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arXiv · 1708.09006

Pix2face: Direct 3D Face Model Estimation

Daniel Crispell·Maxim Bazik
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Abstract

An efficient, fully automatic method for 3D face shape and pose estimation in unconstrained 2D imagery is presented. The proposed method jointly estimates a dense set of 3D landmarks and facial geometry using a single pass of a modified version of the popular "U-Net" neural network architecture. Additionally, we propose a method for directly estimating a set of 3D Morphable Model (3DMM) parameters, using the estimated 3D landmarks and geometry as constraints in a simple linear system. Qualitative modeling results are presented, as well as quantitative evaluation of predicted 3D face landmarks in unconstrained video sequences.

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Daniel Crispell, Maxim Bazik. 2017-08-29. Pix2face: Direct 3D Face Model Estimation. https://arxiv.org/abs/1708.09006

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