Unconstrained 3D face reconstruction

Joseph Roth, Yiying Tong, Xiaoming Liu
2015 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)  
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
more » ... anding computer vision problem. Motivated by the success of the state-of-theart 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.
doi:10.1109/cvpr.2015.7298876 dblp:conf/cvpr/RothTL15 fatcat:viiemsm7yjeqdab5cchulyaxte