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3D Face Reconstruction

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하위 태스크: 1
평가표: 9
보존된 평가 기록: 94

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/prevso1088/open-set-biometrics

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Open-Set Biometrics: Beyond Good Closed-Set Models

https://github.com/prijatelj/bayesian_eval_ground_truth-free

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A Bayesian Evaluation Framework for Subjectively Annotated Visual Recognition Tasks

https://github.com/protossw512/AdaptiveWingLoss

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Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression

https://github.com/pterhoer/DeepFakeAnnotations

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Analyzing Fairness in Deepfake Detection With Massively Annotated Databases

https://github.com/pterhoer/ExplainableFaceImageQuality

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Pixel-Level Face Image Quality Assessment for Explainable Face Recognition

https://github.com/pterhoer/FairScoreNormalization

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Post-Comparison Mitigation of Demographic Bias in Face Recognition Using Fair Score Normalization

https://github.com/pterhoer/MAAD-Face

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MAAD-Face: A Massively Annotated Attribute Dataset for Face Images

https://github.com/pterhoer/PrivacyPreservingFaceRecognition

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Unsupervised Enhancement of Soft-biometric Privacy with Negative Face Recognition

https://github.com/pterhoer/QMagFace

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QMagFace: Simple and Accurate Quality-Aware Face Recognition

https://github.com/pterhoer/optimalmatchingconfidence

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PIC-Score: Probabilistic Interpretable Comparison Score for Optimal Matching Confidence in Single- and Multi-Biometric (Face) Recognition

https://github.com/purdue-m2/ai-face-fairnessbench

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AI-Face: A Million-Scale Demographically Annotated AI-Generated Face Dataset and Fairness Benchmark

https://github.com/purdue-m2/auc_fairness_with_noisy_groups

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Preserving AUC Fairness in Learning with Noisy Protected Groups

https://github.com/purdue-m2/fairness-generalization

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Preserving Fairness Generalization in Deepfake Detection

https://github.com/purdue-m2/sp_cup

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Robust AI-Generated Face Detection with Imbalanced Data

https://github.com/purpleleaves007/ferdenoise

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ReSup: Reliable Label Noise Suppression for Facial Expression Recognition

https://github.com/pvskand/DisguiseNet

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DisguiseNet : A Contrastive Approach for Disguised Face Verification in the Wild

https://github.com/qingyuliu/exposing-the-deception

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Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection

https://github.com/qinxiaoyi/cross-age_speaker_verification

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Cross-Age Speaker Verification: Learning Age-Invariant Speaker Embeddings

https://github.com/qiqitao77/awesome-comprehensive-deepfake-detection

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Evolving from Single-modal to Multi-modal Facial Deepfake Detection: A Survey

https://github.com/qiqitao77/comprehensive-advances-in-deepfake-detection-spanning-diverse-modalities

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Evolving from Single-modal to Multi-modal Facial Deepfake Detection: A Survey

https://github.com/qizhangli/nobox-attacks

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Practical No-box Adversarial Attacks against DNNs

https://github.com/quarxilon/generator_attribution

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Transferable Class-Modelling for Decentralized Source Attribution of GAN-Generated Images

https://github.com/quereste/implicit-deepfake

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Deepfake for the Good: Generating Avatars through Face-Swapping with Implicit Deepfake Generation

https://github.com/radekd91/emoca

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EMOCA: Emotion Driven Monocular Face Capture and Animation

https://github.com/radu-dogaru/LB-CNN-compact-and-fast-binary-including-very-fast-ELM

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LB-CNN: An Open Source Framework for Fast Training of Light Binary Convolutional Neural Networks using Chainer and Cupy

https://github.com/rafaelmmamede/fairness_ocfr

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Fairness Under Cover: Evaluating the Impact of Occlusions on Demographic Bias in Facial Recognition

https://github.com/rafaelvareto/maximal-entropy-loss

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Open-set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature Augmentation

https://github.com/raining-dev/avt2-dwf

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AVT2-DWF: Improving Deepfake Detection with Audio-Visual Fusion and Dynamic Weighting Strategies

https://github.com/rainy-xu/tall4deepfake

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TALL: Thumbnail Layout for Deepfake Video Detection

https://github.com/rama0126/pwtf-dvd

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Beyond Spatial Frequency: Pixel-wise Temporal Frequency-based Deepfake Video Detection

https://github.com/ramihaf/mtf_data_set

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Multi-Task Faces (MTF) Data Set: A Legally and Ethically Compliant Collection of Face Images for Various Classification Tasks

https://github.com/raphaelmemmesheimer/sl-dml

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SL-DML: Signal Level Deep Metric Learning for Multimodal One-Shot Action Recognition

https://github.com/raywang335/l2rclip

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Learning-to-Rank Meets Language: Boosting Language-Driven Ordering Alignment for Ordinal Classification

https://github.com/razla/xai-based-detection-of-adversarial-attacks-on-deepfake-detectors

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XAI-Based Detection of Adversarial Attacks on Deepfake Detectors

https://github.com/realityengines/post_hoc_debiasing

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Intra-Processing Methods for Debiasing Neural Networks

https://github.com/recluzegeek/AWARE-NET

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AWARE-NET: Adaptive Weighted Averaging for Robust Ensemble Network in Deepfake Detection

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.