Deep Energies for Estimating Three-Dimensional Facial Pose and Expression

Jane Wu, Michael Bao, Xinwei Yao, Ronald Fedkiw

Communications on Applied Mathematics and Computation ›› 2023, Vol. 6 ›› Issue (2) : 837-861. DOI: 10.1007/s42967-023-00256-y

Deep Energies for Estimating Three-Dimensional Facial Pose and Expression

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Abstract

While much progress has been made in capturing high-quality facial performances using motion capture markers and shape-from-shading, high-end systems typically also rely on rotoscope curves hand-drawn on the image. These curves are subjective and difficult to draw consistently; moreover, ad-hoc procedural methods are required for generating matching rotoscope curves on synthetic renders embedded in the optimization used to determine three-dimensional (3D) facial pose and expression. We propose an alternative approach whereby these curves and other keypoints are detected automatically on both the image and the synthetic renders using trained neural networks, eliminating artist subjectivity, and the ad-hoc procedures meant to mimic it. More generally, we propose using machine learning networks to implicitly define deep energies which when minimized using classical optimization techniques lead to 3D facial pose and expression estimation.

Keywords

Numerical optimization / Neural networks / Motion capture / Face tracking

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Jane Wu, Michael Bao, Xinwei Yao, Ronald Fedkiw. Deep Energies for Estimating Three-Dimensional Facial Pose and Expression. Communications on Applied Mathematics and Computation, 2023, 6(2): 837‒861 https://doi.org/10.1007/s42967-023-00256-y

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Funding
Office of Naval Research(N00014-13-1-0346); Office of Naval Research(N00014-17-1-2174); Army Research Laboratory(AHPCRC W911NF-07-0027); Amazon; Toyota USA; VMware; Stanford Engineering

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