aware Synthesis for Video Frame Interpolation](https://mlsanity.com/paper/1803.10967/) (2018)
- [Video Compression through Image Interpolation](https://mlsanity.com/paper/1804.06919/) (2018)
- [MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement](https://mlsanity.com/paper/1810.08768/) (2018)
- [Accurate Optical Flow via Direct Cost Volume Processing](https://mlsanity.com/paper/1704.07325/) (2017)
- [Learning Image Matching by Simply Watching Video](https://mlsanity.com/paper/1603.06041/) (2016)

## Built on by (most cited first, 9 of 9)

- [Responsive Listening Head Generation: A Benchmark Dataset and Baseline](https://mlsanity.com/paper/2112.13548/) (2021)
- [A Dynamic Multi-Scale Voxel Flow Network for Video Prediction](https://mlsanity.com/paper/2303.09875/) (2023)
- [Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolation](https://mlsanity.com/paper/2203.10291/) (2022)
- [Optical Flow Reusing for High-Efficiency Space-Time Video Super Resolution](https://mlsanity.com/paper/2110.06786/) (2021)
- [Video Frame Interpolation with Densely Queried Bilateral Correlation](https://mlsanity.com/paper/2304.13596/) (2023)
- [Neural Image Re-Exposure](https://mlsanity.com/paper/2305.13593/) (2023)
- [AdaFNIO: Adaptive Fourier Neural Interpolation Operator for video frame interpolation](https://mlsanity.com/paper/2211.10791/) (2022)
- [IDO-VFI: Identifying Dynamics via Optical Flow Guidance for Video Frame Interpolation with Events](https://mlsanity.com/paper/2305.10198/) (2023)
- [MAEVI: Motion Aware Event-Based Video Frame Interpolation](https://mlsanity.com/paper/2303.02025/) (2023)

## Abstract (arXiv)

The problem of video inter-frame interpolation is an essential task in the field of image processing. Correctly increasing the number of frames in the recording while maintaining smooth movement allows to improve the quality of played video sequence, enables more effective compression and creating a slow-motion recording. This paper proposes the FastRIFE algorithm, which is some speed improvement of the RIFE (Real-Time Intermediate Flow Estimation) model. The novel method was examined and compared with other recently published algorithms. All source codes are available at https://gitlab.com/malwinq/interpolation-of-images-for-slow-motion-videos