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arXiv · 2606.02962

Hand Trajectory Fusion for Egocentric Natural Language Query Grounding

Enmin Zhong·Carlos R. del-Blanco·Fernando Jaureguizar·Narciso García
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Abstract

Egocentric Natural Language Query (NLQ) grounding asks a model to localize, in a long first-person video, the temporal interval that answers a free-form text query. Existing methods fuse video appearance with the query but ignore hand motion, despite the fact that roughly 41% of Ego4D NLQ queries are answered at a moment of hand--object manipulation or their immediate outcomes.We propose a hand-trajectory encoder for converting a sequence of hand skeletons into highly-semantic hand kinematic features, which are then aligned and combined with pretrained video--text features through a cross-attention fusion strategy with adaptive gating. On the Ego4D NLQ v2 validation split, the clearest gains appear for Hand-Object Interaction queries (+2.54 R1@IoU=0.3) and Quantity/State queries (+4.32 R1@IoU=0.3), indicating that hand trajectory provides grounding cues beyond appearance alone.

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Enmin Zhong, Carlos R. del-Blanco, Fernando Jaureguizar, Narciso García. 2026-06-01. Hand Trajectory Fusion for Egocentric Natural Language Query Grounding. https://arxiv.org/abs/2606.02962

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