追跡可能な人間からヒューマノイドへの手話ベンチマーク
Traceable Human-to-Humanoid Sign Language Benchmarking
中国手話の大規模データセットHumanoidCSL-20Kを構築し、人間の動作からヒューマノイドへの変換過程を追跡可能にしたベンチマークを提案。
著者: Ao Liu, Shengeng Tang, Lechao Cheng, Yanbin Hao, Bingkun Bao, Richang Hong
分類: cs.RO
原文アブストラクト
Sign data collection is costly, and teleoperation scales poorly, motivating reuse of large video corpora. Humanoid signing requires converting video-derived human motion into robot trajectories while preserving linguistic motion cues. Errors from fitting, human-motion repair, retargeting, robot geometry repair, and control are hard to separate from the final trajectory alone. We introduce HumanoidCSL-20K, a dataset and benchmark of 20,648 sentence-level Chinese Sign Language sequences, each with four aligned versions: the source, the repaired human motion, the direct robot reference, and the geometry-repaired robot reference. Observation-supported local human-motion repair, full-robot geometry repair, and cross-representation provenance make each transformation traceable. Paired evaluations measure human-motion continuity and content preservation, robot-reference feasibility, and physical execution. A sign-specific kinematic-reference protocol scores handshape, location, palm orientation, and inter-hand relation over the full planned motion. Full-corpus results show fewer abnormal arm / hand steps and less inter-hand and hand-body penetration after repair. Control experiments separate reference learnability from curriculum effects, while component scores expose remaining execution errors.