2020/03/27 by Xueying Wang, Yudong Guo, Wang, Xueying +5 · 1 citation
Computer Science · Engineering · Mathematics · #3D Shape Modeling and Analysis #Advanced Vision and Imaging #Artificial intelligence #Artificial neural network #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Construct (python library) #Deep learning #FOS: Computer and information sciences #Face (sociological concept) #Face recognition and analysis #Fidelity #Field (mathematics) #Image (mathematics) #Light field #Mathematics #Pattern recognition (psychology) #Photometric stereo #cs.CV
paper · pdf · doi:10.48550/arxiv.2003.12307
published in arXiv (Cornell University) (Cornell University) · Accepted to CVPR2020. The source code is available https://github.com/Juyong/FacePSNet
arxiv created 2020/03/27 · openalex publication_date 2020/03/27 · arxiv updated 2020/03/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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 https://github.com/Juyong/FacePSNet.