Video Prediction on Human3.6M
0.9851SSIMSimVP+SVQ
Evaluation Results
| Method | Links | |||||
|---|---|---|---|---|---|---|
| SimVP+SVQcodebook=frozen2023.12 | 0.9851 | 34.07 | 0.0238 | — | 1,264.9 | |
| SimVP+SVQcodebook=learnable2023.12 | 0.9851 | 34.06 | 0.0237 | — | 1,265.1 | |
| TAU2023.12 | 0.9839 | 34.03 | 0.0278 | — | 1,390.7 | |
| SimVP (w/o VQ)2023.12 | 0.9834 | 34.08 | 0.0322 | — | 1,441 | |
| PredRNN++2023.12 | 0.9832 | 34.02 | 0.032 | — | 1,452.2 | |
| PredRNN2023.12 | 0.9831 | 33.94 | 0.0325 | — | 1,458.3 | |
| MIM2023.12 | 0.9829 | 33.97 | 0.0334 | — | 1,467.1 | |
| PredRNN.V22023.12 | 0.9827 | 33.84 | 0.0333 | — | 1,454.7 | |
| ConvLSTM2023.12 | 0.9813 | 33.4 | 0.0356 | — | 1,583.3 | |
| MAU2023.12 | 0.9812 | 33.33 | 0.0356 | — | 1,577 | |
| PhyDNet2023.12 | 0.9804 | 33.84 | 0.0371 | — | 1,614.7 | |
| E3D-LSTM2023.12 | 0.9803 | 32.52 | 0.0413 | — | 1,442.5 | |
| PredNet2023.12 | 0.9786 | 31.76 | 0.0326 | — | 1,625.3 | |
| Grid keypointNumber of parameters=2.0M2021.07 | 0.915 | 26.06 | 0.055 | 166.1 | — | |
| Struct-VRNNNumber of parameters=2.3M2021.07 | 0.901 | 24.98 | 0.056 | 193.8 | — | |
| SVG-LPNumber of parameters=22.8M2021.07 | 0.893 | 24.67 | 0.084 | 179.5 | — |