Human Motion Prediction on H3.6M (560ms & 1000ms Activity Averages)
0.93Eating Error (1000ms)AGED
Evaluation Results
| Method | Links | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AGEDadversarial training=true2019.08 | 0.93 | 0.78 | 0.91 | 0.86 | 1.06 | 1.21 | 1.25 | 1.3 | 0.99 | 1.09 | — | — | |
| AGEDadversarial training=false2019.08 | 1.01 | 0.89 | 1.02 | 0.92 | 1.15 | 1.43 | 1.33 | 1.54 | 1.07 | 1.24 | — | — | |
| zero-velocity2019.08 | 1.02 | 1.35 | 1.32 | 1.38 | 1.69 | 1.41 | 1.96 | 1.21 | — | 1.59 | — | — | |
| Residual sup.2019.08 | 1.08 | 0.93 | 1.03 | 0.95 | 1.25 | 1.5 | 1.43 | 1.69 | 1.14 | 1.33 | — | — | |
| DCT-based Graph Convolutional Networktraining space=angles2019.08 | 1.12 | 0.65 | 0.67 | 0.76 | 0.87 | 1.57 | 1.33 | 1.7 | 0.9 | 1.27 | — | — | |
| convSeq2Seq2019.08 | 1.24 | — | 0.92 | — | — | 1.62 | — | 1.86 | — | 1.41 | — | — | |
| SGSN2023.08 | 68.2 | — | — | 56 | 31.6 | 58.8 | 69.1 | 96.5 | 58.93 | 81.13 | 79 | 101 | |
| ResChunk2023.08 | 70.54 | — | — | 51.78 | 42.93 | 63.25 | 68.41 | 95.36 | 58.75 | 81.95 | 71.87 | 98.65 | |
| MSR-GCN2023.08 | 77.11 | — | — | 52.54 | 49.45 | 71.64 | 88.59 | 117.59 | 65.44 | 91.73 | 71.18 | 100.59 | |
| TrajDep2023.08 | 77.75 | — | — | 53.39 | 50.74 | 72.62 | 91.61 | 121.53 | 66.69 | 93.42 | 71.01 | 101.79 | |
| DMGNN2023.08 | 86.66 | — | — | 58.11 | 50.85 | 72.15 | 81.9 | 138.32 | 75.23 | 103.22 | 110.06 | 115.75 | |
| Seq2seq2023.08 | 100.2 | — | — | 79.87 | 94.83 | 137.44 | 121.3 | 161.7 | 101.51 | 137.96 | 110.05 | 152.48 |