Direct mesh fitting for 3D hand shape reconstruction is highly accurate.
However, the reconstructed meshes are prone to artifacts and do not appear as
plausible hand shapes. Conversely, parametric models like MANO ensure plausible
hand shapes but are not as accurate as the non-parametric methods. In this
work, we introduce a novel weakly-supervised hand shape estimation framework
that integrates non-parametric mesh fitting with MANO model in an end-to-end
fashion. Our joint model overcomes the tradeoff in accuracy and plausibility to
yield well-aligned and high-quality 3D meshes, especially in challenging
two-hand and hand-object interaction scenarios.

本研究介绍了一种弱监督手部形状估计框架，它将非参数网格拟合与 MANO 模型在端到端的方式中集成起来，克服了准确性和可信度之间的折衷，特别是在具有挑战性的双手和手 - 物体交互场景中产生了对齐良好、高质量的 3D 网格。

克服三维手形重建中准确度与真实性之间的权衡

Overcoming the Trade-off Between Accuracy and Plausibility in 3D Hand  Shape Reconstruction

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.

使用单个 RGB 图像提出一种新的框架，实现了 3D 手部形状重构和手 - 物体抓握优化，通过 UV 坐标映射的密集表示方法替代了以往稀疏点的接触区域近似，同时引入了推理时间优化来微调抓握，实现了更好的手部形状重构准确性和质感。在 Ho3D，FreiHAND 和 DexYCB 等数据集上进行的实验表明，我们的方法优于现有技术。