3D Face Reconstruction
76 papers with code • 7 benchmarks • 11 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
DAD-3DHeads
FaMoS
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.
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.
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.
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.
Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry
Our synergy process leverages a representation cycle for 3DMM parameters and 3D landmarks.
Perspective Reconstruction of Human Faces by Joint Mesh and Landmark Regression
Even though 3D face reconstruction has achieved impressive progress, most orthogonal projection-based face reconstruction methods can not achieve accurate and consistent reconstruction results when the face is very close to the camera due to the distortion under the perspective projection.
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.
