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arXiv Machine Learning By Daniel Grimm, J. Marius Z\"ollner

FutureLift: Lifting Trajectory Proposals into Bidirectional Future Graphs for Motion Forecasting

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FutureLift is a refiner that transforms multimodal motion proposals into heterogeneous future graphs, linking temporal nodes to agent histories, map elements, alternative modes, and other agents’ future trajectories. Its bidirectional temporal edges enable later evidence to revise earlier segments in a single pass, producing residual coordinate and confidence corrections. Evaluated on Argoverse2 and the Waymo Open Motion Dataset, FutureLift consistently improves frozen proposals and outperforms other refiners, achieving top leaderboard rankings on the WOMD motion‑prediction benchmark without LiDAR or ensembling.

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