DexterCap: 一种经济实惠且自动化的手部物体操作捕捉系统
DexterCap: An Affordable and Automated System for Capturing Dexterous Hand-Object Manipulation
AI总结：
DexterCap是一种低成本的自动化手部物体操作捕捉系统，通过字符编码标记和自动重建流程，实现稳健的跟踪和细粒度手部-物体交互数据集DexterHand的构建。
摘要：
捕捉细粒度的手部-物体交互极具挑战性，因为紧密排列的手指会导致严重的自我遮挡，且在手内操作的运动非常微妙。现有的光学运动捕捉系统依赖于昂贵的相机设置和大量的手动后处理，而低成本的视觉方法在遮挡情况下往往准确性与可靠性较低。为了解决这些挑战，我们提出了DexterCap，一种用于灵活手内操作的低成本光学捕捉系统。DexterCap使用密集的、字符编码的标记图案以在严重的自我遮挡下实现稳健的跟踪，同时结合一个自动化重建流程，该流程需要最小的手动努力。借助DexterCap，我们引入了DexterHand数据集，涵盖了多样化的操作行为和物体，从简单的原始动作到复杂的关节物体如魔方。我们发布该数据集和代码以支持未来对灵活手部-物体交互的研究。项目网站：https://pku-mocca.github.io/Dextercap-Page/
英文摘要：
Capturing fine-grained hand-object interactions is challenging due to severe self-occlusion from closely spaced fingers and the subtlety of in-hand manipulation motions. Existing optical motion capture systems rely on expensive camera setups and extensive manual post-processing, while low-cost vision-based methods often suffer from reduced accuracy and reliability under occlusion. To address these challenges, we present DexterCap, a low-cost optical capture system for dexterous in-hand manipulation. DexterCap uses dense, character-coded marker patches to achieve robust tracking under severe self-occlusion, together with an automated reconstruction pipeline that requires minimal manual effort. With DexterCap, we introduce DexterHand, a dataset of fine-grained hand-object interactions covering diverse manipulation behaviors and objects, from simple primitives to complex articulated objects such as a Rubik's Cube. We release the dataset and code to support future research on dexterous hand-object interaction. Project website: https://pku-mocca.github.io/Dextercap-Page/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
