Abstract
3D Face Reconstruction is a complex problem in Computer Vision. Until recently the existing methods were based on multiple image captures and solving complex dense correspondences between different face poses. Recent methods are based on volumetric CNNs and try to reconstruct the 3D face model from a single image. Accurate 3D face reconstructions are used nowadays for avatar modelling, in eyewear and hairstyle recommendation systems. All these require accurate face shape determination, which is subject to at least a fully frontalized 2D projection of the face, or even better to an accurate 3D volumetric reconstruction of the face. Most of the existing methods for 3D reconstruction stop somewhere in the middle of the forehead, limiting thus the obtained 3D model. We propose a mostly geometric method for facial reconstruction, based on structure from motion techniques on uncalibrated cameras, augmented with forehead surface modelling for added realism. We present a full section for proving that we are on par with state of the art deep learning techniques.
Citare
A. Marinescu, A. Dărăbant, T. Ileni, A Fast and Robust, Forehead-Augmented 3D Face Reconstruction from Multiple Images using Geometrical Methods, SoftCOM 2020, 28th International Conference on Software, Telecommunications and Computer Networks