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/fdbtrs/pocketnet
공식 코드https://github.com/fdbtrs/sface-privacy-friendly-and-accurate-face-recognition-using-synthetic-data
공식 코드SFace: Privacy-friendly and Accurate Face Recognition using Synthetic Data
https://github.com/fdbtrs/synthetic-face-recognition
공식 코드Identity-driven Three-Player Generative Adversarial Network for Synthetic-based Face Recognition
https://github.com/fdnerf/fdnerf
공식 코드FDNeRF: Few-shot Dynamic Neural Radiance Fields for Face Reconstruction and Expression Editing
https://github.com/fedeloper/binary_deepfake_detection
공식 코드Faster Than Lies: Real-time Deepfake Detection using Binary Neural Networks
https://github.com/fedenoce/s2l-s2d
공식 코드Learning Landmarks Motion from Speech for Speaker-Agnostic 3D Talking Heads Generation
https://github.com/fei-hdu/ca-gan
공식 코드Towards Realistic Face Photo-Sketch Synthesis via Composition-Aided GANs
https://github.com/fei-hdu/sRender
공식 코드Bridging Unpaired Facial Photos And Sketches By Line-drawings
https://github.com/felixrosberg/facedancer
공식 코드FaceDancer: Pose- and Occlusion-Aware High Fidelity Face Swapping
https://github.com/fengju514/Expression-Net
공식 코드https://github.com/fengju514/Face-Pose-Net
공식 코드https://github.com/fengyuentau/pytorch-flops
공식 코드https://github.com/filby89/spectre
공식 코드Visual Speech-Aware Perceptual 3D Facial Expression Reconstruction from Videos
https://github.com/flakeua/bachelorsthesis
공식 코드Appearance-based gaze estimation enhanced with synthetic images using deep neural networks
https://github.com/flyingby/awesome-deepfake-generation-and-detection
공식 코드https://github.com/fodark/anonygan
공식 코드Graph-based Generative Face Anonymisation with Pose Preservation
https://github.com/forblindrev/aibias
공식 코드Fair and accurate age prediction using distribution aware data curation and augmentation
https://github.com/forever208/fmae-iat
공식 코드Representation Learning and Identity Adversarial Training for Facial Behavior Understanding
https://github.com/forget2save/campro
공식 코드https://github.com/freddiemg/btd--unsupervised-detection-of-medical-deepfakes
공식 코드Back-in-Time Diffusion: Unsupervised Detection of Medical Deepfakes
https://github.com/ftmghorbani/EVI-HRnet
공식 코드Expression-aware video inpainting for HMD removal in XR applications
https://github.com/fudandisc/socioverse
공식 코드https://github.com/fuenwang/mixfairface
공식 코드MixFairFace: Towards Ultimate Fairness via MixFair Adapter in Face Recognition
https://github.com/fusionbrainlab/adversarialfaces
공식 코드Inverting Black-Box Face Recognition Systems via Zero-Order Optimization in Eigenface Space
https://github.com/fuxivirtualhuman/styletalk
공식 코드StyleTalk: One-shot Talking Head Generation with Controllable Speaking Styles
https://github.com/fvmassoli/affwild2-challenge
공식 코드A Multi-resolution Approach to Expression Recognition in the Wild
https://github.com/fvmassoli/cross-resolution-face-recognition
공식 코드https://github.com/fvmassoli/mafer-multires-facial-expression-recognition
공식 코드MAFER: a Multi-resolution Approach to Facial Expression Recognition
https://github.com/fvmassoli/trj-based-adversarials-detection
공식 코드https://github.com/fwang91/IMDb-Face
공식 코드https://github.com/g-milis/NEUTART
공식 코드https://github.com/gabrella/qot
공식 코드Quaternion Orthogonal Transformer for Facial Expression Recognition in the Wild
https://github.com/gama-ufsc/brain-age
공식 코드Does pre-training on brain-related tasks results in better deep-learning-based brain age biomarkers?
https://github.com/gan-police/audiodeepfake-detection
공식 코드https://github.com/gan-police/frequency-forensics
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.