Face Reconstruction Edit Task
Computer Vision • Facial Recognition and Modelling
About
Face reconstruction is the task of recovering the facial geometry of a face from an image.
( Image credit: Microsoft Deep3DFaceReconstruction )
Benchmarks
Subtasks
Datasets
Greatest papers with code
Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression
ICCV 2017
• AaronJackson/vrn
• 
Our CNN works with just a single 2D facial image, does not require accurate alignment nor establishes dense correspondence between images, works for arbitrary facial poses and expressions, and can be used to reconstruct the whole 3D facial geometry (including the non-visible parts of the face) bypassing the construction (during training) and fitting (during testing) of a 3D Morphable Model.
Ranked #3 on
3D Face Reconstruction
on Florence
Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network
We propose a straightforward method that simultaneously reconstructs the 3D facial structure and provides dense alignment.
Ranked #1 on
Face Alignment
on AFLW-LFPA
Towards Fast, Accurate and Stable 3D Dense Face Alignment
Firstly, on the basis of a lightweight backbone, we propose a meta-joint optimization strategy to dynamically regress a small set of 3DMM parameters, which greatly enhances speed and accuracy simultaneously.
Ranked #1 on
3D Face Reconstruction
on AFLW2000-3D
3D FACE MODELING 3D FACE RECONSTRUCTION FACE ALIGNMENT FACE RECOGNITION
Face Alignment in Full Pose Range: A 3D Total Solution
• cleardusk/3DDFA
• 
In this paper, we propose to tackle these three challenges in an new alignment framework termed 3D Dense Face Alignment (3DDFA), in which a dense 3D Morphable Model (3DMM) is fitted to the image via Cascaded Convolutional Neural Networks.
Ranked #2 on
Face Alignment
on AFLW
3D POSE ESTIMATION DEPTH IMAGE ESTIMATION FACE ALIGNMENT FACE MODEL FACE RECONSTRUCTION
Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set
• Microsoft/Deep3DFaceReconstruction
• 
Recently, deep learning based 3D face reconstruction methods have shown promising results in both quality and efficiency. However, training deep neural networks typically requires a large volume of data, whereas face images with ground-truth 3D face shapes are scarce.
Ranked #2 on
3D Face Reconstruction
on NoW Benchmark
Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network
The 3D shapes of faces are well known to be discriminative.
Ranked #4 on
3D Face Reconstruction
on Florence
(Average 3D Error metric)
Extreme 3D Face Reconstruction: Seeing Through Occlusions
CVPR 2018
• anhttran/extreme_3d_faces
•
Motivated by the concept of bump mapping, we propose a layered approach which decouples estimation of a global shape from its mid-level details (e. g., wrinkles).
AvatarMe: Realistically Renderable 3D Facial Reconstruction "in-the-wild"
Over the last years, with the advent of Generative Adversarial Networks (GANs), many face analysis tasks have accomplished astounding performance, with applications including, but not limited to, face generation and 3D face reconstruction from a single "in-the-wild" image.
3D Face Reconstruction from A Single Image Assisted by 2D Face Images in the Wild
• XgTu/2DASL-CNN
•
3D face reconstruction from a single 2D image is a challenging problem with broad applications.
Ranked #3 on
Face Alignment
on AFLW2000-3D
3D FACE RECONSTRUCTION FACE ALIGNMENT FACE MODEL SELF-SUPERVISED LEARNING
GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction
In this paper, we take a radically different approach and harness the power of Generative Adversarial Networks (GANs) and DCNNs in order to reconstruct the facial texture and shape from single images.
Ranked #1 on
3D Face Reconstruction
on Florence
(Average 3D Error metric)