Papers with Code 아카이브

3D Face Reconstruction

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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/Bingwen-Hu/ERGAN-Pytorch

공식 코드

Unsupervised Eyeglasses Removal in the Wild

https://github.com/BradyFU/DVG

공식 코드

Dual Variational Generation for Low-Shot Heterogeneous Face Recognition

https://github.com/Carlyx/DPE

공식 코드

DPE: Disentanglement of Pose and Expression for General Video Portrait Editing

https://github.com/ChenqiKONG/OSDFD

공식 코드

Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture

https://github.com/ClaudiaShu/SSL-FER

공식 코드

Revisiting Self-Supervised Contrastive Learning for Facial Expression Recognition

https://github.com/Clench/TAR_resAE

공식 코드

TAR: Generalized Forensic Framework to Detect Deepfakes using Weakly Supervised Learning

https://github.com/CodeShareBot/Micro-Attention-for-Micro-Expression

공식 코드

Micro-Attention for Micro-Expression recognition

https://github.com/ControlNet/LAV-DF

공식 코드

Glitch in the Matrix: A Large Scale Benchmark for Content Driven Audio-Visual Forgery Detection and Localization

https://github.com/ControlNet/MARLIN

공식 코드

MARLIN: Masked Autoencoder for facial video Representation LearnINg

https://github.com/Coral79/CDDB

공식 코드

A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials

https://github.com/CreativePapers/Learn-to-Synthesize-and-Synthesize-to-Learn

공식 코드

Learn to synthesize and synthesize to learn

https://github.com/D-X-Y/SAN

공식 코드

Style Aggregated Network for Facial Landmark Detection

https://github.com/D-X-Y/landmark-detection

공식 코드

Teacher Supervises Students How to Learn From Partially Labeled Images for Facial Landmark Detection

https://github.com/Darg-Iztech/gender-prediction-from-tweets

공식 코드

Gender Prediction from Tweets: Improving Neural Representations with Hand-Crafted Features

https://github.com/DariusAf/MesoNet

공식 코드

MesoNet: a Compact Facial Video Forgery Detection Network

https://github.com/DengPingFan/Scoot

공식 코드

Face Sketch Synthesis Style Similarity:A New Structure Co-occurrence Texture Measure

https://github.com/DeokyunKim/Progressive-Face-Super-Resolution

공식 코드

Progressive Face Super-Resolution via Attention to Facial Landmark

https://github.com/DetionDX/Attribute2Sketch2Face

공식 코드

Face Synthesis from Visual Attributes via Sketch using Conditional VAEs and GANs

https://github.com/DetionDX/GP-GAN-Gender-Preserving-GAN-for-Synthesizing-Faces-from-Landmarks

공식 코드

GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks

https://github.com/DetionDX/GP-GAN-GenderPreserving-GAN-for-Synthesizing-Faces-from-Landmarks

공식 코드

GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks

https://github.com/Dianezzy/ParaLip

공식 코드

Parallel and High-Fidelity Text-to-Lip Generation

https://github.com/DingXiaoH/RepMLP

공식 코드

RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition

https://github.com/Drkun/ZSTCI

공식 코드

Incremental Embedding Learning via Zero-Shot Translation

https://github.com/ESanchezLozano/GANnotation

공식 코드

Triple consistency loss for pairing distributions in GAN-based face synthesis

https://github.com/EduardaCaldeira/DiffProb

공식 코드

DiffProb: Data Pruning for Face Recognition

https://github.com/EndlessSora/DeeperForensics-1.0

공식 코드

DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery Detection

https://github.com/EricGzq/AMTENnet

공식 코드

Fake face detection via adaptive manipulation traces extraction network

https://github.com/FER-LMC/S2D

공식 코드

From Static to Dynamic: Adapting Landmark-Aware Image Models for Facial Expression Recognition in Videos

https://github.com/FaceOnLive/Face-Recognition-SDK-Android

공식 코드

Cross-Age Contrastive Learning for Age-Invariant Face Recognition

https://github.com/FacePerceiver/FaRL

공식 코드

General Facial Representation Learning in a Visual-Linguistic Manner

https://github.com/Faceplugin-ltd/FaceLivenessDetection-Android

공식 코드

Generalized Face Liveness Detection via De-fake Face Generator

https://github.com/FeiiYin/StyleHEAT

공식 코드

StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via Pre-trained StyleGAN

https://github.com/Fictionarry/InsTaG

공식 코드

InsTaG: Learning Personalized 3D Talking Head from Few-Second Video

https://github.com/FuxiCV/3D-Face-GCNs

공식 코드

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

https://github.com/FuxiCV/MeInGame

공식 코드

MeInGame: Create a Game Character Face from a Single Portrait

https://github.com/FuxiVirtualHuman/Write-a-Speaker

공식 코드

Write-a-speaker: Text-based Emotional and Rhythmic Talking-head Generation

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