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/garynlfd/kfc
공식 코드KFC: Kinship Verification with Fair Contrastive Loss and Multi-Task Learning
https://github.com/gauravreddy08/OcularAI
공식 코드MagicEye: An Intelligent Wearable Towards Independent Living of Visually Impaired
https://github.com/genbing99/SoftNet-SpotME
공식 코드Shallow Optical Flow Three-Stream CNN for Macro- and Micro-Expression Spotting from Long Videos
https://github.com/germino-liuhe/dsat
공식 코드Precise Facial Landmark Detection by Dynamic Semantic Aggregation Transformer
https://github.com/gewanying/shap-anti-spoofing
공식 코드Explainable deepfake and spoofing detection: an attack analysis using SHapley Additive exPlanations
https://github.com/gilikn/fakeout
공식 코드FakeOut: Leveraging Out-of-domain Self-supervision for Multi-modal Video Deepfake Detection
https://github.com/gist-ailab/teaching-where-to-look
공식 코드https://github.com/git-disl/Chameleon
공식 코드Personalized Privacy Protection Mask Against Unauthorized Facial Recognition
https://github.com/gkaradzhov/ClickbaitRANLP
공식 코드We Built a Fake News & Click-bait Filter: What Happened Next Will Blow Your Mind!
https://github.com/gojasper/flash-diffusion
공식 코드Flash Diffusion: Accelerating Any Conditional Diffusion Model for Few Steps Image Generation
https://github.com/golnazghiasi/cofw68-benchmark
공식 코드Occlusion Coherence: Detecting and Localizing Occluded Faces
https://github.com/golnazghiasi/hpm-detection-code
공식 코드Occlusion Coherence: Detecting and Localizing Occluded Faces
https://github.com/gongsixue/DebFace
공식 코드Jointly De-biasing Face Recognition and Demographic Attribute Estimation
https://github.com/gonyrosenman/tff
공식 코드https://github.com/google/tf_mesh_renderer
공식 코드https://github.com/grigorisg9gr/deformable_tracking_review_ijcv2016
공식 코드A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"
https://github.com/grigorisg9gr/polynomial_nets
공식 코드https://github.com/grip-unina/id-reveal
공식 코드https://github.com/grip-unina/poi-forensics
공식 코드https://github.com/gsethan17/CAPNet_ABAW2021
공식 코드Causal affect prediction model using a facial image sequence
https://github.com/guanghaozhu663/skd-tstsan
공식 코드https://github.com/guanjiyang/sac_jc
공식 코드https://github.com/gufranSabri/FSBI
공식 코드FSBI: Deepfakes Detection with Frequency Enhanced Self-Blended Images
https://github.com/guillermogotre/cusp
공식 코드https://github.com/guoyang9/class-imbalance-VQA
공식 코드Loss re-scaling VQA: Revisiting the LanguagePrior Problem from a Class-imbalance View
https://github.com/guoyurong0104/crl-code
공식 코드Competing Ratio Loss for Discriminative Multi-class Image Classification
https://github.com/gurayozgur/foundpad
공식 코드FoundPAD: Foundation Models Reloaded for Face Presentation Attack Detection
https://github.com/gurayozgur/madation
공식 코드MADation: Face Morphing Attack Detection with Foundation Models
https://github.com/gy20073/compact_bilinear_pooling
공식 코드https://github.com/gyh5421/unified_deep_clustering
공식 코드https://github.com/hadushhailu/daal-dml
공식 코드DAAL: Density-Aware Adaptive Line Margin Loss for Multi-Modal Deep Metric Learning
https://github.com/haibo-qiu/SynFace
공식 코드https://github.com/haibo-qiu/from
공식 코드End2End Occluded Face Recognition by Masking Corrupted Features
https://github.com/haiyuwu/ba-test-dataset
공식 코드What Should Be Balanced in a "Balanced" Face Recognition Dataset?
https://github.com/haiyuwu/celebamso
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.