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홈
SOTA
3D Face Reconstruction
3D Face Reconstruction On Florence
3D Face Reconstruction On Florence
평가 지표
Mean NME
평가 결과
이 벤치마크에서 각 모델의 성능 결과
Columns
모델 이름
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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