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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/radu-dogaru/LB-CNN-compact-and-fast-binary-including-very-fast-ELM
공식 코드https://github.com/rafaelmmamede/fairness_ocfr
공식 코드Fairness Under Cover: Evaluating the Impact of Occlusions on Demographic Bias in Facial Recognition
https://github.com/rafaelvareto/maximal-entropy-loss
공식 코드Open-set Face Recognition with Neural Ensemble, Maximal Entropy Loss and Feature Augmentation
https://github.com/raining-dev/avt2-dwf
공식 코드AVT2-DWF: Improving Deepfake Detection with Audio-Visual Fusion and Dynamic Weighting Strategies
https://github.com/rainy-xu/tall4deepfake
공식 코드https://github.com/rama0126/pwtf-dvd
공식 코드Beyond Spatial Frequency: Pixel-wise Temporal Frequency-based Deepfake Video Detection
https://github.com/ramihaf/mtf_data_set
공식 코드https://github.com/raphaelmemmesheimer/sl-dml
공식 코드SL-DML: Signal Level Deep Metric Learning for Multimodal One-Shot Action Recognition
https://github.com/raywang335/l2rclip
공식 코드https://github.com/razla/xai-based-detection-of-adversarial-attacks-on-deepfake-detectors
공식 코드XAI-Based Detection of Adversarial Attacks on Deepfake Detectors
https://github.com/realityengines/post_hoc_debiasing
공식 코드https://github.com/recluzegeek/AWARE-NET
공식 코드AWARE-NET: Adaptive Weighted Averaging for Robust Ensemble Network in Deepfake Detection
https://github.com/redcping/Delta_Age_AdaIN
공식 코드DAA: A Delta Age AdaIN operation for age estimation via binary code transformer
https://github.com/renderme-360/renderme-360
공식 코드RenderMe-360: A Large Digital Asset Library and Benchmarks Towards High-fidelity Head Avatars
https://github.com/renmin1991/perturbation-inactivate
공식 코드Perturbation Inactivation Based Adversarial Defense for Face Recognition
https://github.com/rgb91/temporal-deepfake-segmentation
공식 코드https://github.com/riaduli/thresholded_event_vision_face_dataset
공식 코드Descriptor: Face Detection Dataset for Programmable Threshold-Based Sparse-Vision
https://github.com/ristea/xmad-bench
공식 코드XMAD-Bench: Cross-Domain Multilingual Audio Deepfake Benchmark
https://github.com/rizhaocai/metapattern_fas
공식 코드https://github.com/rlit/LatentRANSAC
공식 코드https://github.com/rmushfiqur2/deepfake_op_rel
공식 코드Individualized Deepfake Detection Exploiting Traces Due to Double Neural-Network Operations
https://github.com/rohdelab/drcdt_face
공식 코드Local Sliced-Wasserstein Feature Sets for Illumination-invariant Face Recognition
https://github.com/rohit7044/3D-GANTex
공식 코드https://github.com/ronny3050/PrimateFaceRecognition
공식 코드https://github.com/roscibely/Gender-Classification
공식 코드Predicting Gender by First Name Using Character-level Machine Learning
https://github.com/royorel/FFHQ-Aging-Dataset
공식 코드https://github.com/royorel/Lifespan_Age_Transformation_Synthesis
공식 코드https://github.com/royorel/StyleSDF
공식 코드StyleSDF: High-Resolution 3D-Consistent Image and Geometry Generation
https://github.com/rperi/trustworthy-asv-fairness
공식 코드https://github.com/rshaojimmy/AAAI2020-RFMetaFAS
공식 코드https://github.com/rshaojimmy/deepfake-adapter
공식 코드https://github.com/rshaojimmy/multimodal-deepfake
공식 코드https://github.com/rshaojimmy/seqdeepfake
공식 코드https://github.com/rub-syssec/generatedmediasurvey
공식 코드A Representative Study on Human Detection of Artificially Generated Media Across Countries
https://github.com/rub-syssec/wavefake
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.