Video Frame Interpolation On Vimeo90K

PSNR

Results

Performance results of various models on this benchmark

Model Name	PSNR	Paper Title	Repository
SepConv-L1	33.80	Video Frame Interpolation via Adaptive Separable Convolution	-
ToFlow	33.73	Video Enhancement with Task-Oriented Flow	-
M2M-PWC	35.4	Many-to-many Splatting for Efficient Video Frame Interpolation	-
FILM	36.06	FILM: Frame Interpolation for Large Motion	-
BMBC	35.01	BMBC:Bilateral Motion Estimation with Bilateral Cost Volume for Video Interpolation	-
ABME	36.18	Asymmetric Bilateral Motion Estimation for Video Frame Interpolation	-
MEMC-Net*	34.40	MEMC-Net: Motion Estimation and Motion Compensation Driven Neural Network for Video Interpolation and Enhancement	-
UPR-Net LARGE	36.42	A Unified Pyramid Recurrent Network for Video Frame Interpolation	-
NCM-Base	35.88	Neighbor Correspondence Matching for Flow-based Video Frame Synthesis	-
IFRNet	36.20	IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation	-
SoftSplat	36.10	Softmax Splatting for Video Frame Interpolation	-
CDFI	35.17	CDFI: Compression-Driven Network Design for Frame Interpolation	-
FRUCnet	34.65	-	-
DAIN	34.71	Depth-Aware Video Frame Interpolation	-
ECMNet	34.95	Enhanced Correlation Matching based Video Frame Interpolation	-
EBME-H*	36.19	Enhanced Bi-directional Motion Estimation for Video Frame Interpolation	-
MA-CSPA	36.76	Exploring Motion Ambiguity and Alignment for High-Quality Video Frame Interpolation	-
RRIN	35.22	Video Frame Interpolation via Residue Refinement
XVFI	35.07	XVFI: eXtreme Video Frame Interpolation	-
DAIN	34.71	Depth-Aware Video Frame Interpolation	-

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