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3D hand-mesh reconstruction from RGB images facilitates many applications,including augmented reality (AR). However, this requires not only real-timespeed and accurate hand pose and shape but also plausible mesh-image alignment.While existing works already achieve promising results, meeting all threerequirements is very challenging. This paper presents a novel pipeline bydecoupling the hand-mesh reconstruction task into three stages: a joint stageto predict hand joints and segmentation; a mesh stage to predict a rough handmesh; and a refine stage to fine-tune it with an offset mesh for mesh-imagealignment. With careful design in the network structure and in the lossfunctions, we can promote high-quality finger-level mesh-image alignment anddrive the models together to deliver real-time predictions. Extensivequantitative and qualitative results on benchmark datasets demonstrate that thequality of our results outperforms the state-of-the-art methods onhand-mesh/pose precision and hand-image alignment. In the end, we also showcaseseveral real-time AR scenarios.

Towards Accurate Alignment in Real-time 3D Hand-Mesh Reconstruction