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/ZhiwenShao/PyTorch-JAANet
공식 코드J$\hat{\text{A}}$A-Net: Joint Facial Action Unit Detection and Face Alignment via Adaptive Attention
https://github.com/ZichenMiao/GNN-with-Low-rank-Learnable-Local-Filters
공식 코드https://github.com/ZichenMiao/L3Net
공식 코드https://github.com/Zielon/MICA
공식 코드https://github.com/ZiqiaoPeng/SyncTalk
공식 코드SyncTalk: The Devil is in the Synchronization for Talking Head Synthesis
https://github.com/ZitongYu/CDCN
공식 코드Searching Central Difference Convolutional Networks for Face Anti-Spoofing
https://github.com/ZitongYu/DeepFAS
공식 코드https://github.com/aakash4305/gbdf
공식 코드GBDF: Gender Balanced DeepFake Dataset Towards Fair DeepFake Detection
https://github.com/aaroncomo/lipfd
공식 코드https://github.com/aashishrai3799/Towards-Realistic-Generative-3D-Face-Models
공식 코드https://github.com/abacusai/intraprocessing_debiasing
공식 코드https://github.com/abcdef2000/R2RNet
공식 코드R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network
https://github.com/abdallahdib/NextFace
공식 코드Practical Face Reconstruction via Differentiable Ray Tracing
https://github.com/abdelkadermh/isarcasmeval
공식 코드https://github.com/abheesht17/super-pixels
공식 코드Superpixel-based Knowledge Infusion in Deep Neural Networks for Image Classification
https://github.com/abhi1kumar/LUVLi
공식 코드LUVLi Face Alignment: Estimating Landmarks' Location, Uncertainty, and Visibility Likelihood
https://github.com/abhinavdhall/deepfake
공식 코드Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and Localization
https://github.com/abrarmajeedi/Disguised-Facial-Recognition-DFR
공식 코드A Supervised Learning Methodology for Real-Time Disguised Face Recognition in the Wild
https://github.com/adamian98/pulse
공식 코드PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models
https://github.com/adauchendu/topformer
공식 코드TOPFORMER: Topology-Aware Authorship Attribution of Deepfake Texts with Diverse Writing Styles
https://github.com/adipiz99/RawNetLite
공식 코드https://github.com/aditya-vora/FCHD-Fully-Convolutional-Head-Detector
공식 코드https://github.com/adobe-research/videosham-dataset
공식 코드Video Manipulations Beyond Faces: A Dataset with Human-Machine Analysis
https://github.com/adrianastan/tada
공식 코드TADA: Training-free Attribution and Out-of-Domain Detection of Audio Deepfakes
https://github.com/advboxes/AdvBox
공식 코드Advbox: a toolbox to generate adversarial examples that fool neural networks
https://github.com/advcolor123/advcolor
공식 코드Rethinking the Threat and Accessibility of Adversarial Attacks against Face Recognition Systems
https://github.com/adversarial-for-goodness/APF
공식 코드https://github.com/ae-1129/lanmsff
공식 코드https://github.com/afm215/FaVGen
공식 코드Fairer Analysis and Demographically Balanced Face Generation for Fairer Face Verification
https://github.com/ah651/deepfake_adv
공식 코드https://github.com/ahmedelgazzar0/dast-gcn
공식 코드Dynamic Adaptive Spatio-temporal Graph Convolution for fMRI Modelling
https://github.com/ai-application-and-integration-lab/dgua_fas
공식 코드https://github.com/ai-forever/ghost
공식 코드A new face swap method for image and video domains: a technical report
https://github.com/ai-s2-lab/m2s-add
공식 코드Betray Oneself: A Novel Audio DeepFake Detection Model via Mono-to-Stereo Conversion
https://github.com/ailon-island/facke
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.