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/anirudharc/transformer
https://github.com/anitagold/30-days-of-Udacity
Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
https://github.com/anjandeepsahni/face_classification
https://github.com/anjandeepsahni/face_recognition
https://github.com/anjith2006/bob.paper.mcae.icb2019
Domain Adaptation in Multi-Channel Autoencoder based Features for Robust Face Anti-Spoofing
https://github.com/anjith2006/bob.paper.mccnn.tifs2018
Biometric Face Presentation Attack Detection with Multi-Channel Convolutional Neural Network
https://github.com/anjith2006/bob.paper.oneclass_mccnn_2019
https://github.com/anjith2006/bob.paper.tbiom2023_edgeface
https://github.com/anjith2006/bob.paper.tifs2022_hfr_prepended_domain_transformer
Prepended Domain Transformer: Heterogeneous Face Recognition without Bells and Whistles
https://github.com/ankit-vaghela30/Google-landmark-prediction
https://github.com/annabellachen/newDarknet
https://github.com/anookeen/yolo
https://github.com/anoushkrit/Knowledge
https://github.com/anshky/HR-NET
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/anshu123priya/Real-v-s-Fake-Emotion-Challenge
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/anshu123priya/Real-vs-Fake-Emotion-Challenge
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/anuj-anuj/face-similarity
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/anujdutt9/Artistic-Style-Transfer-using-Keras-Tensorflow
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/aobaruwa/TensorFlow-Projects
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/aoru45/BasketNet
https://github.com/aoru45/Deep-Anomaly-Detection-for-Generalized-Face-Anti-Spoofing
https://github.com/aoru45/LFFD-Pytorch
https://github.com/apaz-cli/ML-ImageHash
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/apisarek/gan_advances
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/aporw/Class-Attendance-Tool--Face-Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/arahosu/musicslots
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/aralhekimoglu/face
Sample and Computation Redistribution for Efficient Face Detection
https://github.com/arghadeep25/Face-Detection
https://github.com/ariangc/breinchallenge
https://github.com/ariel415el/ALAE
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/ariel415el/SimplePytorch-ALAE
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/arkel23/Danbooru2018AnimeCharacterRecognitionDataset_Revamped
https://github.com/arsfutura/face-recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/artemmavrin/focal-loss
https://github.com/arturjordao/arturjordao
Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction Method
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.