AVI-HT: Adaptive Vision-IMU Fusion for 3D Gloved Hand Tracking

원문 초록

We present AVI-HT, an adaptive visual-IMU fusion approach for tracking 3D hand poses by jointly modeling the egocentric images with on-glove 6-DoF IMU signals. AVI-HT significantly improved accuracy and availability, particularly in hand-object interaction (HOI) scenarios involving heavy visual occlusion. Two complementary ingredients underpin its success: (1) synchronized multi-modal training data pairing on-body vision-IMU sensor streams with ground-truth 3D hand poses from a motion-capture system, and (2) a cross-sensor deep attention mechanism that adaptively modulates the trust assigned to the vision and individual IMU sensors. To evaluate AVI-HT in real-world settings, we conduct extensive experiments on our DexGloveHOI dataset that consists of 100K+ pairwise vision-IMU samples with synchronized 3D annotated poses, in which users manipulate a variety of objects during daily tasks. We compare against multiple single- and multi-modal tracking approaches under two hand models (UmeTrack, MANO). The results show that AVI-HT reduces mean keypoint error by 16.1% and its wrist-aligned variant by 24.2% over the baselines. A hand-object contact evaluation shows that AVI-HT keeps finger contacts with the manipulated object more accurately and consistently over time, reducing contact losses by 8.5%. Ablation studies further show that our fusion design outperforms simpler schemes and that the gains grow with occlusion and hold across subjects.