| title | Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking |
|---|---|
| authors | Zekun Qi, Xuchuan Chen, Dairu Liu, Chenghuai Lin, Yunrui Lian, Sikai Liang, Zhikai Zhang, Yu Guan, Jilong Wang, Wenyao Zhang, Xinqiang Yu, He Wang, Li Yi |
| date | 2026-06-02 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2606.03985 |
| affiliation | galbot |
| upvotes | 41 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf code |
| abstract | We introduce Humanoid-GPT, a GPT-style Transformer with causal attention trained on a billion-scale motion corpus for whole-body control. Unlike prior shallow MLP trackers constrained by scarce data and an agility-generalization trade-off, Humanoid-GPT is pre-trained on a 2B-frame retargeted corpus that unifies all major mocap datasets with large-scale in-house recordings. Scaling both data and model capacity yields a single generative Transformer that tracks highly dynamic behaviors while achieving unprecedented zero-shot generalization to unseen motions and control tasks. Extensive experiments and scaling analyses show that our model establishes a new performance frontier, demonstrating robust zero-shot generalization to unseen tasks while simultaneously tracking highly dynamic and complex motions. |
cite
@misc{qi2026humanoid,
title={Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking},
author={Zekun Qi and Xuchuan Chen and Dairu Liu and Chenghuai Lin and Yunrui Lian and Sikai Liang and Zhikai Zhang and Yu Guan and Jilong Wang and Wenyao Zhang and Xinqiang Yu and He Wang and Li Yi},
year={2026},
eprint={2606.03985},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2606.03985}
}