Gesture-Aware Pretraining and Token Fusion for 3D Hand Pose Estimation

Rui Hong; Jana Kosecka

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arXiv · 2603.17396

Gesture-Aware Pretraining and Token Fusion for 3D Hand Pose Estimation

Rui Hong·Jana Kosecka

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Abstract

Estimating 3D hand pose from monocular RGB images is fundamental for applications in AR/VR, human-computer interaction, and sign language understanding. In this work we focus on a scenario where a discrete set of gesture labels is available and show that gesture semantics can serve as a powerful inductive bias for 3D pose estimation. We present a two-stage framework: gesture-aware pretraining that learns an informative embedding space using coarse and fine gesture labels from InterHand2.6M, followed by a per-joint token Transformer guided by gesture embeddings as intermediate representations for final regression of MANO hand parameters. Training is driven by a layered objective over parameters, joints, and structural constraints. Experiments on InterHand2.6M demonstrate that gesture-aware pretraining consistently improves single-hand accuracy over the state-of-the-art EANet baseline, and that the benefit transfers across architectures without any modification.

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BibTeX RIS

Rui Hong, Jana Kosecka. 2026-04-07. Gesture-Aware Pretraining and Token Fusion for 3D Hand Pose Estimation. https://arxiv.org/abs/2603.17396

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