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.
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https://github.com/datawiz1984/MLND_Capstone_Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/datawiz1984/malaria-model
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/datoboat/Vehicle-Detection
https://github.com/daveboat/pytorch_focal_loss
https://github.com/davisking/dlib
https://github.com/dc17540/darknet
https://github.com/dchen236/FairFace
FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age
https://github.com/dead-mazai/avia-missing-airplanes
https://github.com/debajyotid2/pointnet_pl
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/decile-team/cords
https://github.com/deepakks1995/inception
FaceNet: A Unified Embedding for Face Recognition and Clustering
https://github.com/deepfakes/faceswap
DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection
https://github.com/deepinx/SDU_face_alignment
Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment
https://github.com/deepinx/ShuffleNet_v1_and_v2
MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
https://github.com/deepinx/iqiyi-vid-challenge
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/deepinx/sdu-face-alignment
Stacked Dense U-Nets with Dual Transformers for Robust Face Alignment
https://github.com/deepinx/shufflenet-v1-and-v2
MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
https://github.com/deeplearning987/DoodleClassifier
https://github.com/deeplearningnapratica/pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/deepmind/dm-haiku
https://github.com/deepmodeling/deepmd-kit
https://github.com/deepquestai/fire-smoke-dataset
https://github.com/deftruth/torchlm
Pixel-in-Pixel Net: Towards Efficient Facial Landmark Detection in the Wild
https://github.com/delmalih/MIAS-mammography-obj-detection
https://github.com/demul/ResidualNet
https://github.com/dengpingfan/fsgan
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/denis19973/faceshifter_tornado
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
https://github.com/deontaepharr/Residual-Attention-Network
https://github.com/derek-baker/group3
Spotting Macro- and Micro-expression Intervals in Long Video Sequences
https://github.com/detectionteamucas/retinanet_tensorflow
https://github.com/dhaalves/CEAL_keras
Cost-Effective Active Learning for Deep Image Classification
https://github.com/dheerajsingh7351/facedetect
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/dhesenkamp/attentive-lstm
https://github.com/didi/AoE
SeesawFaceNets: sparse and robust face verification model for mobile platform
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.