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Spatio-Temporal Gating-Adjacency GCN for Human Motion Prediction

CVPR 2022 · Video › Video segmentation and tracking · approach: Physics-inspired motion prediction · 130 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use GAGCN instead of STSGCNHuman 3.6M · Human 3.6M average, 1000msMPJPE: 77.3 -> 72.9Helped
Use GAGCN instead of STSGCNAMASS-BMLrub · AMASS-BMLrub average, 1000msMPJPE: 45.5 -> 43.1Helped
Use GAGCN instead of STSGCN3DPW · 3DPW average, 1000msMPJPE: 42.3 -> 39.9Helped
Use GAGCN instead of STSGCNHuman 3.6M angle-based prediction · Human 3.6M average, 1000msMAE: 1.09 -> 1.02Helped
Replace stable spatial and temporal adjacency matrices with GAGCN's adaptive gating (ablation)Human 3.6M Walking Together unseen · Human 3.6M Walking Together unseen, 1000msMPJPE: 69.8 -> 51.6Helped
Train with Walking Together seen rather than unseen (ablation)GAGCN · Human 3.6M Walking Together, 1000msMPJPE: 51.6 -> 50.4No clear effect
Add gating over temporal candidate adjacency matrices (ablation)GAGCN · Human 3.6M average, 1000msMPJPE: 93.9 -> 72.9Helped
Add gating over spatial candidate adjacency matrices (ablation)GAGCN · Human 3.6M average, 1000msMPJPE: 91.1 -> 72.9Helped
Increase candidate adjacency counts from S4, T3 to S8, T6 (ablation)GAGCN · Human 3.6M average, 1000msMPJPE: 72.9 -> 76.9Hurt
Change candidate adjacency counts from S4, T3 to S3, T4 (ablation)GAGCN · Human 3.6M average, 1000msMPJPE: 72.9 -> 75.3Hurt

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