Uncertainty-Aware Mesh Decoder for High Fidelity 3D Face Reconstruction

Gun Hee Lee, Seong Whan Lee

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

3D Morphable Model (3DMM) is a statistical model of facial shape and texture using a set of linear basis functions. Most of the recent 3D face reconstruction methods aim to embed the 3D morphable basis functions into Deep Convolutional Neural Network (DCNN). However, balancing the requirements of strong regularization for global shape and weak regularization for high level details is still ill-posed. To address this problem, we properly control generality and specificity in terms of regularization by harnessing the power of uncertainty. Additionally, we focus on the concept of nonlinearity and find out that Graph Convolutional Neural Network (Graph CNN) and Generative Adversarial Network (GAN) are effective in reconstructing high quality 3D shapes and textures respectively. In this paper, we propose to employ (i) an uncertainty-Aware encoder that presents face features as distributions and (ii) a fully nonlinear decoder model combining Graph CNN with GAN. We demonstrate how our method builds excellent high quality results and outperforms previous state-of-The-Art methods on 3D face reconstruction tasks for both constrained and in-The-wild images.

Original languageEnglish
Article number9156897
Pages (from-to)6099-6108
Number of pages10
JournalProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
DOIs
Publication statusPublished - 2020
Event2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 - Virtual, Online, United States
Duration: 2020 Jun 142020 Jun 19

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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