Video Frame Interpolation On Snu Film Hard
평가 지표
PSNR
SSIM
평가 결과
이 벤치마크에서 각 모델의 성능 결과
모델 이름 | PSNR | SSIM | Paper Title | Repository |
|---|---|---|---|---|
| DQBC | 30.94 | 0.9378 | Video Frame Interpolation with Densely Queried Bilateral Correlation | |
| CURE | 30.66 | 0.9373 | Learning Cross-Video Neural Representations for High-Quality Frame Interpolation | |
| UPR-Net LARGE | 30.86 | 0.9377 | A Unified Pyramid Recurrent Network for Video Frame Interpolation | |
| DBVI | 31.68 | 0.953 | Deep Bayesian Video Frame Interpolation | |
| ST-MFNet | 31.698 | - | ST-MFNet: A Spatio-Temporal Multi-Flow Network for Frame Interpolation | |
| EMA-VFI | 30.94 | 0.9392 | Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation | |
| EBME-H* | 30.64 | 0.937 | Enhanced Bi-directional Motion Estimation for Video Frame Interpolation | |
| VFIMamba | 30.99 | 0.9401 | VFIMamba: Video Frame Interpolation with State Space Models |
0 of 8 row(s) selected.