A compositional flow-matching model learns a dictionary of motion primitives with length masks and assembles them via sparse binary placement with geometric continuity losses, reporting SOTA results on two embodied trajectory datasets.
Fast inference and update of probabilistic density estimation on trajectory prediction
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Sparse Compositional Flow Matching by geometric assembly from motion primitives
A compositional flow-matching model learns a dictionary of motion primitives with length masks and assembles them via sparse binary placement with geometric continuity losses, reporting SOTA results on two embodied trajectory datasets.