  We present Hand ArticuLated Occupancy (HALO), a novel representation of
articulated hands that bridges the advantages of 3D keypoints and neural
implicit surfaces and can be used in end-to-end trainable architectures. Unlike
existing statistical parametric hand models (e.g.~MANO), HALO directly
leverages 3D joint skeleton as input and produces a neural occupancy volume
representing the posed hand surface. The key benefits of HALO are (1) it is
driven by 3D key points, which have benefits in terms of accuracy and are
easier to learn for neural networks than the latent hand-model parameters; (2)
it provides a differentiable volumetric occupancy representation of the posed
hand; (3) it can be trained end-to-end, allowing the formulation of losses on
the hand surface that benefit the learning of 3D keypoints. We demonstrate the
applicability of HALO to the task of conditional generation of hands that grasp
3D objects. The differentiable nature of HALO is shown to improve the quality
of the synthesized hands both in terms of physical plausibility and user
preference.
