  We present SIDER(Single-Image neural optimization for facial geometric DEtail
Recovery), a novel photometric optimization method that recovers detailed
facial geometry from a single image in an unsupervised manner. Inspired by
classical techniques of coarse-to-fine optimization and recent advances in
implicit neural representations of 3D shape, SIDER combines a geometry prior
based on statistical models and Signed Distance Functions (SDFs) to recover
facial details from single images. First, it estimates a coarse geometry using
a morphable model represented as an SDF. Next, it reconstructs facial geometry
details by optimizing a photometric loss with respect to the ground truth
image. In contrast to prior work, SIDER does not rely on any dataset priors and
does not require additional supervision from multiple views, lighting changes
or ground truth 3D shape. Extensive qualitative and quantitative evaluation
demonstrates that our method achieves state-of-the-art on facial geometric
detail recovery, using only a single in-the-wild image.
