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face-reconstruction

3D Face Reconstruction

3260 papers • 126 benchmarks • 313 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 )

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in face-reconstruction

Trend

Dataset

Best Model

Actions

REALY

REALY

REALY (side-view)

REALY (side-view)

NoW Benchmark

NoW Benchmark

Libraries

i

Use these libraries to find face-reconstruction models and implementations

tomas-gajarsky/facetorch

2 papers 396

Datasets

AFLW2000-3D

FaceWarehouse

FaceScape

Florence

ExPose

NoW Benchmark

Subtasks

Facial Recognition and Modelling

Most implemented papers

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

Michael J. Black, J. Romero, Timo Bolkart, Tianye Li, Hao Li•Sun Nov 19 2017

Faces Learned with an Articulated Model and Expressions is low-dimensional but more expressive than the FaceWarehouse model and the Basel Face Model and is compared to these models by fitting them to static 3D scans and 4D sequences using the same optimization method.

1490

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

Florence

Florence

AFLW2000-3D

AFLW2000-3D

Stirling-HQ (FG2018 3D face reconstruction challenge)

Stirling-HQ (FG2018 3D face reconstruction challenge)

Stirling-LQ (FG2018 3D face reconstruction challenge)

Stirling-LQ (FG2018 3D face reconstruction challenge)

REALY

REALY

FFHQ-UV

DAD-3DHeads

FaMoS

0

Supplementary Materials for: RetinaFace: Single-shot Multi-level Face Localisation in the Wild

Jiankang Deng, J. Guo, S. Zafeiriou, I. Kotsia, Evangelos Ververas, InsightFace FaceSoft•Tue Dec 31 2019

The proposed RetinaFace can not only predict accurate 3D vertices but also estimate precise pose, and is able to estimate precise pose under variations of pose, expression, illumination, background, occlusion, and image quality.

433 0

YouTube-8M: A Large-Scale Video Classification Benchmark

Sami Abu-El-Haija, Sudheendra Vijayanarasimhan, G. Toderici, Apostol Natsev, Balakrishnan Varadarajan, Nisarg Kothari, Joonseok Lee•Mon Sep 26 2016

YouTube-8M is introduced, the largest multi-label video classification dataset, composed of ~8 million videos (500K hours of video), annotated with a vocabulary of 4800 visual entities, and various (modest) classification models are trained on the dataset.

1347 0

Regressing Robust and Discriminative 3D Morphable Models with a Very Deep Neural Network

G. Medioni, I. Masi, Tal Hassner, A. Tran•Wed Dec 14 2016

A robust method for regressing discriminative 3D morphable face models (3DMM) using a convolutional neural network to regress 3DMM shape and texture parameters directly from an input photo is described.

497 0

Accurate 3D Face Reconstruction With Weakly-Supervised Learning: From Single Image to Image Set

Jiaolong Yang, Yunde Jia, Dong Chen, Xin Tong, Yu Deng, Sicheng Xu•Tue Mar 19 2019

A novel deep 3D face reconstruction approach that leverages a robust, hybrid loss function for weakly-supervised learning which takes into account both low-level and perception-level information for supervision, and performs multi-image face reconstruction by exploiting complementary information from different images for shape aggregation is proposed.

828 0

Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry

Cho-Ying Wu, U. Neumann, Qiangeng Xu•Mon Oct 18 2021

This work studies learning from a synergy process of 3D Morphable Models (3DMM) and3D facial landmarks to predict complete 3D facial geometry, including 3D alignment, face orientation, and 3D face modeling.

75 0

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

Yanfeng Wang, Yao Feng, Fan Wu, Xiaohu Shao, Xi Zhou•Tue Mar 20 2018

A straightforward method that simultaneously reconstructs the 3D facial structure and provides dense alignment and surpasses other state-of-the-art methods on both reconstruction and alignment tasks by a large margin.

723 0

Perspective Reconstruction of Human Faces by Joint Mesh and Landmark Regression

J. Guo, Alexandros Lattas, Jinke Yu, Jiankang Deng•Sun Aug 14 2022

This paper proposes to simultaneously reconstruct3D face mesh in the world space and predict 2D face landmarks on the image plane to address the problem of perspective 3D face reconstruction.

5 0

Towards High-Fidelity 3D Face Reconstruction From In-the-Wild Images Using Graph Convolutional Networks

Kun Zhou, Jiangke Lin, Yi Yuan, Tianjia Shao•Wed Mar 11 2020

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, using graph convolutional networks to reconstruct the detailed colors for the mesh vertices instead of reconstructing the UV map.

113 0

Towards Fast, Accurate and Stable 3D Dense Face Alignment

Zhen Lei, S. Li, Yang Yang, Xiangyu Zhu, Jianzhu Guo•Sat Aug 22 2020

A novel regression framework which makes a balance among speed, accuracy and stability, and a meta-joint optimization strategy to dynamically regress a small set of 3DMM parameters, which greatly enhances speed and accuracy simultaneously.

466 0

Adding a benchmark result helps the community track progress.

3D Face Reconstruction | State-of-the-Art