Sparse Photometric 3D Face Reconstruction Guided by Morphable Models

Xuan Cao, Zhang Chen, Anpei Chen, Xin Chen, Shiying Li, Jingyi Yu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 4635-4644

Abstract


We present a novel 3D face reconstruction technique that leverages sparse photometric stereo (PS) and latest advances on face registration / modeling from a single image. We observe that 3D morphable faces approach provides a reasonable geometry proxy for light position calibration. Specifically, we develop a robust optimization technique that can calibrate per-pixel lighting direction and illumination at a very high precision without assuming uniform surface albedos. Next, we apply semantic segmentation on input images and the geometry proxy to refine hairy vs. bare skin regions using tailored filter. Experiments on synthetic and real data show that by using a very small set of images, our technique is able to reconstruct fine geometric details such as wrinkles, eyebrows, whelks, pores, etc, comparable to and sometimes surpassing movie quality productions.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Cao_2018_CVPR,
author = {Cao, Xuan and Chen, Zhang and Chen, Anpei and Chen, Xin and Li, Shiying and Yu, Jingyi},
title = {Sparse Photometric 3D Face Reconstruction Guided by Morphable Models},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}