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arXiv · 2607.27865

Learning to Understand Body Language from Flight through Robust 3D Avatar Placing

Dragos Costea·Alina Marcu·Cristina Lazar·Marius Leordeanu
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Abstract

Perceiving human motion and intent at long range is a prerequisite for socially intelligent aerial robots, yet the data to learn it barely exists. We introduce Drones2BodyLanguage, a dataset grounding human motion in real UAV footage: avatars manifesting ten communicative intents are placed into unmodified 4K drone scenes with metrically correct position, scale and orientation, maintained over hundreds of frames of camera motion. Enabling it is a lightweight geometric world model of the local scene - semantically selected anchors lifted to 3D through streaming monocular depth - in which a placement point is predicted as an affine anchor combination with provably rigid-invariant weights, and re-rendered under an SVD-fitted ground rotation. Across twelve architectures on scene- and motion-disjoint splits, training on placed data lifts mean intent accuracy by a wide margin for real, retargeted and generated motion alike, with gains confirmed on two in-the-wild scenes.

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Dragos Costea, Alina Marcu, Cristina Lazar, Marius Leordeanu. 2026-07-30. Learning to Understand Body Language from Flight through Robust 3D Avatar Placing. https://arxiv.org/abs/2607.27865

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