  Current parametric models have made notable progress in 3D hand pose and
shape estimation. However, due to the fixed hand topology and complex hand
poses, current models are hard to generate meshes that are aligned with the
image well. To tackle this issue, we introduce a dual noise estimation method
in this paper. Given a single-view image as input, we first adopt a baseline
parametric regressor to obtain the coarse hand meshes. We assume the mesh
vertices and their image-plane projections are noisy, and can be associated in
a unified probabilistic model. We then learn the distributions of noise to
refine mesh vertices and their projections. The refined vertices are further
utilized to refine camera parameters in a closed-form manner. Consequently, our
method obtains well-aligned and high-quality 3D hand meshes. Extensive
experiments on the large-scale Interhand2.6M dataset demonstrate that the
proposed method not only improves the performance of its baseline by more than
10$\%$ but also achieves state-of-the-art performance. Project page:
\url{https://github.com/hanhuili/DNE4Hand}.
