LIMMT: Less is More for Motion Tracking

Yu Guan, Zekun Qi, Chenghuai Lin, Xuchuan Chen, Dairu Liu, Zhang, Jilong Wang, Xinqiang Yu, He Wang, Li Yi

LIMMT: Less is More for Motion Tracking: 15 upvotes on Hugging Face Daily Papers, #16 of 48 papers on 2026-06-08. Day-by-day upvote history.

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 for Motion Tracking). To our knowledge, this is the first data-centric study for physics-based humanoid motion tracking. We go beyond simply removing low-quality and erroneous clips, but define motion data quality through three dimensions: physics feasibility, diversity, and complexity. We show that even training with under 3% of AMASS yields better tracking performance than training with the full dataset. We further conduct data cleaning on the estimated web-sourced mocap data. Extensive experiments and analyses validate the effectiveness of our framework.

Paper page on Hugging Face · arXiv

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