  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.
