  We introduce a simple and effective network architecture for monocular 3D
hand pose estimation consisting of an image encoder followed by a mesh
convolutional decoder that is trained through a direct 3D hand mesh
reconstruction loss. We train our network by gathering a large-scale dataset of
hand action in YouTube videos and use it as a source of weak supervision. Our
weakly-supervised mesh convolutions-based system largely outperforms
state-of-the-art methods, even halving the errors on the in the wild benchmark.
The dataset and additional resources are available at
https://arielai.com/mesh_hands.

Computer Vision and Pattern Recognition