Papers with Code 아카이브
3D Face Reconstruction
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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/yiyuan1991/3D-Face-GCNs
https://github.com/yjzhai-cs/vehicle-10
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/yoboget/CProGAN-ME
Progressive Growing of GANs for Improved Quality, Stability, and Variation
https://github.com/yoshitomo-matsubara/torchdistill
https://github.com/ypeleg/komplex
https://github.com/yqyao/ssd.pytorch
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/ytng001/sensemaking
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
https://github.com/ytsheng/sketch2cat-pulse
PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/yuanyuanli85/tf-hrnet
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/yubangji123/Interpret_FR
https://github.com/yudhisteer/Face-Recognition-with-Masks
https://github.com/yudie433/darknet
https://github.com/yuemenglong/deep-learning-copy
DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/yueyongjiao/Transformer-Contextual
https://github.com/yueyongjiao/attention_is_all_you_need_pytorch
https://github.com/yuezunli/CVPRW2019_Face_Artifacts
Exposing DeepFake Videos By Detecting Face Warping Artifacts
https://github.com/yuezunli/WIFS2018_In_Ictu_Oculi
In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking
https://github.com/yufat48/pulpissimo
An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics
https://github.com/yujiacheng333/CircleLossMNIST
Circle Loss: A Unified Perspective of Pair Similarity Optimization
https://github.com/yukichou/PET
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/yuliani29/yolotraining
https://github.com/yuriak/PPDAI
https://github.com/yuval-alaluf/restyle-encoder
Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
https://github.com/yuvalnirkin/face_segmentation
https://github.com/yuvrajarora/CSCI5922_Project
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/yuxiangalvin/DeepLOB-Model-Implementation-Project
https://github.com/yuzq97/starter_project
Interpreting the Latent Space of GANs for Semantic Face Editing
https://github.com/yw0nam/DenseNet
https://github.com/yxgeee/MMT
https://github.com/yxlijun/s3fd.pytorch
https://github.com/yxu0611/tensorflow-implementation-of-lcnn
https://github.com/yyfsXZ/structured_self_attention_sent_embedding
https://github.com/yyk-wew/F3Net
FaceForensics++: Learning to Detect Manipulated Facial Images
https://github.com/yytang2012/darknet
https://github.com/yzhou359/MakeItTalk
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.