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/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/fabiofumarola/ultrayolo
https://github.com/facebookresearch/detectron2
https://github.com/facebookresearch/disentangling-correlated-factors
https://github.com/facebookresearch/pytext
https://github.com/facebookresearch/pytorch_GAN_zoo
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/facebookresearch/supervision-by-registration
https://github.com/faddey-w/face-swap
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
https://github.com/faridSam/srn
Selective Refinement Network for High Performance Face Detection
https://github.com/farinamatteo/zero
https://github.com/fbplab/MEBeauty-database
https://github.com/feidfoe/AdjustBnd4Imbalance
https://github.com/feiyunzhang/person_attribute_mxnet
MOON: A Mixed Objective Optimization Network for the Recognition of Facial Attributes
https://github.com/felipeyanez/nmf
Primal-Dual Algorithms for Non-negative Matrix Factorization with the Kullback-Leibler Divergence
https://github.com/fenghansen/ESRGAN-Keras
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/fenglian425/Agriculture_AI
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/ffletcherr/face-recognition-liveness
Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
https://github.com/ffr4nz/UnknownUnknowns
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/final-0/Real-ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/final-0/Real-ESRGAN-bicubic
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/fizyr/keras-retinanet
https://github.com/flora-zyx/SJNet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/flynn-chen/faceforensics_benchmark
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/flytocc/PaddleClas
https://github.com/fnyanez/nmf
Primal-Dual Algorithms for Non-negative Matrix Factorization with the Kullback-Leibler Divergence
https://github.com/foamliu/Gaze-Estimation
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/foamliu/InsightFace-PyTorch
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/foamliu/InsightFace-v2
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/foamliu/InsightFace-v3
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.