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 )


Use these libraries to find 3D Face Reconstruction models and implementations

Most implemented papers

Learning a model of facial shape and expression from 4D scans

Rubikplayer/flame-fitting 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

google/youtube-8m 27 Sep 2016

Despite the size of the dataset, some of our models train to convergence in less than a day on a single machine using TensorFlow.

Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network

YadiraF/PRNet ECCV 2018

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 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 20 Mar 2019

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 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 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

choyingw/SynergyNet 19 Oct 2021

Our synergy process leverages a representation cycle for 3DMM parameters and 3D landmarks.

Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation

matansel/pix2vertex ICCV 2017

It has been recently shown that neural networks can recover the geometric structure of a face from a single given image.