  In this paper, we present a HAnd Mesh Recovery (HAMR) framework to tackle the
problem of reconstructing the full 3D mesh of a human hand from a single RGB
image. In contrast to existing research on 2D or 3D hand pose estimation from
RGB or/and depth image data, HAMR can provide a more expressive and useful mesh
representation for monocular hand image understanding. In particular, the mesh
representation is achieved by parameterizing a generic 3D hand model with shape
and relative 3D joint angles. By utilizing this mesh representation, we can
easily compute the 3D joint locations via linear interpolations between the
vertexes of the mesh, while obtain the 2D joint locations with a projection of
the 3D joints.To this end, a differentiable re-projection loss can be defined
in terms of the derived representations and the ground-truth labels, thus
making our framework end-to-end trainable.Qualitative experiments show that our
framework is capable of recovering appealing 3D hand mesh even in the presence
of severe occlusions.Quantitatively, our approach also outperforms the
state-of-the-art methods for both 2D and 3D hand pose estimation from a
monocular RGB image on several benchmark datasets.
