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/jongli747/curricular-sincnet
공식 코드https://github.com/joojs/fairface
공식 코드FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age
https://github.com/josephdrahos/sreds
공식 코드https://github.com/juliojj/app-real-age
공식 코드On the effect of age perception biases for real age regression
https://github.com/jung-jun-uk/mixface
공식 코드MixFace: Improving Face Verification Focusing on Fine-grained Conditions
https://github.com/jung-jun-uk/unpg
공식 코드Unified Negative Pair Generation toward Well-discriminative Feature Space for Face Recognition
https://github.com/jungyg/alsuv
공식 코드Face Reconstruction Transfer Attack as Out-of-Distribution Generalization
https://github.com/jungyu0413/Navigating-Label-Ambiguity-NLA-
공식 코드Navigating Label Ambiguity for Facial Expression Recognition in the Wild
https://github.com/jungyu0413/V-NAW
공식 코드V-NAW: Video-based Noise-aware Adaptive Weighting for Facial Expression Recognition
https://github.com/junjun-jiang/Face-Hallucination-Benchmark
공식 코드https://github.com/junjun-jiang/TLcR-RL
공식 코드https://github.com/junshutang/3DFaceShop
공식 코드3DFaceShop: Explicitly Controllable 3D-Aware Portrait Generation
https://github.com/junwan2014/MMDN-master
공식 코드Robust Facial Landmark Detection by Multi-order Multi-constraint Deep Networks
https://github.com/justusschock/shapenet
공식 코드https://github.com/jymoon0613/simfle
공식 코드https://github.com/kaiwang960112/Challenge-condition-FER-dataset
공식 코드Region Attention Networks for Pose and Occlusion Robust Facial Expression Recognition
https://github.com/kaiwang960112/Self-Cure-Network
공식 코드Suppressing Uncertainties for Large-Scale Facial Expression Recognition
https://github.com/kaiyangzhou/coop
공식 코드https://github.com/kalyo-zjl/WM3DR
공식 코드https://github.com/kangyeolk/animeceleb
공식 코드AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head Reenactment
https://github.com/kartik-3004/segface
공식 코드https://github.com/kdhht2334/elim_fer
공식 코드Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition
https://github.com/keivanB/Young_Face_Aging_YFA
공식 코드https://github.com/kgrm/face-recog-eval
공식 코드Strengths and Weaknesses of Deep Learning Models for Face Recognition Against Image Degradations
https://github.com/khalilimahdi/loss_balancing_icml2023
공식 코드https://github.com/khawar-islam/fpvt
공식 코드https://github.com/khurramHashmi/FeatEnHancer
공식 코드FeatEnHancer: Enhancing Hierarchical Features for Object Detection and Beyond Under Low-Light Vision
https://github.com/kim1102/IG-FIQA
공식 코드https://github.com/kim1102/VIGFace
공식 코드VIGFace: Virtual Identity Generation for Privacy-Free Face Recognition
https://github.com/kjanjua26/Git-Loss-For-Deep-Face-Recognition
공식 코드https://github.com/kk7nc/RMDL
공식 코드https://github.com/kkkcx/diffender
공식 코드DIFFender: Diffusion-Based Adversarial Defense against Patch Attacks
https://github.com/konstantinkobs/SimLoss
공식 코드https://github.com/koushiksrivats/flip
공식 코드FLIP: Cross-domain Face Anti-spoofing with Language Guidance
https://github.com/krasjet-yu/yolo-facev2
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.