LIMMT: Less is More for Motion Tracking
Summary
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.
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Paper page - LIMMT: Less is More for Motion Tracking
Source: https://huggingface.co/papers/2606.06953
Abstract
Training with high-quality motion data improves tracking policy optimization trajectories, with minimal data subsets outperforming full datasets in physics-based humanoid motion tracking.
We argue that high-quality motion data can steer tracking policies toward better optimization trajectories early in training. In this work, we introduce LIMMT (Less Is More forMotion Tracking). To our knowledge, this is the firstdata-centric studyforphysics-based humanoid motion tracking. We go beyond simply removing low-quality and erroneous clips, but definemotion data qualitythrough three dimensions:physics feasibility,diversity, andcomplexity. We show that even training with under 3% ofAMASSyields better tracking performance than training with the full dataset. We further conductdata cleaningon the estimated web-sourcedmocap data. Extensive experiments and analyses validate the effectiveness of our framework.
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