Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking
📰 ArXiv cs.AI
Learn how Humanoid-GPT scales data and structure for zero-shot motion tracking using a GPT-style Transformer with causal attention
Action Steps
- Train a GPT-style Transformer with causal attention on a large-scale motion corpus
- Pre-process motion data by retargeting and unifying multiple datasets
- Evaluate the performance of Humanoid-GPT on zero-shot motion tracking tasks
- Compare the results with prior shallow MLP trackers
- Apply Humanoid-GPT to real-world applications such as robotics or computer vision
Who Needs to Know This
Researchers and engineers working on motion tracking and whole-body control can benefit from this article, as it introduces a new approach to scaling data and model capacity for improved performance
Key Insight
💡 Scaling both data and model capacity can improve performance in motion tracking tasks
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🤖 Humanoid-GPT: Scaling data and structure for zero-shot motion tracking with a GPT-style Transformer! 🚀
Key Takeaways
Learn how Humanoid-GPT scales data and structure for zero-shot motion tracking using a GPT-style Transformer with causal attention
Full Article
Title: Humanoid-GPT: Scaling Data and Structure for Zero-Shot Motion Tracking
Abstract:
arXiv:2606.03985v1 Announce Type: cross 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 Transforme
Abstract:
arXiv:2606.03985v1 Announce Type: cross 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 Transforme
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