Papers with Code 아카이브
3D Face Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 9
- 보존된 평가 기록
- 94
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/xalanq/chinese-sentiment-classification
https://github.com/xg416/DATUM
Expanding Accurate Person Recognition to New Altitudes and Ranges: The BRIAR Dataset
https://github.com/xg416/mambatm
Expanding Accurate Person Recognition to New Altitudes and Ranges: The BRIAR Dataset
https://github.com/xialuxi/arcface-caffe
Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks
https://github.com/xiamenwcy/extended-caffe
https://github.com/xiaoccer/mobilefacenet_pytorch
MobileFaceNets: Efficient CNNs for Accurate Real-Time Face Verification on Mobile Devices
https://github.com/xiaoxiong74/face-cluster-by-infomap
https://github.com/xilinx/finn-examples
https://github.com/xiuyu0000/papers_with_examples/tree/main/pfld
https://github.com/xiuyu0000/vision/blob/main/mindvision/ms3d/models/pointnet2cls.py
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/xmu-xiaoma666/External-Attention-pytorch
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
https://github.com/xmu-xiaoma666/RepMLP-pytorch
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition
https://github.com/xuanyuzhou98/SqueezeSeg
https://github.com/xurui1217/pointnet.pytorch-master
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/xytpai/retinanet
https://github.com/y2kmz/pointnetv2
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/yakhyo/faceboxes-pytorch
https://github.com/yakhyo/gaze-estimation
Sample and Computation Redistribution for Efficient Face Detection
https://github.com/yakhyo/head-pose-estimation
https://github.com/yakhyo/retinaface-pytorch
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/yangyi02/densebox
DenseBox: Unifying Landmark Localization with End to End Object Detection
https://github.com/yangyucheng000/-insightface
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/yangyucheng000/ESRGAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/yangyucheng000/HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/yangyucheng000/MSpaper/tree/main/U-GAT-IT
https://github.com/yangyucheng000/Paper-2/tree/main/ARCM-main
ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/yangyucheng000/Paper-4/tree/main/JLONMFSC
Optimal Projections for Discriminative Dictionary Learning using the JL-lemma
https://github.com/yangyucheng000/Paper-4/tree/main/LADS-mindspore
LAD-RCNN:A Powerful Tool for Livestock Face Detection and Normalization
https://github.com/yangyucheng000/Paper-4/tree/main/PDOC_mindspore
SFace: Sigmoid-Constrained Hypersphere Loss for Robust Face Recognition
https://github.com/yangyucheng000/RetinaFace
RetinaFace: Single-stage Dense Face Localisation in the Wild
https://github.com/yangyucheng000/paper
CenterFace: Joint Face Detection and Alignment Using Face as Point
https://github.com/yangyucheng000/papercode-2/tree/main/JLONMFSC-master
Optimal Projections for Discriminative Dictionary Learning using the JL-lemma
https://github.com/yangyucheng000/papercode-2/tree/main/LADS-mindspore
LAD-RCNN:A Powerful Tool for Livestock Face Detection and Normalization
https://github.com/yangyucheng000/papercode-2/tree/main/SFAT-MindSpore-main
https://github.com/yangyucheng000/papercode-2/tree/main/TAA_MindSpore-main
TAA-GCN: A Temporally Aware Adaptive Graph Convolutional Network for Age Estimation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.