Human-as-Humanoid: Enabling Zero-Shot Humanoid Learning from Ego-Exo Human Videos with Human-Aligned Embodiments

Xiaopeng Lin; Ruoqi Yang; Shijie Lian; Zhaolong Shen; Bin Yu; Changti Wu; Haibao Liu; Yuxiang Zhang; Hong Li; Qiyuan Su; Haochen Liu; Xuguo He; Yukun Shi; Cong Huang; Zhirui Zhang; Bojun Cheng; Kai Chen

arXiv · 2606.32009

Human-as-Humanoid: Enabling Zero-Shot Humanoid Learning from Ego-Exo Human Videos with Human-Aligned Embodiments

Xiaopeng Lin·Ruoqi Yang·Shijie Lian·Zhaolong Shen·Bin Yu·Changti Wu·Haibao Liu·Yuxiang Zhang·Hong Li·Qiyuan Su·Haochen Liu·Xuguo He·Yukun Shi·Cong Huang·Zhirui Zhang·Bojun Cheng·Kai Chen

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Abstract

Vision-language-action (VLA) models across robot embodiments require high-quality observation--action supervision to learn deployable action distributions, yet scaling such robot data remains difficult, especially for high-DoF humanoids. Teleoperation provides controller-aligned supervision, while human egocentric videos capture diverse bimanual manipulation but do not directly provide executable robot actions. We introduce Human-as-Humanoid, a human-to-humanoid supervision framework that enables near-real-time human-centric action generation, making human demonstrations usable for high-DoF humanoid VLA training by jointly aligning the robot embodiment, the sensing setup, and the action-label interface. Built on PrimeU, a human-aligned 60-DoF upper-body humanoid, Human-as-Humanoid uses synchronized ego-exo videos to pair deployment-aligned egocentric observations with exocentric motion recovery, retargets the recovered human motion through staged Inverse Kinematics (IK) into controller-aligned 60-DoF action chunks, and trains the VLA model with Forward Kinematics (FK)-aware supervision to preserve wrist and fingertip task-space geometry. This converts large-scale human demonstrations from visual observations into executable observation--action supervision for the target humanoid. Experiments validate the conversion chain at the motion-recovery, robot-action-space, and real-robot deployment levels. Human-as-Humanoid yields a 4.8--7.2x raw demonstration-throughput gain over humanoid teleoperation in our data-collection analysis, and on several downstream tasks, policies post-trained only with the converted human labels generalize to real-robot deployment without target-task robot demonstrations. The official project website is available at https://zgc-embodyai.github.io/Human-as-Humanoid.

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Xiaopeng Lin, Ruoqi Yang, Shijie Lian, Zhaolong Shen, Bin Yu, Changti Wu, Haibao Liu, Yuxiang Zhang, Hong Li, Qiyuan Su, Haochen Liu, Xuguo He, Yukun Shi, Cong Huang, Zhirui Zhang, Bojun Cheng, Kai Chen. 2026-06-30. Human-as-Humanoid: Enabling Zero-Shot Humanoid Learning from Ego-Exo Human Videos with Human-Aligned Embodiments. https://arxiv.org/abs/2606.32009

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