Trajectory Prediction on UNIV
0.12ADEScePT
Evaluation results
| Method | Links | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| ScePTsampling=Best-of-202022.06 | 0.12 | 0.65 | — | — | — | — | — | — | — | — | |
| ScePTmode=most likely2022.06 | 0.19 | 1.19 | — | — | — | — | — | — | — | — | |
| Expert Traj2022.03 | 0.19 | 0.44 | — | — | — | — | — | — | — | — | |
| Trajectron++sampling=Best-of-202022.06 | 0.2 | 0.44 | — | — | — | — | — | — | — | — | |
| Trajectron++2022.03 | 0.2 | 0.44 | 1.73 | 0.228 | -1.08 | — | — | — | — | — | |
| NPSN-NCE-Trajectron++sampling_strategy=NPSN2022.03 | 0.23 | 0.41 | — | — | — | — | — | — | — | — | |
| NPSN-SGCNsampling_strategy=NPSN2022.03 | 0.23 | 0.39 | — | — | — | — | — | — | — | — | |
| NPSN-Trajectron++sampling_strategy=NPSN2022.03 | 0.27 | 0.44 | — | — | — | — | — | — | — | — | |
| NCE-Trajectron++2022.03 | 0.28 | 0.54 | — | — | — | — | — | — | — | — | |
| NPSN-STGCNNsampling_strategy=NPSN2022.03 | 0.28 | 0.44 | — | — | — | — | — | — | — | — | |
| NPSN-PECNetsampling_strategy=NPSN2022.03 | 0.29 | 0.44 | — | — | — | — | — | — | — | — | |
| Trajectron++2022.03 | 0.3 | 0.55 | — | — | — | — | — | — | — | — | |
| Social-Implicit2022.03 | 0.31 | 0.6 | 1.65 | 0.148 | 1.67 | — | — | — | — | — | |
| S-ATTNmode=most likely2022.06 | 0.33 | 3.92 | — | — | — | — | — | — | — | — | |
| PECNet2022.03 | 0.34 | 0.56 | — | — | — | — | — | — | — | — | |
| SGCN2022.03 | 0.37 | 0.67 | — | — | — | — | — | — | — | — | |
| LMTraj-ZEROFoundation Model=GPT-4, zero-shot=true2024.03 | 0.37 | 0.77 | — | — | — | — | — | — | — | — | |
| MIDMode=Deterministic2026.05 | 0.43 | 1.01 | — | — | — | — | — | — | — | — | |
| Social-STGCNN2022.03 | 0.44 | 0.79 | — | — | — | — | — | — | — | — | |
| MATFsampling=Best-of-202022.06 | 0.44 | 0.91 | — | — | — | — | — | — | — | — | |
| Trajectron++mode=most likely2022.06 | 0.44 | 1.17 | — | — | — | — | — | — | — | — | |
| S-STGCNN2022.03 | 0.44 | 0.79 | 3.31 | 0.06 | 5.65 | — | — | — | — | — | |
| FEP-DiffMode=Deterministic2026.05 | 0.46 | 1 | — | — | — | — | — | — | — | — | |
| EqMotionMode=Deterministic2026.05 | 0.5 | 1.1 | — | — | — | — | — | — | — | — | |
| NPSN-Causal-STGATsampling_strategy=NPSN2022.03 | 0.51 | 1.09 | — | — | — | — | — | — | — | — | |
| SocialVAEMode=Deterministic2026.05 | 0.51 | 1.11 | — | — | — | — | — | — | — | — | |
| STGAT2022.03 | 0.52 | 1.1 | — | — | — | — | — | — | — | — | |
| Causal-STGAT2022.03 | 0.52 | 1.1 | — | — | — | — | — | — | — | — | |
| Linearzero-shot=true2024.03 | 0.52 | 1.17 | — | — | — | — | — | — | — | — | |
| NPSN-STGATsampling_strategy=NPSN2022.03 | 0.53 | 1.13 | — | — | — | — | — | — | — | — | |
| SoPhiesampling=Best-of-202022.06 | 0.54 | 1.24 | — | — | — | — | — | — | — | — | |
| NPSNMode=Deterministic2026.05 | 0.54 | 1.14 | — | — | — | — | — | — | — | — | |
| Kalman filterzero-shot=true2024.03 | 0.55 | 1.2 | — | — | — | — | — | — | — | — | |
| LMTraj-ZEROFoundation Model=GPT-3.5, zero-shot=true2024.03 | 0.56 | 0.98 | — | — | — | — | — | — | — | — | |
| GP-GraphMode=Deterministic2026.05 | 0.56 | 1.19 | — | — | — | — | — | — | — | — | |
| Trajectron++Mode=Deterministic2026.05 | 0.56 | 1.45 | — | — | — | — | — | — | — | — | |
| SingularTrajMode=Deterministic2026.05 | 0.57 | 1.12 | — | — | — | — | — | — | — | — | |
| S-GANsampling=Best-of-202022.06 | 0.6 | 1.26 | — | — | — | — | — | — | — | — | |
| S-GAN2022.03 | 0.6 | 1.26 | 2.37 | 0.44 | 2.03 | — | — | — | — | — | |
| EigenTrajectoryMode=Deterministic2026.05 | 0.65 | 1.31 | — | — | — | — | — | — | — | — | |
| S-LSTMmode=most likely2022.06 | 0.67 | 1.4 | — | — | — | — | — | — | — | — | |
| AgentFormerMode=Deterministic2026.05 | 0.67 | 1.42 | — | — | — | — | — | — | — | — | |
| NPSN-SGANsampling_strategy=NPSN2022.03 | 0.71 | 1.43 | — | — | — | — | — | — | — | — | |
| Social-GAN2022.03 | 0.76 | 1.52 | — | — | — | — | — | — | — | — | |
| JACoP2026.05 | 0.82 | 1.7 | — | — | — | — | — | — | — | 2.1 | |
| AutoTrajectoryzero-shot=true2024.03 | 0.89 | 1.45 | — | — | — | — | — | — | — | — | |
| GP-Graph2026.05 | 0.91 | 1.31 | — | — | — | — | — | — | — | 1.65 | |
| MoFlowMode=Deterministic2026.05 | 0.93 | 1.77 | — | — | — | — | — | — | — | — | |
| AgentFormer2026.05 | 1.01 | 2.26 | — | — | — | — | — | — | — | 1.72 | |
| AgentFormer + JACoP Aligner2026.05 | 1.05 | 2.26 | — | — | — | — | — | — | — | 2.93 | |
| SingularTrajectory2026.05 | 1.12 | 2.36 | — | — | — | — | — | — | — | 1.77 | |
| Stopzero-shot=true2024.03 | 1.36 | 2.47 | — | — | — | — | — | — | — | — | |
| EqMotion2026.05 | 2.3 | 5.39 | — | — | — | — | — | — | — | 1.6 | |
| Agentformer2023.03 | — | — | — | — | — | 0.25 | 0.45 | — | — | — | |
| AgentFormer2024.07 | — | — | — | — | — | 0.25 | 0.45 | — | — | — | |
| AgentFormerVenue=ICCV’21, K=20, Observability=agent-centric2026.05 | — | — | — | — | — | 0.25 | 0.45 | — | — | — | |
| DD-MDNEvaluation Protocol=Momentary Observation, Input Horizon=2, K=202026.02 | — | — | — | — | — | — | — | 0.24 | 0.42 | — | |
| EigenTrajEvaluation Protocol=Momentary Observation, Input Horizon=2, K=202026.02 | — | — | — | — | — | — | — | 0.25 | 0.44 | — | |
| EigenTrajectory2024.07 | — | — | — | — | — | 0.24 | 0.43 | — | — | — | |
| EigenTrajectoryVenue=ICCV’23, K=20, Observability=agent-centric2026.05 | — | — | — | — | — | 0.24 | 0.43 | — | — | — | |
| EqMotionVenue=CVPR’23, K=20, Observability=agent-centric2026.05 | — | — | — | — | — | 0.23 | 0.43 | — | — | — | |
| FEP-DiffVenue=-, K=20, Observability=agent-centric2026.05 | — | — | — | — | — | 0.22 | 0.39 | — | — | — | |
| GP-GraphVenue=ECCV’22, K=20, Observability=agent-centric2026.05 | — | — | — | — | — | 0.24 | 0.42 | — | — | — | |
| GroupnetVenue=CVPR’22, K=20, Observability=agent-centric2026.05 | — | — | — | — | — | 0.26 | 0.49 | — | — | — | |
| GroupNet2023.03 | — | — | — | — | — | 0.26 | 0.49 | — | — | — | |
| LED2023.03 | — | — | — | — | — | 0.26 | 0.43 | — | — | — | |
| LED2024.07 | — | — | — | — | — | 0.26 | 0.43 | — | — | — | |
| MemoNet2023.03 | — | — | — | — | — | 0.24 | 0.43 | — | — | — | |
| MemoNet2024.07 | — | — | — | — | — | 0.24 | 0.43 | — | — | — | |
| MID2023.03 | — | — | — | — | — | 0.22 | 0.45 | — | — | — | |
| MID2024.07 | — | — | — | — | — | 0.22 | 0.45 | — | — | — | |
| MIDEvaluation Protocol=Momentary Observation, Input Horizon=2, K=202026.02 | — | — | — | — | — | — | — | 0.3 | 0.56 | — | |
| MIDVenue=CVPR’22, K=20, Observability=agent-centric2026.05 | — | — | — | — | — | 0.24 | 0.47 | — | — | — | |
| MoFlowVenue=CVPR’25, K=20, Observability=agent-centric2026.05 | — | — | — | — | — | 0.25 | 0.41 | — | — | — | |
| NMMP2023.03 | — | — | — | — | — | 0.52 | 1.11 | — | — | — | |
| NPSN2023.03 | — | — | — | — | — | 0.22 | 0.41 | — | — | — | |
| NPSN2024.07 | — | — | — | — | — | 0.23 | 0.39 | — | — | — | |
| NPSNVenue=CVPR’22, K=20, Observability=agent-centric2026.05 | — | — | — | — | — | 0.23 | 0.39 | — | — | — | |
| PCCSNet2024.07 | — | — | — | — | — | 0.29 | 0.6 | — | — | — | |
| PECNet2023.03 | — | — | — | — | — | 0.35 | 0.6 | — | — | — | |
| PECNet2024.07 | — | — | — | — | — | 0.35 | 0.6 | — | — | — | |
| PPT2024.07 | — | — | — | — | — | 0.22 | 0.4 | — | — | — | |
| SingTrajEvaluation Protocol=Momentary Observation, Input Horizon=2, K=202026.02 | — | — | — | — | — | — | — | 0.24 | 0.43 | — | |
| SingularTrajVenue=CVPR’24, K=20, Observability=agent-centric2026.05 | — | — | — | — | — | 0.25 | 0.44 | — | — | — | |
| Social-GAN2023.03 | — | — | — | — | — | 0.76 | 1.52 | — | — | — | |
| Social-GAN2024.07 | — | — | — | — | — | 0.76 | 1.52 | — | — | — | |
| SocialVAE2024.07 | — | — | — | — | — | 0.21 | 0.36 | — | — | — | |
| SocialVAEVenue=ECCV’22, K=20, Observability=agent-centric2026.05 | — | — | — | — | — | 0.22 | 0.37 | — | — | — | |
| STAR2023.03 | — | — | — | — | — | 0.31 | 0.62 | — | — | — | |
| STAR2024.07 | — | — | — | — | — | 0.31 | 0.62 | — | — | — | |
| Trajectron++2023.03 | — | — | — | — | — | 0.3 | 0.54 | — | — | — | |
| Trajectron++Venue=ECCV’20, K=20, Observability=agent-centric2026.05 | — | — | — | — | — | 0.36 | 0.6 | — | — | — | |
| TUTR2024.07 | — | — | — | — | — | 0.23 | 0.42 | — | — | — |