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/eewindfly/facenet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/egeoguzman/Liveness-Detection-Siamese
Face Liveness Detection Based on Client Identity Using Siamese Network
https://github.com/ehijano/Faster-RCNN-Global-Wheat-Detection
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/eladrich/pix2vertex.pytorch
Unrestricted Facial Geometry Reconstruction Using Image-to-Image Translation
https://github.com/elangarka/Regularized-Xception-FER-Extra-Training-Data-Step-Decay-Learning-Rate
Challenges in Representation Learning: A report on three machine learning contests
https://github.com/elhamod/phylonn
Latent Space Factorisation and Manipulation via Matrix Subspace Projection
https://github.com/elliott-iadvize/ocr-aws-distributed
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/elliottzheng/face-detection
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/eltechno/pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/embracesource-cv-com/FCHD
https://github.com/emilianavt/OpenSeeFace
Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression
https://github.com/enzomuschik/distilfnd
https://github.com/epoc88/PFLD_68pts_Pytorch_2020
https://github.com/eric-erki/ID-Card-Segmentation
https://github.com/eric-erki/IdenProf
https://github.com/eric-erki/Visualising-Facial-Expression-Changes
https://github.com/eric-erki/darknet
https://github.com/eric-erki/insightface
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
https://github.com/eric-erki/yolov3
https://github.com/eric-mitchell/mend
https://github.com/eric-mitchell/serac
https://github.com/eriklindernoren/PyTorch-GAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/ermuur/darknet
https://github.com/esadsetts/Mod_4_project
https://github.com/eshaanagarwal/hr-net-implementation
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/esimov/pigo
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
https://github.com/espectre/GCNs_on_imbalanced_datasets
https://github.com/espressif/esp-who
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/eth-sri/llm-anonymization
https://github.com/eth212/Qmind_ModelNet10_PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/evtimovi/foggysight
https://github.com/ewinata/CIFAR-10
https://github.com/fL0n9/SKFAC-MindSpore
https://github.com/fabioasdias/ubao
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/fabiofumarola/ultrayolo
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.