https://github.com/nashory/pggan-pytorch
Progressive Growing of GANs for Improved Quality, Stability, and Variation
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.
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Progressive Growing of GANs for Improved Quality, Stability, and Variation
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
RetinaFace: Single-stage Dense Face Localisation in the Wild
Very Deep Convolutional Networks for Large-Scale Image Recognition
Decorrelated Adversarial Learning for Age-Invariant Face Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Very Deep Convolutional Networks for Large-Scale Image Recognition
Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videos
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Very Deep Convolutional Networks for Large-Scale Image Recognition
img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation
Very Deep Convolutional Networks for Large-Scale Image Recognition
RetinaFace: Single-stage Dense Face Localisation in the Wild
FaceNet: A Unified Embedding for Face Recognition and Clustering
Very Deep Convolutional Networks for Large-Scale Image Recognition
Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks
FaceNet: A Unified Embedding for Face Recognition and Clustering
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.