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UV-Based 3D Hand-Object Reconstruction with Grasp Optimization

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arxiv 2211.13429 v1 pith:YVU2HFPQ submitted 2022-11-24 cs.CV cs.AI

classification cs.CVcs.AI
keywords handgrasphand-objectoptimizationreconstructioncontactproposeregions
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We propose a novel framework for 3D hand shape reconstruction and hand-object grasp optimization from a single RGB image. The representation of hand-object contact regions is critical for accurate reconstructions. Instead of approximating the contact regions with sparse points, as in previous works, we propose a dense representation in the form of a UV coordinate map. Furthermore, we introduce inference-time optimization to fine-tune the grasp and improve interactions between the hand and the object. Our pipeline increases hand shape reconstruction accuracy and produces a vibrant hand texture. Experiments on datasets such as Ho3D, FreiHAND, and DexYCB reveal that our proposed method outperforms the state-of-the-art.

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Cited by 1 Pith paper

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  1. MaskHand: Generative Masked Modeling for Robust Hand Mesh Reconstruction in the Wild

    cs.CV 2024-12 conditional novelty 6.0 of 10

    MaskHand applies masked generative modeling to MANO pose tokens with confidence-guided iterative sampling, achieving top results on HO3Dv3, FreiHAND, DexYCB, and HInt hand benchmarks.

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