UniEdge combines a unified spatial-temporal graph, edge-to-edge graph convolution, and a transformer encoder predictor to achieve state-of-the-art ADE/FDE on ETH, UCY, and SDD.
Reciprocal twin networks for pedestrian motion learning and future path prediction,
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Unified Spatial-Temporal Edge-Enhanced Graph Networks for Pedestrian Trajectory Prediction
UniEdge combines a unified spatial-temporal graph, edge-to-edge graph convolution, and a transformer encoder predictor to achieve state-of-the-art ADE/FDE on ETH, UCY, and SDD.