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

0 of 23 row(s) selected.