While our method contributes towards building control-
lable implicit facial avatars, some challenges remain. First,
surface representations achieve detailed facial geometry, but
they cannot model the ﬁne occlusions produced by hair.
Future work could address this by combining volumetric
representations [40] with animatable surfaces. Second, the
iterative non-rigid ray marching makes IMavatar slow to
train (∼ 2 GPU days).
Initializing with mesh-ray inter-
sections could speed up the process, as done in concurrent
work [30]. Third, our method relies on accurate face track-
ing and our performance degenerates with noisy 3DMM pa-
rameters (See Sup. Mat.). Reﬁning the poses and expres-
sions during training is a promising future direction. Fi-
nally, the appearance in the mouth interior region can be

D-Net

B-Morph

Fwd-Skin

C-Net

NerFACE [22]

Ours-

Ours

GT
I M Avatar- Implicit Morphable Head Avatars from Videos