3D Face Reconstruction
64 papers with code • 7 benchmarks • 10 datasets
3D Face Reconstruction is a computer vision task that involves creating a 3D model of a human face from a 2D image or a set of images. The goal of 3D face reconstruction is to reconstruct a digital 3D representation of a person's face, which can be used for various applications such as animation, virtual reality, and biometric identification.
( Image credit: 3DDFA_V2 )
Benchmarks
These leaderboards are used to track progress in 3D Face Reconstruction
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Libraries
Use these libraries to find 3D Face Reconstruction models and implementationsDatasets
AFLW2000-3D
FaceWarehouse
FaceScape
Florence
ExPose
NoW Benchmark
REALY
FFHQ-UV
1,056 People Living_Face & Anti-Spoofing Data
5,199 People – 3D Face Recognition Images Data
Most implemented papers
Learning a model of facial shape and expression from 4D scans
Rubikplayer/flame-fitting
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SIGGRAPH Asia 2017
FLAME is low-dimensional but more expressive than the FaceWarehouse model and the Basel Face Model.
YouTube-8M: A Large-Scale Video Classification Benchmark
Despite the size of the dataset, some of our models train to convergence in less than a day on a single machine using TensorFlow.
Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network
The 3D shapes of faces are well known to be discriminative.
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.
RetinaFace: Single-Shot Multi-Level Face Localisation in the Wild
serengil/deepface
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CVPR 2020
Though tremendous strides have been made in uncontrolled face detection, accurate and efficient 2D face alignment and 3D face reconstruction in-the-wild remain an open challenge.
Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set
Microsoft/Deep3DFaceReconstruction
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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.
Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks
FuxiCV/3D-Face-GCNs
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CVPR 2020
In this paper, we introduce a method to reconstruct 3D facial shapes with high-fidelity textures from single-view images in-the-wild, without the need to capture a large-scale face texture database.
Towards Fast, Accurate and Stable 3D Dense Face Alignment
cleardusk/3DDFA_V2
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ECCV 2020
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.
Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry
Our synergy process leverages a representation cycle for 3DMM parameters and 3D landmarks.
Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation
matansel/pix2vertex
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ICCV 2017
It has been recently shown that neural networks can recover the geometric structure of a face from a single given image.
