# LIMMT：运动跟踪中的少即是多

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-05 08:00
- AIHOT 分数：59
- AIHOT 链接：https://aihot.virxact.com/items/cmq4qg4m600rxslt25z8ie57x
- 原文链接：https://arxiv.org/abs/2606.06953

## AI 摘要

LIMMT 提出首个以数据为中心的人形运动跟踪框架，从物理可行性、多样性和复杂度三个维度定义运动数据质量。实验表明，仅用不到 3% 的 AMASS 数据集训练，跟踪性能即优于使用完整数据集。该研究还对网络估算的动捕数据进行了清洗，验证了数据质量驱动的有效性。

## 正文

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.