  Learning a dense 3D model with fine-scale details from a single facial image
is highly challenging and ill-posed. To address this problem, many approaches
fit smooth geometries through facial prior while learning details as additional
displacement maps or personalized basis. However, these techniques typically
require vast datasets of paired multi-view data or 3D scans, whereas such
datasets are scarce and expensive. To alleviate heavy data dependency, we
present a robust texture-guided geometric detail recovery approach using only a
single in-the-wild facial image. More specifically, our method combines
high-quality texture completion with the powerful expressiveness of implicit
surfaces. Initially, we inpaint occluded facial parts, generate complete
textures, and build an accurate multi-view dataset of the same subject. In
order to estimate the detailed geometry, we define an implicit signed distance
function and employ a physically-based implicit renderer to reconstruct fine
geometric details from the generated multi-view images. Our method not only
recovers accurate facial details but also decomposes normals, albedos, and
shading parts in a self-supervised way. Finally, we register the implicit shape
details to a 3D Morphable Model template, which can be used in traditional
modeling and rendering pipelines. Extensive experiments demonstrate that the
proposed approach can reconstruct impressive facial details from a single
image, especially when compared with state-of-the-art methods trained on large
datasets.
