  Traditional 3D morphable face models (3DMMs) provide fine-grained control
over expression but cannot easily capture geometric and appearance details.
Neural volumetric representations approach photorealism but are hard to animate
and do not generalize well to unseen expressions. To tackle this problem, we
propose IMavatar (Implicit Morphable avatar), a novel method for learning
implicit head avatars from monocular videos. Inspired by the fine-grained
control mechanisms afforded by conventional 3DMMs, we represent the expression-
and pose- related deformations via learned blendshapes and skinning fields.
These attributes are pose-independent and can be used to morph the canonical
geometry and texture fields given novel expression and pose parameters. We
employ ray marching and iterative root-finding to locate the canonical surface
intersection for each pixel. A key contribution is our novel analytical
gradient formulation that enables end-to-end training of IMavatars from videos.
We show quantitatively and qualitatively that our method improves geometry and
covers a more complete expression space compared to state-of-the-art methods.
