Video Frame Interpolation On Vimeo90K
평가 지표
PSNR
평가 결과
이 벤치마크에서 각 모델의 성능 결과
비교 표
| 모델 이름 | PSNR |
|---|---|
| video-frame-interpolation-via-adaptive | 33.80 |
| video-enhancement-with-task-oriented-flow | 33.73 |
| many-to-many-splatting-for-efficient-video | 35.4 |
| film-frame-interpolation-for-large-motion | 36.06 |
| bmbc-bilateral-motion-estimation-with | 35.01 |
| asymmetric-bilateral-motion-estimation-for | 36.18 |
| memc-net-motion-estimation-and-motion | 34.40 |
| a-unified-pyramid-recurrent-network-for-video | 36.42 |
| neighbor-correspondence-matching-for-flow | 35.88 |
| ifrnet-intermediate-feature-refine-network | 36.20 |
| softmax-splatting-for-video-frame | 36.10 |
| cdfi-compression-driven-network-design-for | 35.17 |
| 모델 13 | 34.65 |
| depth-aware-video-frame-interpolation | 34.71 |
| enhanced-correlation-matching-based-video | 34.95 |
| enhanced-bi-directional-motion-estimation-for | 36.19 |
| exploring-motion-ambiguity-and-alignment-for | 36.76 |
| video-frame-interpolation-via-residue | 35.22 |
| xvfi-extreme-video-frame-interpolation | 35.07 |
| depth-aware-video-frame-interpolation | 34.71 |
| extracting-motion-and-appearance-via-inter | 36.64 |
| video-frame-interpolation-with-densely | 36.57 |
| learning-cross-video-neural-representations | 35.73 |