Papers with Code 아카이브
3D Face Reconstruction
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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/Meta-Portrait/MetaPortrait
공식 코드MetaPortrait: Identity-Preserving Talking Head Generation with Fast Personalized Adaptation
https://github.com/Microsoft/Deep3DFaceReconstruction
공식 코드Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set
https://github.com/Microsoft/EdgeML
공식 코드RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference
https://github.com/Microsoft/FERPlus
공식 코드Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution
https://github.com/Milan-BUAA/TSAN-brain-age-estimation
공식 코드Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss
https://github.com/Mirevi/UCP-Framework
공식 코드https://github.com/MunchkinChen/FADING
공식 코드https://github.com/Mvrjustid/ACII19-Apex-Time-Network
공식 코드A Novel Apex-Time Network for Cross-Dataset Micro-Expression Recognition
https://github.com/NetEase-Media/ControlTalk
공식 코드Controllable Talking Face Generation by Implicit Facial Keypoints Editing
https://github.com/NetaGlazer/ADD-GP
공식 코드Few-Shot Speech Deepfake Detection Adaptation with Gaussian Processes
https://github.com/NetoPedro/FocusFace
공식 코드FocusFace: Multi-task Contrastive Learning for Masked Face Recognition
https://github.com/Nicholasli1995/VisualizingNDF
공식 코드https://github.com/NickyFot/HitchhikersGuide
공식 코드A Hitchhikers Guide to Fine-Grained Face Forgery Detection Using Common Sense Reasoning
https://github.com/Nithin-GK/UniteandConquer
공식 코드Unite and Conquer: Plug & Play Multi-Modal Synthesis using Diffusion Models
https://github.com/NuayHL/TimeStepGenerating
공식 코드Time Step Generating: A Universal Synthesized Deepfake Image Detector
https://github.com/OceanSummerDay/HSTA_MER
공식 코드Hierarchical Space-Time Attention for Micro-Expression Recognition
https://github.com/Open-Debin/Emotion-FAN
공식 코드Frame attention networks for facial expression recognition in videos
https://github.com/OpenImagingLab/LenslessFace
공식 코드LenslessFace: An End-to-End Optimized Lensless System for Privacy-Preserving Face Verification
https://github.com/OpenRL-Lab/DeepFakeFace
공식 코드Robustness and Generalizability of Deepfake Detection: A Study with Diffusion Models
https://github.com/OpenTAI/wild-deepfake
공식 코드WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection
https://github.com/PRALabBiometrics/APhotoEverydayDB
공식 코드Are Adaptive Face Recognition Systems still Necessary? Experiments on the APE Dataset
https://github.com/PaddlePaddle/PaddleClas
공식 코드https://github.com/PaddlePaddle/models
공식 코드https://github.com/PalomaCantero/Deepfake-Detection-and-the-Impact-of-Limited-Computing-Capabilities
공식 코드Deepfake Detection and the Impact of Limited Computing Capabilities
https://github.com/PanosAntoniadis/emotion-gcn
공식 코드https://github.com/Pea-Shooter/ADNet
공식 코드ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
https://github.com/Pei-KaiHuang/AAAI25-SLIP
공식 코드SLIP: Spoof-Aware One-Class Face Anti-Spoofing with Language Image Pretraining
https://github.com/Peihao-Xiang/MTCAE-DFER
공식 코드MTCAE-DFER: Multi-Task Cascaded Autoencoder for Dynamic Facial Expression Recognition
https://github.com/Powercoder64/TAA-GCN
공식 코드TAA-GCN: A Temporally Aware Adaptive Graph Convolutional Network for Age Estimation
https://github.com/Puspitamajumdariiit/Unravelling-the-Effect-of-Image-Distortions
공식 코드https://github.com/QingyuZhao/BR-Net
공식 코드Representation Learning with Statistical Independence to Mitigate Bias
https://github.com/RQuispeC/multi-stream-crowd-counting-extended
공식 코드Multi-Stream Networks and Ground-Truth Generation for Crowd Counting
https://github.com/RameshaKaru/devi
공식 코드DEVI: Open-source Human-Robot Interface for Interactive Receptionist Systems
https://github.com/Raschka-research-group/coral-cnn
공식 코드Rank consistent ordinal regression for neural networks with application to age estimation
https://github.com/Raul2718/FLUXSynID
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.