  3D Human Body Reconstruction from a monocular image is an important problem
in computer vision with applications in virtual and augmented reality
platforms, animation industry, en-commerce domain, etc. While several of the
existing works formulate it as a volumetric or parametric learning with complex
and indirect reliance on re-projections of the mesh, we would like to focus on
implicitly learning the mesh representation. To that end, we propose a novel
model, HumanMeshNet, that regresses a template mesh's vertices, as well as
receives a regularization by the 3D skeletal locations in a multi-branch,
multi-task setup. The image to mesh vertex regression is further regularized by
the neighborhood constraint imposed by mesh topology ensuring smooth surface
reconstruction. The proposed paradigm can theoretically learn local surface
deformations induced by body shape variations and can therefore learn
high-resolution meshes going ahead. We show comparable performance with SoA (in
terms of surface and joint error) with far lesser computational complexity,
modeling cost and therefore real-time reconstructions on three publicly
available datasets. We also show the generalizability of the proposed paradigm
for a similar task of predicting hand mesh models. Given these initial results,
we would like to exploit the mesh topology in an explicit manner going ahead.
