AI

PAMI: Part Anchored Motion for Text to Human-Object Interaction Generation

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 08/10/2026, 16:18:25 UTC.

Briefings

A new framework called PAMI improves human-object interaction generation by using body-part anchors to vote for object motion, reducing drift and penetration. It achieves 14.5% higher contact recall than previous methods on the InterAct dataset. The system uses a coarse-to-fine hierarchy combining long-range and short-range sensors.

Key points

• PAMI uses body-part anchors to vote for object motion, expressing object motion relative to multiple body-part anchors.
• PAMI achieves 14.5% higher contact recall than the previous state of the art on the InterAct dataset.

Context

Text-conditioned full-body human-object interaction generation requires synthesizing human motion and object trajectories that match input text while remaining precisely coordinated over time. Most methods represent the human and object as separate trajectories and predict the global human-object couplings, often yielding object drift, missed contact, and penetration.

Limitations

The study uses the InterAct dataset for experiments and ablations. Results are based on a single published paper from September 2026. Long-term generalizability beyond the InterAct dataset remains unverified.

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