DexterCap: An Affordable and Automated System for Capturing Dexterous Hand-Object Manipulation
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866917268988887040 |
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| author | Liang, Yutong Xu, Shiyi Zhang, Yulong Zhan, Bowen Zhang, He Liu, Libin |
| author_facet | Liang, Yutong Xu, Shiyi Zhang, Yulong Zhan, Bowen Zhang, He Liu, Libin |
| contents | 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/ |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2601_05844 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DexterCap: An Affordable and Automated System for Capturing Dexterous Hand-Object Manipulation Liang, Yutong Xu, Shiyi Zhang, Yulong Zhan, Bowen Zhang, He Liu, Libin Graphics Artificial Intelligence Robotics I.3.6 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/ |
| title | DexterCap: An Affordable and Automated System for Capturing Dexterous Hand-Object Manipulation |
| topic | Graphics Artificial Intelligence Robotics I.3.6 |
| url | https://arxiv.org/abs/2601.05844 |