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EgoExoMoCap: Distributed Ego-Exo Human Motion Capture

Research Multimodal & Generative

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TL;DR - EgoExoMoCap combines egocentric and exocentric multimodal signals from multiple smart-glasses wearers to reconstruct human motion without conventional camera rigs or mocap suits. This could enable scalable motion-data collection for embodied AI and VR/AR.

  • Jointly estimates the wearer’s motion and the movements of nearby people.
  • Uses head and optional wrist tracking to recover global 3D motion.
  • Incorporates context-aware DINOv3 image features for robustness to noise and occlusion.
  • Experiments on two in-the-wild datasets demonstrate reconstruction in challenging scenarios.

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EgoExoMoCap: Distributed Ego-Exo Human Motion Capture

arXiv cs.CV Jiaxi Jiang, Bharat Lal Bhatnagar, Nan Yang, Lingni Ma, Sebastian Starke, Robin Kips, Nadine Bertsch, Christian Holz, Federica Bogo 2026-07-17 arXiv:2607.15868
Public signals Hugging Face upvotes 0
Providers: Hugging Face · Upvotes 0 OpenAlex · N/A Publisher · N/A Semantic Scholar · N/A X · N/A Fetched 2026-08-18 14:39:26.574293 UTC

TL;DR - EgoExoMoCap combines egocentric and exocentric multimodal signals from multiple smart-glasses wearers to reconstruct human motion without conventional camera rigs or mocap suits. This could enable scalable motion-data collection for embodied AI and VR/AR.

  • Jointly estimates the wearer’s motion and the movements of nearby people.
  • Uses head and optional wrist tracking to recover global 3D motion.
  • Incorporates context-aware DINOv3 image features for robustness to noise and occlusion.
  • Experiments on two in-the-wild datasets demonstrate reconstruction in challenging scenarios.
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