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.

提出了一种无须多张图片和监督的图片光度优化方法——SIDER，该方法通过统计模型和符号距离函数结合，利用隐式神经表示的三维形状估计出含有细节的粗糙人脸三维几何模型，并通过光度损失函数优化得到真实图像，实现从单张图片中自动恢复面部几何细节，达到了最先进的效果。

SIDER: 面部几何细节恢复的单图像神经优化