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Existing optical motion capture systems rely on expensive camera setups and extensive manual post\u2010processing, while low\u2010cost vision\u2010based methods often suffer from reduced accuracy and reliability under occlusion. To address these challenges, we present DexterCap, a low\u2010cost optical capture system for dexterous in\u2010hand manipulation. DexterCap uses dense, character\u2010coded marker patches to achieve robust tracking under severe self\u2010occlusion, together with an automated reconstruction pipeline that requires minimal manual effort. With DexterCap, we introduce DexterHand, a dataset of fine\u2010grained hand\u2010object 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\u2010object interaction. 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