发表机构
The University of Hong Kong; Shenzhen Loop Area Institute; TranscEngram（香港大学; 深圳河套学院; TranscEngram）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
YOCO是一种快速少样本、无需微调的校准框架，通过条件超网络预测LoRA风格更新，提升灵巧遥操作的校准效率与手部状态估计质量。
AI 中文摘要
灵巧遥操作需要可靠的人手状态估计。然而，常见的低成本动作捕捉手套和无标记跟踪器往往存在偏差，这些偏差会因用户、手套贴合度和记录会话的不同而变化，从而降低重定向和演示的质量。我们提出YOCO，这是一种快速的少样本、无需微调的校准框架，可从少量成对的原始姿态和目标姿态中校正有偏差的手部姿态流。YOCO不会为每个操作员或会话优化单独的模型，而是基于成对示例对校准超网络（HyperNet）进行条件设置，并为冻结的MANO手部估计模块预测LoRA风格的更新，将每个用户的校准转变为轻量级的前馈适应步骤，同时保留MANO的几何先验和紧凑估计器的效率。我们在InterHand2.6M上使用合成漂移增强训练YOCO，并在增强的InterHand序列、离线真实手套数据和灵巧遥操作任务上进行评估。在这些设置中，与未校准输入和标准校准基线相比，YOCO提高了校准效率、手部状态估计质量和遥操作性能。
英文摘要
Dexterous teleoperation requires reliable human-hand state estimations. However, common low-cost motion-capture gloves and markerless trackers often exhibit biases that vary across users, glove fit, and recording sessions, degrading retargeting and demonstration quality. We present YOCO, a fast few-shot, fine-tuning-free calibration framework that corrects biased hand-pose streams from a small set of paired raw and target poses. Instead of optimizing a separate model for every operator or session, YOCO conditions a calibration HyperNet on the paired examples and predicts LoRA-style updates for a frozen MANO hand-estimation module, turning per-user calibration into a lightweight feed-forward adaptation step while preserving the geometric prior of MANO and the efficiency of a compact estimator. We train YOCO with synthetic drift augmentations on InterHand2.6M and evaluate on augmented InterHand sequences, offline real glove data, and dexterous teleoperation tasks. Across these settings, YOCO improves calibration efficiency, hand-state estimation quality and teleoperation performance compared with uncalibrated input and standard calibration baselines.
CommentsCoRL 2026
