https://github.com/sanoojan/reface
공식 코드Realistic and Efficient Face Swapping: A Unified Approach with Diffusion Models
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/sariyanidi/M3DFB
공식 코드3D Face Reconstruction Error Decomposed: A Modular Benchmark for Fair and Fast Method Evaluation
https://github.com/sashankmvv/Emotion-recognition
공식 코드Facial Emotion Recognition: A multi-task approach using deep learning
https://github.com/sbelharbi/interpretable-fer-aus
공식 코드Guided Interpretable Facial Expression Recognition via Spatial Action Unit Cues
https://github.com/sbelharbi/structured-output-ae
공식 코드Deep Neural Networks Regularization for Structured Output Prediction
https://github.com/sccsok/Locate-and-Verify
공식 코드Locate and Verify: A Two-Stream Network for Improved Deepfake Detection
https://github.com/sciencefans/Quality-Aware-Network
공식 코드https://github.com/sciencefans/RSA-for-object-detection
공식 코드https://github.com/sclbd/deepfakebench
공식 코드DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection
https://github.com/scu-zjz/forensichub
공식 코드ForensicHub: A Unified Benchmark & Codebase for All-Domain Fake Image Detection and Localization
https://github.com/searecluse/cvprw2024
공식 코드https://github.com/seasonSH/LiftedGAN
공식 코드https://github.com/seasonSH/Probabilistic-Face-Embeddings
공식 코드https://github.com/secml-lab-vt/EvolvingThreat-DeepfakeImageDetect
공식 코드An Analysis of Recent Advances in Deepfake Image Detection in an Evolving Threat Landscape
https://github.com/securifai/masked_faces
공식 코드A realistic approach to generate masked faces applied on two novel masked face recognition data sets
https://github.com/sefaburakokcu/finn-quantized-yolo
공식 코드https://github.com/semchan/HyperLips
공식 코드HyperLips: Hyper Control Lips with High Resolution Decoder for Talking Face Generation
https://github.com/seva100/stylegan-for-facerec
공식 코드https://github.com/seymayucer/facialphenotypes
공식 코드Measuring Hidden Bias within Face Recognition via Racial Phenotypes
https://github.com/sfzhang15/FaceBoxes
공식 코드https://github.com/sfzhang15/SFD
공식 코드https://github.com/sh1newu/advmark
공식 코드Are Watermarks Bugs for Deepfake Detectors? Rethinking Proactive Forensics
https://github.com/sh1newu/sepmark
공식 코드SepMark: Deep Separable Watermarking for Unified Source Tracing and Deepfake Detection
https://github.com/shahroztariq/CLRNet
공식 코드A Convolutional LSTM based Residual Network for Deepfake Video Detection
https://github.com/shaibagon/large_scale_cc
공식 코드https://github.com/shanface33/deepfake_model_attribution
공식 코드https://github.com/shanface33/gpt4mf_ub
공식 코드Can ChatGPT Detect DeepFakes? A Study of Using Multimodal Large Language Models for Media Forensics
https://github.com/shangeth/Speaker-Representation-for-Speaker-Profiling
공식 코드Learning Speaker Representation with Semi-supervised Learning approach for Speaker Profiling
https://github.com/sharpstill/AU_R-CNN
공식 코드AU R-CNN: Encoding Expert Prior Knowledge into R-CNN for Action Unit Detection
https://github.com/shawnxyang/tip-im
공식 코드Towards Face Encryption by Generating Adversarial Identity Masks
https://github.com/shengfly/global-local-transformer
공식 코드https://github.com/sherrycattt/REGDet
공식 코드https://github.com/shighghyujie/newpatch-rl
공식 코드Simultaneously Optimizing Perturbations and Positions for Black-box Adversarial Patch Attacks
https://github.com/shiluo1990/SFA
공식 코드https://github.com/shiluo1990/bdc
공식 코드Bounding-box deep calibration for high performance face detection
https://github.com/shimmer-ghq/deepfidelity
공식 코드DeepFidelity: Perceptual Forgery Fidelity Assessment for Deepfake Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.