Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking
Authors: name: Zekun Qi, name: Xuchuan Chen, name: Dairu Liu, name: Chenghuai Lin, name: Yunrui Lian, name: Sikai Liang, name: Zhikai Zhang, name: Yu Guan, name: Jilong Wang, name: Wenyao Zhang, name: Xinqiang Yu, name: He Wang, name: Li Yi
Organization: galbot

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.