Inequality-Constrained and Robust 3D Face Model Fitting.

ABSTRACT: Fitting 3D morphable models (3DMMs) on faces is a well-studied problem, motivated by various industrial and research applications. 3DMMs express a 3D facial shape as a linear sum of basis functions. The resulting shape, however, is a plausible face only when the basis coefficients take values within limited intervals. Methods based on unconstrained optimization address this issue with a weighted ℓ ₂ penalty on coefficients; however, determining the weight of this penalty is difficult, and the existence of a single weight that works universally is questionable. We propose a new formulation that does not require the tuning of any weight parameter. Specifically, we formulate 3DMM fitting as an inequality-constrained optimization problem, where the primary constraint is that

SUBMITTER: Sariyanidi E 

PROVIDER: S-EPMC7594629 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature