AnyHand dataset boosts 3D hand pose estimation with synthetic data

By PulseAugur Editorial · [1 sources] · 2026-06-09 04:00

Researchers have introduced AnyHand, a large-scale synthetic dataset designed to improve 3D hand pose estimation. The dataset includes over 2.5 million single-hand and 4.1 million hand-object interaction RGB-D images, featuring rich geometric annotations and addressing limitations in existing real-world and synthetic datasets regarding occlusions and aligned depth information. Experiments show that incorporating AnyHand into training significantly boosts performance on benchmarks like FreiHAND and HO-3D, highlighting the critical role of data diversity and quality alongside scale. AI

IMPACT Enhances 3D hand pose estimation capabilities, potentially improving AR/VR and robotics applications.

RANK_REASON This is a research paper detailing a new dataset for computer vision. [lever_c_demoted from research: ic=1 ai=1.0]

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arXiv cs.CV TIER_1 English(EN) · Chen Si, Yulin Liu, Bo Ai, Jianwen Xie, Rolandos Alexandros Potamias, Chuanxia Zheng, Hao Su · 2026-06-09 04:00

AnyHand: A Large-Scale Synthetic Dataset for RGB(-D) Hand Pose Estimation

arXiv:2603.25726v3 Announce Type: replace Abstract: We present AnyHand, a large-scale synthetic dataset designed to advance the state of the art in 3D hand pose estimation. While recent works with foundation approaches have shown that scaling training data markedly improves hand …

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AnyHand: A Large-Scale Synthetic Dataset for RGB(-D) Hand Pose Estimation

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