发表机构
Hefei University of Technology（合肥工业大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对手语数据收集昂贵和遥操作扩展性差的问题，提出HumanoidCSL-20K数据集与基准，通过可追溯的转换和评估协议，实现从视频到人形机器人手语轨迹的可靠生成。
AI 中文摘要
手语数据收集成本高昂，遥操作扩展性差，这促使人们重用大型视频语料库。人形机器人手语需要将视频推导出的人类运动转换为机器人轨迹，同时保留语言运动线索。拟合、人体运动修复、重定向、机器人几何修复和控制产生的误差难以仅从最终轨迹中分离。我们提出了HumanoidCSL-20K，一个包含20,648个句子级中文手语序列的数据集和基准，每个序列有四个对齐版本：源、修复后的人体运动、直接机器人参考和几何修复后的机器人参考。基于观测的局部人体运动修复、全机器人几何修复和跨表示来源追踪使每次转换都可追溯。配对评估衡量人体运动连续性和内容保留、机器人参考可行性和物理执行。一个特定于手语的运动学参考协议在完整计划运动上评分手形、位置、手掌方向和手间关系。全语料结果显示修复后异常手臂/手部步骤减少，手间和手-身体穿透减少。对照实验将参考可学习性与课程效应分离，而组件分数暴露了剩余的执行误差。
英文摘要
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.
