https://github.com/HRNet/HRNet-Human-Pose-Estimation
High-Resolution Representations for Labeling Pixels and Regions
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/HRNet/HRNet-Image-Classification
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/HRNet/HRNet-MaskRCNN-Benchmark
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/HRNet/HRNet-Object-Detection
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/HRNet/HRNet-Semantic-Segmentation
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/HamadYA/GhostFaceNets
https://github.com/Hamiedamr/EmotionNet-Nano-An-Efficient-Deep-Convolutional-Neural-Network-Design-for-Real-time-Facial-Expressi
https://github.com/Hamiltonsjtu/based_on_facenet
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/Hangz-nju-cuhk/Talking-Face-Generation-DAVS
Talking Face Generation by Adversarially Disentangled Audio-Visual Representation
https://github.com/HannH/DMFN
https://github.com/Harut0726/my-pointnet-tensorflow
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/HavenFeng/photometric_optimization
https://github.com/Hayashi-Yudai/ML_models
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/Hazqeel09/ellzaf_ml
S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens
https://github.com/HearyShen/HAN-SelfAttentive
https://github.com/Heonozis/FaceShifter-pytorch
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
https://github.com/HiJiGOO/self-attentive
https://github.com/Hikaylee/SynergyNet
Synergy between 3DMM and 3D Landmarks for Accurate 3D Facial Geometry
https://github.com/HongSic/DarknetAI
https://github.com/HongguLiu/MesoNet.Pytorch
https://github.com/HongwenZhang/JVCR-3Dlandmark
https://github.com/Human3DAIGC/AccurateFacialLandmarkDetection
Cascaded Dual Vision Transformer for Accurate Facial Landmark Detection
https://github.com/HyeongJu916/Boaz-SR-ESRGAN-PyTorch
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/IIGROUP/Multi-Modal-CelebA-HQ-Dataset
TediGAN: Text-Guided Diverse Face Image Generation and Manipulation
https://github.com/IIGROUP/TediGAN
TediGAN: Text-Guided Diverse Face Image Generation and Manipulation
https://github.com/IPLI/IPLI_Server
https://github.com/IQTLabs/Pytorch_Retinaface
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/IanSullivan/DeepFakeTorch
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/IcedDoggie/Micro-Expression-with-Deep-Learning
Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition
https://github.com/IcewineChen/mxnet-batch_hard_triplet_loss
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/Ierezell/PapierFewShot
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
https://github.com/IlyaOvodov/AngelinaReader
https://github.com/InterDigitalInc/Latent-Transformer
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
https://github.com/Ipsedo/MusicGAN
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/IsharaSandun/Face-Recognition-using-python
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/IssacCyj/Adversarial-Occlussion-aware-Face-Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.