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3D Face Reconstruction

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하위 태스크: 1
평가표: 9
보존된 평가 기록: 94

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.

<span style="color:grey; opacity: 0.6">( Image credit: 3DDFA_V2 )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/zyh-uaiaaaa/erasing-attention-consistency

공식 코드

Learn From All: Erasing Attention Consistency for Noisy Label Facial Expression Recognition

https://github.com/zyh-uaiaaaa/generalizable-fer

공식 코드

Generalizable Facial Expression Recognition

https://github.com/zyx2012/Age_estimation_BMVC2017

공식 코드

Quantifying Facial Age by Posterior of Age Comparisons

https://github.com/zyzhangustc/mellm

공식 코드

MELLM: Exploring LLM-Powered Micro-Expression Understanding Enhanced by Subtle Motion Perception

https://gitlab.com/acit-lausanne/refacing-cgan

공식 코드

Fast refacing of MR images with a generative neural network lowers re-identification risk and preserves volumetric consistency

https://gitlab.com/pl8787/CaffeAttention

공식 코드

Locally Smoothed Neural Networks

https://gitlab.idiap.ch/biometric/icassp2024.dvpf

공식 코드

Deep Variational Privacy Funnel: General Modeling with Applications in Face Recognition

https://gitlab.idiap.ch/bob/bob.paper.backdoors_anomaly_detection.biosig2022

공식 코드

An anomaly detection approach for backdoored neural networks: face recognition as a case study

https://gitlab.idiap.ch/bob/bob.paper.eusipco2023_mlphash

공식 코드

MLP-Hash: Protecting Face Templates via Hashing of Randomized Multi-Layer Perceptron

https://gitlab.idiap.ch/bob/bob.paper.icassp2021_morph

공식 코드

Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks

https://gitlab.idiap.ch/bob/bob.paper.icassp2022_morph_generate

공식 코드

Are GAN-based Morphs Threatening Face Recognition?

https://gitlab.idiap.ch/bob/bob.paper.ijcb2021_synthetic_dataset

공식 코드

On the use of automatically generated synthetic image datasets for benchmarking face recognition

https://gitlab.idiap.ch/bob/bob.paper.ijcb2023_caim_hfr

공식 코드

Bridging the Gap: Heterogeneous Face Recognition with Conditional Adaptive Instance Modulation

https://gitlab.idiap.ch/bob/bob.paper.ijcb2023_synthdistill

공식 코드

SynthDistill: Face Recognition with Knowledge Distillation from Synthetic Data

https://gitlab.idiap.ch/bob/bob.paper.ijcb2024_agnostic_features_mad

공식 코드

Evaluating the Effectiveness of Attack-Agnostic Features for Morphing Attack Detection

https://gitlab.idiap.ch/bob/bob.paper.tifs2022_hfr_prepended_domain_transformer

공식 코드

Prepended Domain Transformer: Heterogeneous Face Recognition without Bells and Whistles

https://huggingface.co/FoivosPar/Arc2Face

공식 코드

Arc2Face: A Foundation Model for ID-Consistent Human Faces

https://worksheets.codalab.org/worksheets/0x7b8f6fbc6b5c49c18ac7ca94aafaa1a7

공식 코드

Network In Network

https://bitbucket.org/nishkalavallabhi/complexity-features

Readability-based Sentence Ranking for Evaluating Text Simplification

https://dagshub.com/Bharat-mtr/LightCNN

A Light CNN for Deep Face Representation with Noisy Labels

https://dagshub.com/Bharat-mtr/One_Shot_Face_Reenactment

One-shot Face Reenactment

https://gitee.com/gai-shaoyan/mind3d

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/163GitHub/AI

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/18alantom/identify

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/18alantom/smel_project

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/1996scarlet/ArcFace-Multiplex-Recognition

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

https://github.com/1996scarlet/Laser-Eye

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/1996scarlet/dense-head-pose-estimation

Face Alignment in Full Pose Range: A 3D Total Solution

https://github.com/1996scarlet/faster-mobile-retinaface

RetinaFace: Single-stage Dense Face Localisation in the Wild

https://github.com/1adrianb/2D-and-3D-face-alignment

How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)

https://github.com/1adrianb/binary-face-alignment

Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources

https://github.com/1adrianb/binary-human-pose-estimation

Binarized Convolutional Landmark Localizers for Human Pose Estimation and Face Alignment with Limited Resources

https://github.com/1adrianb/face-alignment-training

How far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks)

https://github.com/1chenchen22/LFNSB

https://github.com/1ho0jin1/QMUL-SurvFace-Open-Set-Identification

Surveillance Face Recognition Challenge

https://github.com/2023-MindSpore-1/ms-code-16/tree/main/ADNet

ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.