Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking

• URL: http://arxiv.org/abs/2606.03985v1
• Date: Tue, 02 Jun 2026 17:59:05 GMT
• Title: Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking
• Abstract summary: We introduce Humanoid-GPT, a GPT-style Transformer with causal attention trained on a billion-scale motion corpus for whole-body control.<n>Humanoid-GPT is pre-trained on a 2B-frame retargeted corpus that unifies all major mocap datasets with large-scale in-house recordings.
• Score: 21.334872673132594
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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