Recently, 3D face reconstruction from a single image has achieved great
success with the help of deep learning and shape prior knowledge, but they
often fail to produce accurate geometry details. On the other hand, photometric
stereo methods can recover reliable geometry details, but require dense inputs
and need to solve a complex optimization problem. In this paper, we present a
lightweight strategy that only requires sparse inputs or even a single image to
recover high-fidelity face shapes with images captured under near-field lights.
To this end, we construct a dataset containing 84 different subjects with 29
expressions under 3 different lights. Data augmentation is applied to enrich
the data in terms of diversity in identity, lighting, expression, etc. With
this constructed dataset, we propose a novel neural network specially designed
for photometric stereo based 3D face reconstruction. Extensive experiments and
comparisons demonstrate that our method can generate high-quality
reconstruction results with one to three facial images captured under
near-field lights. Our full framework is available at
this https URL

在这篇论文中，我们提出了一种基于光度立体技术的轻量级策略，仅需要稀疏输入或单个图像即可恢复近场光下高保真度的面部形态。通过构建包含 84 个不同主题和 29 种表情的数据集，我们提出了一个专门设计用于光度立体基于 3D 面部重建的新型神经网络。广泛的实验和比较表明，我们的方法可以生成高质量的重建结果。