https://github.com/hongshuochen/DefakeHop
공식 코드DefakeHop: A Light-Weight High-Performance Deepfake Detector
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/hotfinda/Compensation-sampling
공식 코드Compensation Sampling for Improved Convergence in Diffusion Models
https://github.com/huage001/dynast
공식 코드DynaST: Dynamic Sparse Transformer for Exemplar-Guided Image Generation
https://github.com/huangjiadidi/DeepFakeMnist
공식 코드https://github.com/huangyangyu/ADNet
공식 코드ADNet: Leveraging Error-Bias Towards Normal Direction in Face Alignment
https://github.com/huangyangyu/NoiseFace
공식 코드https://github.com/huangyangyu/SeqFace
공식 코드SeqFace: Make full use of sequence information for face recognition
https://github.com/huashen218/llm-deepfake-human-study
공식 코드Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?
https://github.com/huitailangyz/AFGAN
공식 코드Attributes Aware Face Generation with Generative Adversarial Networks
https://github.com/humanlab/hart
공식 코드https://github.com/humansensinglab/PATMAT
공식 코드PATMAT: Person Aware Tuning of Mask-Aware Transformer for Face Inpainting
https://github.com/hurricanelx/lrfr
공식 코드https://github.com/hwang1996/3D-Cartoon-Face-Generation
공식 코드3D Cartoon Face Generation with Controllable Expressions from a Single GAN Image
https://github.com/hxngiee/DiffFace
공식 코드DiffFace: Diffusion-based Face Swapping with Facial Guidance
https://github.com/i13abe/Adaptive-discriminant-and-center
공식 코드https://github.com/iamsangmeng/interface
공식 코드InterFace:Adjustable Angular Margin Inter-class Loss for Deep Face Recognition
https://github.com/iamwangyabin/poundnet
공식 코드https://github.com/ibug-group/face_reid
공식 코드A real-time and unsupervised face Re-Identification system for Human-Robot Interaction
https://github.com/ibug-group/fpage
공식 코드FP-Age: Leveraging Face Parsing Attention for Facial Age Estimation in the Wild
https://github.com/idstcv/mogface
공식 코드https://github.com/idt-iti/lightweight-face-detector-pruning
공식 코드Filter-Pruning of Lightweight Face Detectors Using a Geometric Median Criterion
https://github.com/idt-iti/xai-deepfakes
공식 코드Towards Quantitative Evaluation of Explainable AI Methods for Deepfake Detection
https://github.com/igor-morawski/tmm-sem
공식 코드Leveraging Content and Context Cues for Low-Light Image Enhancement
https://github.com/igsb/gestaltmatcher-arc
공식 코드Improving Deep Facial Phenotyping for Ultra-rare Disorder Verification Using Model Ensembles
https://github.com/ihp-lab/fg-net
공식 코드FG-Net: Facial Action Unit Detection with Generalizable Pyramidal Features
https://github.com/iman2693/gdsm
공식 코드Brain Age Estimation with a Greedy Dual-Stream Model for Limited Datasets
https://github.com/imatge-upc/wav2pix
공식 코드Wav2Pix: Speech-conditioned Face Generation using Generative Adversarial Networks
https://github.com/iperov/DeepFaceLab
공식 코드DeepFaceLab: Integrated, flexible and extensible face-swapping framework
https://github.com/iremeyiokur/multipie_extended_ear_dataset
공식 코드Multimodal Age and Gender Classification Using Ear and Profile Face Images
https://github.com/irisdominguez/dataset-bias-metrics
공식 코드https://github.com/irvingmeng/securevector
공식 코드Towards Privacy-Preserving, Real-Time and Lossless Feature Matching
https://github.com/isi-vista/face-completion
공식 코드https://github.com/isspek/cross-lingual-cyberbullying
공식 코드Unified and Multilingual Author Profiling for Detecting Haters
https://github.com/ivclab/CPG
공식 코드Compacting, Picking and Growing for Unforgetting Continual Learning
https://github.com/ivclab/Online-Face-Recognition-and-Authentication
공식 코드Data-specific Adaptive Threshold for Face Recognition and Authentication
https://github.com/ivonacolakovic/synthgap-mitigation-using-kd-in-ffr
공식 코드How Knowledge Distillation Mitigates the Synthetic Gap in Fair Face Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.