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.

翻译：直接网格拟合进行3D手部形状重建具有高准确性，但重建后的网格容易产生伪影，无法呈现合理的手部形状。反之，基于参数化模型（如MANO）虽能确保手部形状的合理性，但其准确性不及非参数化方法。本文提出一种新颖的弱监督手部形状估计框架，以端到端方式将非参数化网格拟合与MANO模型相结合。我们的联合模型克服了准确性与合理性之间的权衡，能够生成对齐良好且高质量的3D网格，特别是在双人交互和手-物交互等复杂场景中表现优异。