用高质量运动数据提升人体动作追踪效果，仅需3%数据就超越全量训练。
LIMMT: Less is More for Motion Tracking
- 从物理合理性、多样性、复杂度三维度定义动作数据质量
- 仅用AMASS数据的3%即实现优于全量数据的追踪性能
- 适合关注数据质量与效率的物理仿真与动捕研究者
我们认为高质量的运动数据能在训练初期引导追踪策略走向更优的优化路径。本文提出LIMMT（Less Is More for Motion Tracking），首次针对基于物理的人形动作追踪开展数据中心研究。我们不仅剔除低质量与错误片段，更从物理可行性、多样性与复杂度三个维度定义运动数据质量。实验表明，即使仅使用AMASS数据的不足3%，其追踪性能也优于使用完整数据集的训练结果。此外，我们对网络获取的估计动作捕捉数据进行了清洗。大量实验与分析验证了该框架的有效性。
原文摘要 · Abstract (English)
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.
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