Video Frame Interpolation
96 papers with code • 20 benchmarks • 12 datasets
The goal of Video Frame Interpolation is to synthesize several frames in the middle of two adjacent frames of the original video. Video Frame Interpolation can be applied to generate slow motion video, increase video frame rate, and frame recovery in video streaming.
Benchmarks
These leaderboards are used to track progress in Video Frame Interpolation
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Libraries
Use these libraries to find Video Frame Interpolation models and implementationsDatasets
UCF101
DAVIS
GoPro
Middlebury
Vimeo90K
X4K1000FPS
MSU Video Frame Interpolation
Inter4K
SlowFlow
ATD-12K
Subtasks
Most implemented papers
RIFE: Real-Time Intermediate Flow Estimation for Video Frame Interpolation
We propose RIFE, a Real-time Intermediate Flow Estimation algorithm for Video Frame Interpolation (VFI).
Video Frame Interpolation via Adaptive Separable Convolution
sniklaus/sepconv-slomo
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ICCV 2017
Our method develops a deep fully convolutional neural network that takes two input frames and estimates pairs of 1D kernels for all pixels simultaneously.
AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation
mcg-nku/amt
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CVPR 2023
It is based on two essential designs.
Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation
avinashpaliwal/Super-SloMo
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CVPR 2018
Finally, the two input images are warped and linearly fused to form each intermediate frame.
Depth-Aware Video Frame Interpolation
The proposed model then warps the input frames, depth maps, and contextual features based on the optical flow and local interpolation kernels for synthesizing the output frame.
Video Enhancement with Task-Oriented Flow
Many video enhancement algorithms rely on optical flow to register frames in a video sequence.
Implementing Adaptive Separable Convolution for Video Frame Interpolation
As Deep Neural Networks are becoming more popular, much of the attention is being devoted to Computer Vision problems that used to be solved with more traditional approaches.
Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution
Mukosame/Zooming-Slow-Mo-CVPR-2020
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CVPR 2020
Rather than synthesizing missing LR video frames as VFI networks do, we firstly temporally interpolate LR frame features in missing LR video frames capturing local temporal contexts by the proposed feature temporal interpolation network.
ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation
danielism97/st-mfnet
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CVPR 2022
Video frame interpolation (VFI) is currently a very active research topic, with applications spanning computer vision, post production and video encoding.
Joint Video Multi-Frame Interpolation and Deblurring under Unknown Exposure Time
Moreover, on the seemingly implausible x16 interpolation task, our method outperforms existing methods by more than 1. 5 dB in terms of PSNR.
