Traditional 3D face models are based on mesh representations with texture.
One of the most important models is FLAME (Faces Learned with an Articulated
Model and Expressions), which produces meshes of human faces that are fully
controllable. Unfortunately, such models have problems with capturing geometric
and appearance details. In contrast to mesh representation, the neural radiance
field (NeRF) produces extremely sharp renders. But implicit methods are hard to
animate and do not generalize well to unseen expressions. It is not trivial to
effectively control NeRF models to obtain face manipulation. The present paper
proposes a novel approach, named NeRFlame, which combines the strengths of both
NeRF and FLAME methods. Our method enables high-quality rendering capabilities
of NeRF while also offering complete control over the visual appearance,
similar to FLAME. Unlike conventional NeRF-based architectures that utilize
neural networks to model RGB colors and volume density, NeRFlame employs FLAME
mesh as an explicit density volume. As a result, color values are non-zero only
in the proximity of the FLAME mesh. This FLAME backbone is then integrated into
the NeRF architecture to predict RGB colors, allowing NeRFlame to explicitly
model volume density and implicitly model RGB colors