Traditional 3D morphable face models (3DMMs) pro-
vide ﬁne-grained control over expression but cannot eas-
ily capture geometric and appearance details. Neural
volumetric representations approach photorealism but are
hard to animate and do not generalize well to unseen ex-
pressions. To tackle this problem, we propose IMavatar
(Implicit Morphable avatar), a novel method for learn-
ing implicit head avatars from monocular videos. Inspired
by the ﬁne-grained control mechanisms afforded by con-
ventional 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 canon-
ical surface intersection for each pixel. A key contribution is
our novel analytical gradient formulation that enables end-
I M Avatar- Implicit Morphable Head Avatars from Videos