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The Ego-Exo4D-HM dataset provides a critical resource for embodied AI by offering dense 4D human motion reconstructions derived from synchronized egocentric and exocentric video captures. This release addresses the limitation of sparse annotations in the original dataset, enabling advanced research in skill learning, procedural activity understanding, and robotic manipulation. By supplying both the data and the reconstruction pipeline, this work directly advances the field of computer vision and robotics for AI agents interacting with humans.
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Ego-Exo4D Human Meshes Dataset: 4D Human Motion Reconstruction for Ego-Exo Captures

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arXiv:2609.30187v1 Announce Type: cross Abstract: Ego-Exo4D is a large-scale dataset providing synchronized egocentric and multi-view exocentric video, a rich resource for skill learning and assessment, procedural activity understanding, and embodied AI. However, the dataset ships with only sparse 3D human pose annotations, and reconstructing dense human motion from its multi-view captures is nontrivial. To this end, we present Ego-Exo4D-HM, a large-scale dataset of 4D human motion reconstructions for Ego-Exo4D's captures, and release the accompanying reconstruction pipeline. The code, dataset, and documentation can be found at https://abhiram824.github.io/egoexo4d_human_meshes.

Abstract:Ego-Exo4D is a large-scale dataset providing synchronized egocentric and multi-view exocentric video, a rich resource for skill learning and assessment, procedural activity understanding, and embodied AI. However, the dataset ships with only sparse 3D human pose annotations, and reconstructing dense human motion from its multi-view captures is nontrivial. To this end, we present Ego-Exo4D-HM, a large-scale dataset of 4D human motion reconstructions for Ego-Exo4D's captures, and release the accompanying reconstruction pipeline. The code, dataset, and documentation can be found at this https URL.

Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)

arXiv:2609.30187 [cs.CV]

(or arXiv:2609.30187v1 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2609.30187 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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