  3D Morphable Models (3DMMs) demonstrate great potential for reconstructing
faithful and animatable 3D facial surfaces from a single image. The facial
surface is influenced by the coarse shape, as well as the static detail (e,g.,
person-specific appearance) and dynamic detail (e.g., expression-driven
wrinkles). Previous work struggles to decouple the static and dynamic details
through image-level supervision, leading to reconstructions that are not
realistic. In this paper, we aim at high-fidelity 3D face reconstruction and
propose HiFace to explicitly model the static and dynamic details.
Specifically, the static detail is modeled as the linear combination of a
displacement basis, while the dynamic detail is modeled as the linear
interpolation of two displacement maps with polarized expressions. We exploit
several loss functions to jointly learn the coarse shape and fine details with
both synthetic and real-world datasets, which enable HiFace to reconstruct
high-fidelity 3D shapes with animatable details. Extensive quantitative and
qualitative experiments demonstrate that HiFace presents state-of-the-art
reconstruction quality and faithfully recovers both the static and dynamic
details. Our project page can be found at https://project-hiface.github.io.
