SOTAVerified

Video Frame Interpolation

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.

Source: Reducing the X-ray radiation exposure frequency in cardio-angiography via deep-learning based video interpolation

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 151–200 of 204 papers

TitleStatusHype
FloLPIPS: A Bespoke Video Quality Metric for Frame Interpoation—0
Neighbor Correspondence Matching for Flow-based Video Frame Synthesis—0
Optical Flow Regularization of Implicit Neural Representations for Video Frame Interpolation—0
Video frame interpolation for high dynamic range sequences captured with dual-exposure sensors—0
Multi-encoder Network for Parameter Reduction of a Kernel-based Interpolation Architecture—0
Image2Gif: Generating Continuous Realistic Animations with Warping NODEsCode0
Video Frame Interpolation Based on Deformable Kernel RegionCode0
Unsupervised Video Interpolation by Learning Multilayered 2.5D Motion Fields—0
HSTR-Net: High Spatio-Temporal Resolution Video Generation For Wide Area SurveillanceCode0
Time Lens++: Event-based Frame Interpolation with Parametric Non-linear Flow and Multi-scale Fusion—0
Long-term Video Frame Interpolation via Feature Propagation—0
Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolation—0
Enhancing Deformable Convolution based Video Frame Interpolation with Coarse-to-fine 3D CNN—0
A Subjective Quality Study for Video Frame Interpolation—0
Splatting-based Synthesis for Video Frame Interpolation—0
Video Reconstruction from a Single Motion Blurred Image using Learned Dynamic Phase CodingCode0
Enhanced Correlation Matching based Video Frame Interpolation—0
ReFIn: A Refinement Approach for Video Frame Interpolation—0
RAI-Net: Range-Adaptive LiDAR Point Cloud Frame Interpolation Network—0
FastRIFE: Optimization of Real-Time Intermediate Flow Estimation for Video Frame InterpolationCode0
EA-Net: Edge-Aware Network for Flow-based Video Frame Interpolation—0
Video Frame Interpolation via Structure-Motion based Iterative Fusion—0
Distributed Video Adaptive Block Compressive Sensing—0
The U-Net based GLOW for Optical-Flow-free Video Interframe Generation—0
Texture-aware Video Frame Interpolation—0
Temporal Spatial-Adaptive Interpolation with Deformable Refinement for Electron Microscopic Images—0
A comprehensive survey on video frame interpolation techniques—0
Motion-Aware Dynamic Architecture for Efficient Frame Interpolation—0
Revisiting Adaptive Convolutions for Video Frame Interpolation—0
A Flexible Recurrent Residual Pyramid Network for Video Frame Interpolation—0
Reducing the X-ray radiation exposure frequency in cardio-angiography via deep-learning based video interpolation—0
W-Cell-Net: Multi-frame Interpolation of Cellular Microscopy VideosCode0
Convolutional Neural Networks vs. Deformable Image Registration For Medical Slice Interpolation—0
GIMP-ML: Python Plugins for using Computer Vision Models in GIMPCode0
FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-scale Temporal LossCode0
AdaCoF: Adaptive Collaboration of Flows for Video Frame InterpolationCode0
Physical Integrity Attack Detection of Surveillance Camera with Deep Learning Based Video Frame Interpolation—0
Unsupervised Video Interpolation Using Cycle ConsistencyCode0
IM-Net for High Resolution Video Frame Interpolation—0
Event-driven Video Frame SynthesisCode0
Deep Video Frame Interpolation using Cyclic Frame GenerationCode0
Zoom-In-to-Check: Boosting Video Interpolation via Instance-level Discrimination—0
Variational Approach for Capsule Video Frame InterpolationCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Frame Interpolation and EnhancementCode0
MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and EnhancementCode0
Learning Joint Wasserstein Auto-Encoders for Joint Distribution Matching—0
Implementing Adaptive Separable Convolution for Video Frame InterpolationCode0
Using phase instead of optical flow for action recognitionCode0
Video Frame Interpolation by Plug-and-Play Deep Locally Linear Embedding—0
PhaseNet for Video Frame Interpolation—0
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All datasetsMSU Video Frame InterpolationVimeo90KX4K1000FPSUCF101MiddleburySNU-FILM (easy)SNU-FILM (extreme)SNU-FILM (hard)SNU-FILM (medium)Vid4 - 4x upscalingX4K1000FPS-2KXiph-2K

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1EMA-VFIPSNR29.89—Unverified
2UPR-Net LARGEPSNR29.73—Unverified
3DQBCPSNR29.45—Unverified
4EBME-HPSNR28.77—Unverified
5EBMEPSNR28.56—Unverified
6VFIformerPSNR28.34—Unverified
7FILMPSNR28.11—Unverified
8IFRNet_largePSNR28.04—Unverified
9CUREPSNR28.01—Unverified
10ABMEPSNR27.99—Unverified
#ModelMetricClaimedVerifiedStatus
1MA-CSPAPSNR36.76—Unverified
2EMA-VFIPSNR36.64—Unverified
3DQBCPSNR36.57—Unverified
4UPR-Net LARGEPSNR36.42—Unverified
5IFRNetPSNR36.2—Unverified
6EBME-H*PSNR36.19—Unverified
7ABMEPSNR36.18—Unverified
8SoftSplatPSNR36.1—Unverified
9FILMPSNR36.06—Unverified
10NCM-BasePSNR35.88—Unverified
#ModelMetricClaimedVerifiedStatus
1HiFIPSNR32.92—Unverified
2DBVIPSNR32.89—Unverified
3VFIMambaPSNR32.15—Unverified
4NCM-BasePSNR31.63—Unverified
5EMA-VFIPSNR31.46—Unverified
6BiFormerPSNR31.32—Unverified
7M2M-PWCPSNR30.81—Unverified
8UPR-Net largePSNR30.68—Unverified
9ECMNetPSNR30.51—Unverified
10fLDR-NetPSNR30.45—Unverified
#ModelMetricClaimedVerifiedStatus
1EMA-VFIPSNR35.48—Unverified
2UPR-Net LARGEPSNR35.47—Unverified
3VFIMambaPSNR35.45—Unverified
4DQBCPSNR35.44—Unverified
5MA-CSPAPSNR35.43—Unverified
6IFRNetPSNR35.42—Unverified
7EBME-H*PSNR35.41—Unverified
8SoftSplatPSNR35.39—Unverified
9ABMEPSNR35.38—Unverified
10CUREPSNR35.36—Unverified
#ModelMetricClaimedVerifiedStatus
1MA-CSPAPSNR38.83—Unverified
2FILMPSNR37.52—Unverified
3CDFIPSNR37.14—Unverified
4SepConv-L1Interpolation Error5.61—Unverified
5ToFlowInterpolation Error5.49—Unverified
6MEMC-NETInterpolation Error5.24—Unverified
7DAINInterpolation Error4.86—Unverified
8FRUCnetInterpolation Error4.78—Unverified
9BMBCInterpolation Error4.48—Unverified
10SoftSplatInterpolation Error4.22—Unverified
#ModelMetricClaimedVerifiedStatus
1ST-MFNetPSNR40.78—Unverified
2VFIMambaPSNR40.51—Unverified
3DBVIPSNR40.46—Unverified
4UPR-Net LARGEPSNR40.44—Unverified
5DQBCPSNR40.31—Unverified
6EBME-H*PSNR40.28—Unverified
7EMA-VFIPSNR39.98—Unverified
8CUREPSNR39.9—Unverified
#ModelMetricClaimedVerifiedStatus
1DBVIPSNR25.9—Unverified
2ST-MFNetPSNR25.81—Unverified
3VFIMambaPSNR25.79—Unverified
4EMA-VFIPSNR25.69—Unverified
5UPR-Net LARGEPSNR25.63—Unverified
6DQBCPSNR25.61—Unverified
7CUREPSNR25.44—Unverified
8EBME-H*PSNR25.4—Unverified
#ModelMetricClaimedVerifiedStatus
1ST-MFNetPSNR31.7—Unverified
2DBVIPSNR31.68—Unverified
3VFIMambaPSNR30.99—Unverified
4DQBCPSNR30.94—Unverified
5EMA-VFIPSNR30.94—Unverified
6UPR-Net LARGEPSNR30.86—Unverified
7CUREPSNR30.66—Unverified
8EBME-H*PSNR30.64—Unverified
#ModelMetricClaimedVerifiedStatus
1ST-MFNetPSNR37.11—Unverified
2DBVIPSNR36.95—Unverified
3VFIMambaPSNR36.4—Unverified
4UPR-Net LARGEPSNR36.29—Unverified
5DQBCPSNR36.25—Unverified
6EMA-VFIPSNR36.09—Unverified
7EBME-H*PSNR36.07—Unverified
8CUREPSNR35.94—Unverified
#ModelMetricClaimedVerifiedStatus
1VRTPSNR27.46—Unverified
2RSTT-LPSNR26.43—Unverified
3RSTT-MPSNR26.37—Unverified
4Zooming Slow-MoPSNR26.31—Unverified
5RSTT-SPSNR26.29—Unverified
#ModelMetricClaimedVerifiedStatus
1VFIMambaPSNR33.33—Unverified
2EMA-VFIPSNR32.85—Unverified
3M2M-PWCPSNR32.07—Unverified
4CUREPSNR30.05—Unverified
#ModelMetricClaimedVerifiedStatus
1VFIMambaPSNR37.13—Unverified
2EMA-VFIPSNR36.9—Unverified
3FILMPSNR36.66—Unverified
4M2M-PWCPSNR36.45—Unverified
#ModelMetricClaimedVerifiedStatus
1EMA-VFIPSNR34.67—Unverified
2VFIMambaPSNR34.62—Unverified
3FILMPSNR33.78—Unverified
4CUREPSNR30.94—Unverified
#ModelMetricClaimedVerifiedStatus
1RIFELPIPS0.14—Unverified
2EMA-VFILPIPS0.03—Unverified
3FLAVRLPIPS0.03—Unverified
#ModelMetricClaimedVerifiedStatus
1DBVIPSNR28.61—Unverified
2ST-MFNetPSNR28.29—Unverified
#ModelMetricClaimedVerifiedStatus
1DBVIPSNR31.73—Unverified
2FLAVRPSNR31.31—Unverified
#ModelMetricClaimedVerifiedStatus
1M2M-PWCPSNR29.03—Unverified
#ModelMetricClaimedVerifiedStatus
1CUREPSNR36.24—Unverified
#ModelMetricClaimedVerifiedStatus
1ST-MFNetPSNR29.18—Unverified
#ModelMetricClaimedVerifiedStatus
1M2M-PWCPSNR33.93—Unverified