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/Avditvs/proGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/AveryGriffin/NarutoCV
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/Ayoob7/facenet-face-recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/AyushAniket/ResNET-and-various-normalizations
https://github.com/BFD91/facial-beauty-rating-tinder-bot
SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction
https://github.com/BIG-CHENG/FaceRec
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/BPMJG/annotated_pointnet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/BR-IDL/PaddleViT/tree/develop/image_classification/RepMLP
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
https://github.com/BZstat/Attendance-AI
https://github.com/Baichenjia/Resnet
https://github.com/Bao-Jiarong/ResNet
https://github.com/Baukebrenninkmeijer/HackGAN
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/BeanGreen247/Python-AI-Arts
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/BearNinja123/esrGAN_vBearNinja123
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/BeefMILF/facenet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/Bennie-Han/Image-augementation-pytorch
Training Region-based Object Detectors with Online Hard Example Mining
https://github.com/BiDAlab/DeepFakesON-Phys
DeepFakesON-Phys: DeepFakes Detection based on Heart Rate Estimation
https://github.com/Bkmz21/CompactCNNCascade
Compact Convolutional Neural Network Cascade for Face Detection
https://github.com/BonJunKu/ScooterHelmetDetector
https://github.com/BradNeuberg/personal-photos-model
https://github.com/Bsingstad/ECG-age
https://github.com/Burf/HRNetV2-OCR-Tensorflow2
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/Burf/ResNet-Tensorflow2
https://github.com/Burf/tfdetection
https://github.com/CNC-IISER-BHOPAL/Any-Body-Can-Dance
https://github.com/CSAILVision/semantic-segmentation-pytorch
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/CVxTz/face_age_gender
https://github.com/CaedenZ/distractionModel
Real-time Convolutional Neural Networks for Emotion and Gender Classification
https://github.com/CahideSara/deneme
https://github.com/Calylyli/Mindsporehrnet
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/CanshangD/ResNet_tensorflow2.0
https://github.com/CatoGit/Comparing-the-Performance-of-Deepfake-Detection-Methods-on-Benchmark-Datasets
DeepFakes: a New Threat to Face Recognition? Assessment and Detection
https://github.com/CdtQin/FairFace
Improving Face Recognition from Hard Samples via Distribution Distillation Loss
https://github.com/CellSMB/EM-net
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/CenIII/WSNet
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.