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This paper introduces LIMMT, a data-centric study showing that training with high-quality, minimal subsets of motion data (under 3% of AMASS) outperforms using the full dataset for physics-based humanoid motion tracking, defining motion data quality through physics feasibility, diversity, and complexity.
Humanoid-GPT is a GPT-style Transformer pre-trained on a billion-scale motion corpus, achieving zero-shot generalization for whole-body motion tracking across unseen motions and tasks.