Face Reconstruction
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Face reconstruction is the task of recovering the facial geometry of a face from an image.
<span style="color:grey; opacity: 0.6">( Image credit: Microsoft Deep3DFaceReconstruction )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Juyong/CaricatureFace
공식 코드Landmark Detection and 3D Face Reconstruction for Caricature using a Nonlinear Parametric Model
https://github.com/Juyong/FacePSNet
공식 코드https://github.com/KIT-MRT/latent_diffusion_face_anonymization
공식 코드LDFA: Latent Diffusion Face Anonymization for Self-driving Applications
https://github.com/KU-CVLAB/MoDiTalker
공식 코드MoDiTalker: Motion-Disentangled Diffusion Model for High-Fidelity Talking Head Generation
https://github.com/KUCognitiveInformaticsLab/PairwiseExpressionAnnotator
공식 코드HAPI: A Model for Learning Robot Facial Expressions from Human Preferences
https://github.com/KaenChan/ProbFace
공식 코드https://github.com/KaimingHe/resnet-1k-layers
공식 코드https://github.com/KamruzzamanAsif/BanglaFake
공식 코드BanglaFake: Constructing and Evaluating a Specialized Bengali Deepfake Audio Dataset
https://github.com/Kartik-3004/facexformer
공식 코드https://github.com/KeqiangSun/FAB
공식 코드FAB: A Robust Facial Landmark Detection Framework for Motion-Blurred Videos
https://github.com/KikimorMay/MultiFace
공식 코드MultiFace: A Generic Training Mechanism for Boosting Face Recognition Performance
https://github.com/KnowledgeDiscovery/FaceSec
공식 코드FACESEC: A Fine-grained Robustness Evaluation Framework for Face Recognition Systems
https://github.com/KovenYu/state-information
공식 코드Weakly supervised discriminative feature learning with state information for person identification
https://github.com/LSIbabnikz/DifFIQA
공식 코드DifFIQA: Face Image Quality Assessment Using Denoising Diffusion Probabilistic Models
https://github.com/LeiJiangJNU/DAMDNet
공식 코드Dual Attention MobDenseNet(DAMDNet) for Robust 3D Face Alignment
https://github.com/Leminhbinh0209/AAAI22-ADD
공식 코드https://github.com/Leminhbinh0209/KappaFace
공식 코드KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition
https://github.com/LetterLiGo/SafeEar
공식 코드SafeEar: Content Privacy-Preserving Audio Deepfake Detection
https://github.com/Li-Chongyi/Lighting-the-Darkness-in-the-Deep-Learning-Era-Open
공식 코드Low-Light Image and Video Enhancement Using Deep Learning: A Survey
https://github.com/Li-Chongyi/Zero-DCE
공식 코드Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement
https://github.com/Li-Wanhua/MetaAge
공식 코드https://github.com/Li-Wanhua/POEs
공식 코드Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression
https://github.com/LonglongaaaGo/EXE-GAN
공식 코드Do Inpainting Yourself: Generative Facial Inpainting Guided by Exemplars
https://github.com/Lotayou/Face-Renovation
공식 코드HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
https://github.com/LynnHo/EigenGAN-Tensorflow
공식 코드https://github.com/MASILab/BRAID
공식 코드Brain age identification from diffusion MRI synergistically predicts neurodegenerative disease
https://github.com/MCG-NJU/SADRNet
공식 코드https://github.com/MIvanovska/MAD-DDPM
공식 코드Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models
https://github.com/MRzzm/DINet
공식 코드DINet: Deformation Inpainting Network for Realistic Face Visually Dubbing on High Resolution Video
https://github.com/MSoumm/FaVFA
공식 코드Fairer Analysis and Demographically Balanced Face Generation for Fairer Face Verification
https://github.com/MZMMSEC/SJEDD
공식 코드Semantics-Oriented Multitask Learning for DeepFake Detection: A Joint Embedding Approach
https://github.com/MZMMSEC/SO-DFD
공식 코드Semantic Contextualization of Face Forgery: A New Definition, Dataset, and Detection Method
https://github.com/MahmoudRabea13/IdentiFace
공식 코드https://github.com/MarekKowalski/DeepAlignmentNetwork
공식 코드Deep Alignment Network: A convolutional neural network for robust face alignment
https://github.com/MarekKowalski/HoloFace
공식 코드HoloFace: Augmenting Human-to-Human Interactions on HoloLens
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.