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.

翻译：捕捉精细的手-物交互极具挑战性，主要原因在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细微性。现有光学动作捕捉系统依赖昂贵的相机配置和大量人工后处理，而基于视觉的低成本方法在遮挡条件下往往存在精度与可靠性不足的问题。为应对这些挑战，我们提出了DexterCap——一种用于灵巧手内操作的低成本光学捕捉系统。DexterCap采用密集的字符编码标记点，在严重自遮挡条件下实现鲁棒跟踪，并配备全自动重建流程，极大减少了人工干预。基于DexterCap，我们构建了DexterHand数据集，该数据集涵盖从简单几何体到魔方等复杂关节物体的多样化操作行为与物体类型，记录了精细的手-物交互过程。我们公开该数据集及相关代码，以支持未来灵巧手-物交互研究。