Meet DexterCap: Low-Cost, Automated Capture of Dexterous Hand-Object Manipulation

Kari Jaaskelainen

12 Jan 2026 — 1 min read

Teaching robots and AR/VR systems to understand nimble finger work is hard—fingers hide each other, and tiny motions get lost. DexterCap changes that.

Affordable + automated: A low-cost optical setup with an end-to-end pipeline that needs minimal manual cleanup.
Reliable under occlusion: Dense, character-coded marker patches keep tracking stable even when fingers overlap.
New dataset: DexterHand captures fine-grained hand–object interactions across many objects—from simple shapes to articulated objects like a Rubik’s Cube.

Why it matters: Better, cheaper motion capture can accelerate research in dexterous robotics, hand–object understanding, animation, and human–computer interaction.

Paper and resources: https://arxiv.org/abs/2601.05844v1 (dataset + code released).

Authors: Yutong Liang, Shiyi Xu, Yulong Zhang, Bowen Zhan, He Zhang, Libin Liu.

Paper: https://arxiv.org/abs/2601.05844v1

Register: https://www.AiFeta.com

#Robotics #AI #ComputerVision #MotionCapture #HandTracking #DexterousManipulation #AR #VR #OpenSource #Dataset

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