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/

翻译：由于紧密排列的手指造成的严重自遮挡以及手内操作动作的微妙性，捕捉细粒度的手-物体交互具有挑战性。现有的光学动作捕捉系统依赖于昂贵的摄像机设置和大量的人工后处理，而低成本的基于视觉的方法在遮挡条件下往往精度和可靠性不足。为了解决这些挑战，我们提出了DexterCap，一个用于灵巧手内操作的低成本光学捕捉系统。DexterCap使用密集的字符编码标记块，在严重自遮挡下实现鲁棒跟踪，并配有一个自动化重建流程，仅需最少的人工干预。利用DexterCap，我们引入了DexterHand数据集，这是一个涵盖从简单基元到复杂铰接物体（如魔方）的多样化操作行为和物体的细粒度手-物体交互数据集。我们公开了该数据集和代码，以支持未来关于灵巧手-物体交互的研究。项目网站：https://pku-mocca.github.io/Dextercap-Page/