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Proposes physical self-supervised learning, an autoencoder paradigm for label-free IMU sensing that replaces the neural decoder with a physics-based decoder, achieving up to 5x error reduction in tracking and motion capture tasks without manual labels.
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.