LIMMT：少即是多於運動追蹤

摘要

我們認為，高品質的動作數據能在訓練初期引導追蹤策略走向更優的優化軌跡。在本研究中，我們提出了 LIMMT（少即是多的動作追蹤）。據我們所知，這是首個以數據為核心、針對基於物理的人形動作追蹤的研究。我們不僅僅是移除低品質與錯誤的片段，而是從三個維度定義動作數據品質：物理可行性、多樣性與複雜度。我們證明，即使僅使用 AMASS 資料集不到 3% 的數據進行訓練，其追蹤表現仍優於使用完整數據集訓練的結果。此外，我們還對從網路來源估計的動作捕捉數據進行了數據清理。廣泛的實驗與分析驗證了我們框架的有效性。

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.