UV-Based 3D Hand-Object Reconstruction with Grasp Optimization
About
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.
Ziwei Yu, Linlin Yang, You Xie, Ping Chen, Angela Yao• 2022
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| 3D Hand Reconstruction | FreiHAND | PA MPVPE0.73 | 20 | |
| 3D Hand Reconstruction | HO3D v3 | PA-MPJPE10.8 | 18 | |
| 3D Hand-Object Reconstruction | HO3D v2 | MPJPE1.04 | 11 | |
| 3D Hand-Object Reconstruction | HO3D v3 | MPJPE1.08 | 10 | |
| 3D Mesh Reconstruction | HO3D v3 | PA-MPJPE10.8 | 9 | |
| 3D Hand-Object Reconstruction | DexYCB (S0) | MPJPE1.09 | 8 |
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