  Human hands play a central role in interacting with other people and objects.
For realistic replication of such hand motions, high-fidelity hand meshes have
to be reconstructed. In this study, we firstly propose DeepHandMesh, a
weakly-supervised deep encoder-decoder framework for high-fidelity hand mesh
modeling. We design our system to be trained in an end-to-end and
weakly-supervised manner; therefore, it does not require groundtruth meshes.
Instead, it relies on weaker supervisions such as 3D joint coordinates and
multi-view depth maps, which are easier to get than groundtruth meshes and do
not dependent on the mesh topology. Although the proposed DeepHandMesh is
trained in a weakly-supervised way, it provides significantly more realistic
hand mesh than previous fully-supervised hand models. Our newly introduced
penetration avoidance loss further improves results by replicating physical
interaction between hand parts. Finally, we demonstrate that our system can
also be applied successfully to the 3D hand mesh estimation from general
images. Our hand model, dataset, and codes are publicly available at
https://mks0601.github.io/DeepHandMesh/.
