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Recently, 3D face reconstruction from a single image has achieved greatsuccess with the help of deep learning and shape prior knowledge, but theyoften fail to produce accurate geometry details. On the other hand, photometricstereo methods can recover reliable geometry details, but require dense inputsand need to solve a complex optimization problem. In this paper, we present alightweight strategy that only requires sparse inputs or even a single image torecover high-fidelity face shapes with images captured under near-field lights.To this end, we construct a dataset containing 84 different subjects with 29expressions under 3 different lights. Data augmentation is applied to enrichthe data in terms of diversity in identity, lighting, expression, etc. Withthis constructed dataset, we propose a novel neural network specially designedfor photometric stereo based 3D face reconstruction. Extensive experiments andcomparisons demonstrate that our method can generate high-qualityreconstruction results with one to three facial images captured undernear-field lights. Our full framework is available athttps://github.com/Juyong/FacePSNet.

Lightweight Photometric Stereo for Facial Details Recovery