A number of methods have been proposed for face reconstruction from single/multiple image(s). However, it is still a challenge to do reconstruction for limited number of wild images, in which there exists complex different imaging conditions, various face appearance and limited number of high-quality images. And most current mesh model based methods cannot generate high-quality face model because of the local mapping deviation in geometric optics and distortion error brought by discrete differential operation. In this paper, accurate geometrical consistency modeling on B-spline parameter domain is proposed to reconstruct high-quality face surface from the various images. The modeling is completely consistent with the law of geometric optics, and B-spline reduces the distortion during surface deformation. In our method, 0th- and 1st-order consistency of stereo are formulated based on low-rank texture structures and local normals, respectively, to approach the pinpoint geometric modeling for face reconstruction. A practical solution combining the two consistency as well as an iterative algorithm is proposed to optimize high-detailed B-spline face effectively. Extensive empirical evaluations on synthetic data and unconstrained data are conducted, and the experimental results demonstrate the effectiveness of our method on challenging scenario, e.g., limited number of images with different head poses, illuminations, and expressions.

中文翻译：

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基于有限数量野生图像的 3D 人脸重建的 B 样条参数域几何一致性建模


已经提出了多种用于从单个/多个图像重建面部的方法。然而，对有限数量的野外图像进行重建仍然是一个挑战，其中存在着复杂的不同成像条件、不同的人脸外观以及有限的高质量图像。而目前大多数基于网格模型的方法由于几何光学中的局部映射偏差以及离散微分运算带来的畸变误差而无法生成高质量的人脸模型。本文提出了 B 样条参数域上的精确几何一致性建模，以从各种图像中重建高质量的人脸表面。建模完全符合几何光学定律，B样条减少了表面变形时的畸变。在我们的方法中，分别基于低阶纹理结构和局部法线制定立体的零阶和一阶一致性，以接近面部重建的精确几何建模。提出了一种结合两种一致性和迭代算法的实用解决方案，以有效优化高细节 B 样条面。对合成数据和无约束数据进行了广泛的实证评估，实验结果证明了我们的方法在具有挑战性的场景中的有效性，例如具有不同头部姿势、照明和表情的有限数量的图像。