Human Motion and Animation
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Abstract
Abstract
Humanoid robots require whole-body motions that adapt to scene context, task requirements, and user intent. Motion tracking reproduces specified trajectories, and humanoid vision-language-action systems provide semantic interfaces, but neither offers a scalable and interactive prior for broad full-body behavior. We introduce EgoPriMo (Egocentric Motion Prior for Humanoid Robots), a unified framework that learns such priors from egocentric human demonstrations. Given egocentric observations and a text prompt, EgoPriMo reconstructs, generates, and forecasts SMPL-based full-body motion. Language is used as a high-level control signal rather than a complete motion specification. At the core of EgoPriMo is a Triple-stream DiT that jointly models body dynamics, egocentric visual context, and text; task-conditioning masks route different tasks and missing-modality data through the same checkpoint. Experiments on Nymeria and EgoExo4D show that one checkpoint improves egocentric motion generation over UniEgoMotion while supporting reconstruction and forecasting; the generated SMPL motions can also be executed by a Unitree humanoid controller. These results indicate a practical path from scalable egocentric observations to generalizable and interactive humanoid motion priors.
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@article{Ge2026EgoPriMo,
title = {EgoPriMo: Egocentric Motion Generation for Interactive Humanoid Control},
author = {Haoyang Ge and Peng Ren and Yukun Shi and Cong Huang and Kun Li and Kai Chen},
journal = {arXiv (Cornell University)},
year = {2026},
doi = {10.48550/arxiv.2606.08495},
url = {https://doi.org/10.48550/arxiv.2606.08495}
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