We refer our method as SGAN-kVP-N where kV signifies if the model was trained using variety loss (k = 1 essentially means no variety loss) and P signifies usage of our proposed pooling module. At test time we sample multiple times from the model and chose the best prediction in L2 sense for quantitative evaluation. N refers to the number of time we sample from our model during test time. We report two error metrics Average Displacement Error (ADE) and Final Displacement Error (FDE) for tpred = 8 and 12 in meters.
These results are better from what were reported in the paper. You can use print_args to get hyper-parameters used for training. For SGAN-20VP-20 we used 'global' as opposed to 'local' as done in the paper.
SGAN-20V-20
| Model | ADE8 | ADE12 | FDE8 | FDE12 |
|---|---|---|---|---|
ETH |
0.58 | 0.71 | 1.13 | 1.29 |
Hotel |
0.36 | 0.48 | 0.71 | 1.02 |
Univ |
0.33 | 0.56 | 0.70 | 1.18 |
Zara1 |
0.21 | 0.34 | 0.42 | 0.69 |
Zara2 |
0.21 | 0.31 | 0.42 | 0.64 |
SGAN-20VP-20
| Model | ADE8 | ADE12 | FDE8 | FDE12 |
|---|---|---|---|---|
ETH |
0.57 | 0.77 | 1.14 | 1.39 |
Hotel |
0.38 | 0.43 | 0.73 | 0.88 |
Univ |
0.42 | 0.75 | 0.79 | 1.50 |
Zara1 |
0.22 | 0.34 | 0.43 | 0.68 |
Zara2 |
0.24 | 0.36 | 0.48 | 0.73 |