3D Face Alignment
12 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in 3D Face Alignment
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
AFLW2000-3D
FaMoS
Most implemented papers
How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)
1adrianb/face-alignment
•
•
ICCV 2017
To this end, we make the following 5 contributions: (a) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and finally evaluate it on all other 2D facial landmark datasets.
FacePoseNet: Making a Case for Landmark-Free Face Alignment
Instead, we compare our FPN with existing methods by evaluating how they affect face recognition accuracy on the IJB-A and IJB-B benchmarks: using the same recognition pipeline, but varying the face alignment method.
Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry
Our synergy process leverages a representation cycle for 3DMM parameters and 3D landmarks.
Learning an Animatable Detailed 3D Face Model from In-The-Wild Images
YadiraF/DECA
•
•
Some methods produce faces that cannot be realistically animated because they do not model how wrinkles vary with expression.
Two-stage Convolutional Part Heatmap Regression for the 1st 3D Face Alignment in the Wild (3DFAW) Challenge
This paper describes our submission to the 1st 3D Face Alignment in the Wild (3DFAW) Challenge.
Latent RANSAC
We present a method that can evaluate a RANSAC hypothesis in constant time, i. e. independent of the size of the data.
Hierarchical binary CNNs for landmark localization with limited resources
1adrianb/binary-networks-pytorch
•
•
To this end, we make the following contributions: (a) we are the first to study the effect of neural network binarization on localization tasks, namely human pose estimation and face alignment.
Adversarial Learning Semantic Volume for 2D/3D Face Shape Regression in the Wild
HongwenZhang/JVCR-3Dlandmark
•
•
IEEE Transactions on Image Processing 2019
Then, an end-to-end pipeline is designed to jointly regress the proposed volumetric representation and the coordinate vector.
Dual Attention MobDenseNet(DAMDNet) for Robust 3D Face Alignment
3D face alignment of monocular images is a crucial process in the recognition of faces with disguise. 3D face reconstruction facilitated by alignment can restore the face structure which is helpful in detcting disguise interference. This paper proposes a dual attention mechanism and an efficient end-to-end 3D face alignment framework. We build a stable network model through Depthwise Separable Convolution, Densely Connected Convolutional and Lightweight Channel Attention Mechanism.
img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation
vitoralbiero/img2pose
•
•
CVPR 2021
Tests on AFLW2000-3D and BIWI show that our method runs at real-time and outperforms state of the art (SotA) face pose estimators.
