mlsanityData to 7 Oct 2026

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

arXiv 2105.13482 ↗First posted 27 May 2021cs.CVcited by 9

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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

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 17 of its 35 references that are in the map. First posted on arXiv 27 May 2021.

Builds on

17 of 35 references.

References · 17

  1. 2015FlowNet: Learning Optical Flow with Convolutional Networkscited by 1,054
  2. 2018MegaDepth: Learning Single-View Depth Prediction from Internet Photoscited by 731
  3. 2017Video Enhancement with Task-Oriented Flowcited by 612
  4. 2016Optical Flow Estimation using a Spatial Pyramid Networkcited by 421
  5. 2017Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolationcited by 321
  6. 2020Softmax Splatting for Video Frame Interpolationcited by 276
  7. 2014cuDNN: Efficient Primitives for Deep Learningcited by 240
  8. 2019Depth-Aware Video Frame Interpolationcited by 232
  9. 2017Video Frame Interpolation via Adaptive Separable Convolutioncited by 211
  10. 2017Video Frame Synthesis using Deep Voxel Flowcited by 209
  11. 2015DeepStereo: Learning to Predict New Views from the World's Imagerycited by 185
  12. 2016Single-Image Depth Perception in the Wildcited by 179
5 more
  1. 2018Context-aware Synthesis for Video Frame Interpolationcited by 153
  2. 2018Video Compression through Image Interpolationcited by 143
  3. 2018MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancementcited by 111
  4. 2017Accurate Optical Flow via Direct Cost Volume Processingcited by 66
  5. 2016Learning Image Matching by Simply Watching Videocited by 41

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 2; 2023: 5.

9 papers.

  1. 2021Responsive Listening Head Generation: A Benchmark Dataset and Baselinecited by 67
  2. 2023A Dynamic Multi-Scale Voxel Flow Network for Video Predictioncited by 53
  3. 2022Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolationcited by 17
  4. 2021Optical Flow Reusing for High-Efficiency Space-Time Video Super Resolutioncited by 8
  5. 2023Video Frame Interpolation with Densely Queried Bilateral Correlationcited by 8
  6. 2023Neural Image Re-Exposurecited by 4
  7. 2022AdaFNIO: Adaptive Fourier Neural Interpolation Operator for video frame interpolationcited by 2
  8. 2023IDO-VFI: Identifying Dynamics via Optical Flow Guidance for Video Frame Interpolation with Eventscited by 2
  9. 2023MAEVI: Motion Aware Event-Based Video Frame Interpolationcited by 1