  The combination of traditional rendering with neural networks in Deferred
Neural Rendering (DNR) provides a compelling balance between computational
complexity and realism of the resulting images. Using skinned meshes for
rendering articulating objects is a natural extension for the DNR framework and
would open it up to a plethora of applications. However, in this case the
neural shading step must account for deformations that are possibly not
captured in the mesh, as well as alignment inaccuracies and dynamics -- which
can confound the DNR pipeline. We present Articulated Neural Rendering (ANR), a
novel framework based on DNR which explicitly addresses its limitations for
virtual human avatars. We show the superiority of ANR not only with respect to
DNR but also with methods specialized for avatar creation and animation. In two
user studies, we observe a clear preference for our avatar model and we
demonstrate state-of-the-art performance on quantitative evaluation metrics.
Perceptually, we observe better temporal stability, level of detail and
plausibility.
