https://github.com/jason90330/EdgeFinal
RetinaFace: Single-stage Dense Face Localisation in the Wild
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
FaceQnet: Quality Assessment for Face Recognition based on Deep Learning
FaceQnet: Quality Assessment for Face Recognition based on Deep Learning
Convolutional Neural Networks for Facial Expression Recognition
A General Framework for Adversarial Examples with Objectives
Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs
Progressive Growing of GANs for Improved Quality, Stability, and Variation
SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
FaceForensics++: Learning to Detect Manipulated Facial Images
Very Deep Convolutional Networks for Large-Scale Image Recognition
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Seeking Salient Facial Regions for Cross-Database Micro-Expression Recognition
DenseBox: Unifying Landmark Localization with End to End Object Detection
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation
Very Deep Convolutional Networks for Large-Scale Image Recognition
FaceNet: A Unified Embedding for Face Recognition and Clustering
Very Deep Convolutional Networks for Large-Scale Image Recognition
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
RetinaFace: Single-stage Dense Face Localisation in the Wild
GANprintR: Improved Fakes and Evaluation of the State of the Art in Face Manipulation Detection
Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet
Very Deep Convolutional Networks for Large-Scale Image Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.