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3D Face Reconstruction On Florence

Métriques

Mean NME

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Mean NME	Paper Title	Repository
VRN-Guided	5.2667%	Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression
Piotraschke and Blanz	-	Automated 3D Face Reconstruction From Multiple Images Using Quality Measures	-
DenseLandmarks (Single-view)	-	3D face reconstruction with dense landmarks	-
3DDFA_V2	-	Towards Fast, Accurate and Stable 3D Dense Face Alignment
Deep3DFaceReconstruction	-	Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set
Tran et al.	-	Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network
DenseLandmarks (Multi-view)	-	3D face reconstruction with dense landmarks	-
GANFit	-	GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction
itwmm	-	3D Face Morphable Models "In-the-Wild"	-
ASM	-	ASM: Adaptive Skinning Model for High-Quality 3D Face Modeling	-
3DDFA	6.3833%	Face Alignment Across Large Poses: A 3D Solution	-
3DMM-CNN	-	Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network
PRN	3.7551%	Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
Deng	-	Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set
Unsupervised-3DMMR	-	Unsupervised Training for 3D Morphable Model Regression
Genova et al.	-	Unsupervised Training for 3D Morphable Model Regression

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