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• W3197364189 abstract "3D hand-mesh reconstruction from RGB images facilitates many applications, including augmented reality (AR). However, this requires not only real-time speed and accurate hand pose and shape but also plausible mesh-image alignment. While existing works already achieve promising results, meeting all three requirements is very challenging. This paper presents a novel pipeline by decoupling the hand-mesh reconstruction task into three stages: a joint stage to predict hand joints and segmentation; a mesh stage to predict a rough hand mesh; and a refine stage to fine-tune it with an offset mesh for mesh-image alignment. With careful design in the network structure and in the loss functions, we can promote high-quality finger-level mesh-image alignment and drive the models together to deliver real-time predictions. Extensive quantitative and qualitative results on benchmark datasets demonstrate that the quality of our results outperforms the state-of-the-art methods on hand-mesh/pose precision and hand-image alignment. In the end, we also showcase several real-time AR scenarios." @default.
• W3197364189 created "2021-09-13" @default.
• W3197364189 creator A5022856212 @default.
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• W3197364189 date "2021-09-03" @default.
• W3197364189 modified "2023-10-16" @default.
• W3197364189 title "Towards Accurate Alignment in Real-time 3D Hand-Mesh Reconstruction" @default.
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• W3197364189 doi "https://doi.org/10.48550/arxiv.2109.01723" @default.
• W3197364189 hasPublicationYear "2021" @default.
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