TY - JOUR
T1 - Uncertainty-Aware Mesh Decoder for High Fidelity 3D Face Reconstruction
AU - Lee, Gun Hee
AU - Lee, Seong Whan
N1 - Funding Information:
This work was supported by Institute for Information & communications Technology Planning & Evaluation(IITP) grant funded by the Korea government (MSIT) (No. 2019-0-00079, Artificial Intelligence Graduate School Program(Korea University), No. 2019-0-01371, Development of brain-inspired AI with human-like intelligence, No. 2014-0-00059, Development of Predictive Visual Intelligence Technology).
Publisher Copyright:
© 2020 IEEE.
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85094817532&partnerID=8YFLogxK
U2 - 10.1109/CVPR42600.2020.00614
DO - 10.1109/CVPR42600.2020.00614
M3 - Conference article
AN - SCOPUS:85094817532
SP - 6099
EP - 6108
JO - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
JF - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SN - 1063-6919
M1 - 9156897
T2 - 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020
Y2 - 14 June 2020 through 19 June 2020
ER -