https://github.com/mahxn0/RetinaFace_For_Face_Plate_Detect
RetinaFace: Single-stage Dense Face Localisation in the Wild
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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>
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
RetinaFace: Single-stage Dense Face Localisation in the Wild
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
RetinaFace: Single-stage Dense Face Localisation in the Wild
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
PetFace: A Large-Scale Dataset and Benchmark for Animal Identification
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age
Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Progressive Growing of GANs for Improved Quality, Stability, and Variation
FaceNet: A Unified Embedding for Face Recognition and Clustering
DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-Resolution
FaceNet: A Unified Embedding for Face Recognition and Clustering
High-Resolution Representations for Labeling Pixels and Regions
Deep High-Resolution Representation Learning for Visual Recognition
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
Enriched Long-term Recurrent Convolutional Network for Facial Micro-Expression Recognition
Classification is a Strong Baseline for Deep Metric Learning
Text2FaceGAN: Face Generation from Fine Grained Textual Descriptions
FaceNet: A Unified Embedding for Face Recognition and Clustering
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.