IMUHand Benchmark:A Dataset and Baseline for 3D Hand Motion Reconstruction from Sparse IMUs

Continuous 3D hand motion reconstruction is fundamental to natural human-computer interaction and robotic manipulation. Vision-based methods exploit rich appearance and scene information but are limited by occlusion and restricted fields of view during hand-to-hand contact and hand-object interaction. Hand-mounted inertial measurement units (IMUs) do not require optical visibility, yet sparse inertial observations are insufficient to directly recover the full articulated hand pose and are affected by orientation drift and variations in sensor placement. Existing inertial hand datasets differ in acquisition settings and annotation formats. The lack of large-scale public datasets and standardized evaluation protocols for continuous IMU-only bimanual reconstruction further hinders fair comparison. To address this gap, we develop a pair of IMU gloves for synchronized data acquisition and real-time reconstruction. Using this system, we construct Benchmark, which provides long, continuous bimanual motion sequences, per-frame MANO ground truth, synchronized egocentric video, and a unified IMU-only evaluation protocol. We further propose a reconstruction method that combines learned secondary calibration with independent root orientation estimation to recover wrist orientation and the full articulated hand pose from IMU sequences alone. Under the unified protocol, our method achieves the highest reconstruction accuracy among the inertial methods evaluated.