Papers with Code 아카이브
3D Face Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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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.
https://github.com/epoc88/PFLD_68pts_Pytorch_2020
https://github.com/eric-erki/ID-Card-Segmentation
https://github.com/eric-erki/IdenProf
https://github.com/eric-erki/Visualising-Facial-Expression-Changes
https://github.com/eric-erki/darknet
https://github.com/eric-erki/insightface
MS-Celeb-1M: A Dataset and Benchmark for Large-Scale Face Recognition
https://github.com/eric-erki/yolov3
https://github.com/eric-mitchell/mend
https://github.com/eric-mitchell/serac
https://github.com/eriklindernoren/PyTorch-GAN
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/ermuur/darknet
https://github.com/esadsetts/Mod_4_project
https://github.com/eshaanagarwal/hr-net-implementation
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/esimov/pigo
Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
https://github.com/espectre/GCNs_on_imbalanced_datasets
https://github.com/espressif/esp-who
Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks
https://github.com/eth-sri/llm-anonymization
https://github.com/eth212/Qmind_ModelNet10_PointNet
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/evtimovi/foggysight
https://github.com/ewinata/CIFAR-10
https://github.com/fL0n9/SKFAC-MindSpore
https://github.com/fabioasdias/ubao
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/fabiofumarola/ultrayolo
https://github.com/fabxy/accessibilityCV
https://github.com/facebook/fb.resnet.torch
https://github.com/facebookarchive/fb.resnet.torch
https://github.com/facebookresearch/DomainBed
https://github.com/facebookresearch/detectron2
https://github.com/facebookresearch/disentangling-correlated-factors
https://github.com/facebookresearch/hiera
https://github.com/facebookresearch/pycls
https://github.com/facebookresearch/pytext
https://github.com/facebookresearch/pytorch_GAN_zoo
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/facebookresearch/stochastic_gradient_push
https://github.com/facebookresearch/supervision-by-registration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.