DexterCap: An Affordable and Automated System for Capturing Dexterous Hand-Object Manipulation
This addresses the problem of expensive and manual motion capture for dexterous hand-object interactions, offering an affordable and automated solution for researchers in robotics and computer vision.
The paper tackles the challenge of capturing fine-grained hand-object interactions by introducing DexterCap, a low-cost optical capture system that uses dense, character-coded marker patches for robust tracking under severe self-occlusion, and it includes the DexterHand dataset covering diverse manipulation behaviors.
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/