Video Frame Interpolation On Snu Film Medium
Metrics
PSNR
SSIM
Results
Performance results of various models on this benchmark
Model Name | PSNR | SSIM | Paper Title | Repository |
|---|---|---|---|---|
| CURE | 35.94 | 0.9797 | Learning Cross-Video Neural Representations for High-Quality Frame Interpolation | - |
| ST-MFNet | 37.111 | - | ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation | - |
| EBME-H* | 36.07 | 0.980 | Enhanced Bi-directional Motion Estimation for Video Frame Interpolation | - |
| DQBC | 36.25 | 0.9799 | Video Frame Interpolation with Densely Queried Bilateral Correlation | - |
| EMA-VFI | 36.09 | 0.9801 | Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation | - |
| UPR-Net LARGE | 36.29 | 0.9801 | A Unified Pyramid Recurrent Network for Video Frame Interpolation | - |
| DBVI | 36.95 | 0.985 | Deep Bayesian Video Frame Interpolation | |
| VFIMamba | 36.40 | 0.9805 | VFIMamba: Video Frame Interpolation with State Space Models | - |
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