Unconstrained 3D Face Reconstruction

Joseph Roth, Yiying Tong, Xiaoming Liu; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp. 2606-2615

Abstract


This paper presents an algorithm for unconstrained 3D face reconstruction. The input to our algorithm is an "unconstrained" collection of face images captured under a diverse variation of poses, expressions, and illuminations, without meta data about cameras or timing. The output of our algorithm is a true 3D face surface model represented as a watertight triangulated surface with albedo data or texture information. 3D face reconstruction from a collection of unconstrained 2D images is a long-standing computer vision problem. Motivated by the success of the state-of-the-art method, we developed a novel photometric stereo-based method with two distinct novelties. First, working with a true 3D model allows us to enjoy the benefits of using images from all possible poses, including profiles. Second, by leveraging emerging face alignment techniques and our novel normal field-based Laplace editing, a combination of landmark constraints and photometric stereo-based normals drives our surface reconstruction. Given large photo collections and a ground truth 3D surface, we demonstrate the effectiveness and strength of our algorithm both qualitatively and quantitatively.

Related Material


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[bibtex]
@InProceedings{Roth_2015_CVPR,
author = {Roth, Joseph and Tong, Yiying and Liu, Xiaoming},
title = {Unconstrained 3D Face Reconstruction},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2015}
}