HumanTracker: 人間の知覚に合致した包括的なモーション追跡ベンチマーク
HumanTracker: Towards Comprehensive and Human-Aligned Motion Tracking Benchmark
ヒューマノイドのモーション追跡評価を人間の知覚に合わせてスケーラブルにするため、約153時間の光学モーションデータからなるベンチマークと、選好に基づく評価指標HumanScoreを提案した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Dairu Liu, Zekun Qi, Jiayu Zeng, Ruixi Yu, Yu Guan, Yintianrun Zhang, Xuchuan Chen, Sikai Liang, Zekai Li, Chenghuai Lin, Xinqiang Yu, Wenyao Zhang, He Wang, Li Yi
分類: cs.RO, cs.AI, cs.CV
原文アブストラクト
Humanoid motion tracking is central to teleoperation and whole-body imitation, yet evaluation often disagrees with what people perceive in videos. Kinematic errors average per-frame pose differences but miss the physical artifacts that matter most, particularly unstable support and incorrect contacts such as foot skating and mistimed touch-downs. Meanwhile, widely used test suites are small and lack the diversity needed to stress contact-rich, long-horizon behaviors. We introduce HumanTracker to make humanoid tracking evaluation both perceptually aligned and scalable. The HumanTracker benchmark contains approximately 153 hours of optical motion trajectories from multiple professional performers, organized into four motion families with text labels for fine-grained diagnosis. We further propose HumanScore, a preference-aligned metric trained on 12K motion pairs containing 24K motions. Across representative state-of-the-art trackers, HumanScore better predicts human preferences and reveals contact and stability failures that kinematic metrics often miss.