  We present imGHUM, the first holistic generative model of 3D human shape and
articulated pose, represented as a signed distance function. In contrast to
prior work, we model the full human body implicitly as a function
zero-level-set and without the use of an explicit template mesh. We propose a
novel network architecture and a learning paradigm, which make it possible to
learn a detailed implicit generative model of human pose, shape, and semantics,
on par with state-of-the-art mesh-based models. Our model features desired
detail for human models, such as articulated pose including hand motion and
facial expressions, a broad spectrum of shape variations, and can be queried at
arbitrary resolutions and spatial locations. Additionally, our model has
attached spatial semantics making it straightforward to establish
correspondences between different shape instances, thus enabling applications
that are difficult to tackle using classical implicit representations. In
extensive experiments, we demonstrate the model accuracy and its applicability
to current research problems.
