LIMMT：运动跟踪中的少即是多

摘要

我们认为，高质量的运动数据能够在训练初期引导跟踪策略走向更优的优化轨迹。本文提出了LIMMT（少即是多：运动跟踪）框架。据我们所知，这是首个以数据为中心的基于物理的人形运动跟踪研究。我们不仅剔除低质量及错误片段，更从物理可行性、多样性和复杂性三个维度定义运动数据质量。实验表明，即使仅用不到3%的AMASS数据训练，其跟踪性能也优于使用完整数据集的效果。此外，我们对网络来源的动作捕捉数据进行了数据清洗。大量实验与分析验证了本框架的有效性。

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.