低成本自动捕捉精细手物交互，解决遮挡难题。
DexterCap: An Affordable and Automated System for Capturing Dexterous Hand-Object Manipulation
- 用密集编码标记贴片应对手指严重遮挡。
- 实现全自动重建，人工干预极少。
- 适合研究精细手部操作的学者与工程师。
由于手指紧密排列导致严重自遮挡，以及手中操作动作细微，捕捉精细的手物交互极具挑战。现有光学动捕系统依赖昂贵相机阵列并需大量手动后期处理，而低成本视觉方法在遮挡下常准确率下降。为此，我们提出DexterCap，一种低成本光学捕捉系统，用于精细手中操作。DexterCap采用密集、字符编码标记贴片，在严重自遮挡下仍能稳健跟踪，并配备自动化重建流程，大幅减少人工投入。基于此，我们构建了DexterHand数据集，涵盖从简单物体到复杂联动物体（如魔方）的多样化操作行为。代码与数据已开源，以支持未来对精细手物交互的研究。项目官网：https://pku-mocca.github.io/Dextercap-Page/
原文摘要 · Abstract (English)
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/
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。