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3D Face Reconstruction On Florence

Metrics

Mean NME

Results

Performance results of various models on this benchmark

Model Name
Mean NME
Paper TitleRepository
VRN-Guided5.2667%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-
3DDFA6.3833%Face Alignment Across Large Poses: A 3D Solution-
3DMM-CNN-Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network-
PRN3.7551%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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3D Face Reconstruction On Florence | SOTA | HyperAI