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