https://github.com/tvaranka/meb
공식 코드Data Leakage and Evaluation Issues in Micro-Expression Analysis
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/tyshiwo/FHR_alignment
공식 코드Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos
https://github.com/tyshiwo/FSRNet
공식 코드FSRNet: End-to-End Learning Face Super-Resolution with Facial Priors
https://github.com/uam-biometrics/FaceQnet
공식 코드FaceQnet: Quality Assessment for Face Recognition based on Deep Learning
https://github.com/uam-biometrics/faceqgen
공식 코드FaceQgen: Semi-Supervised Deep Learning for Face Image Quality Assessment
https://github.com/uark-cviu/micron-bert
공식 코드https://github.com/uds-lsv/author-profiling-prevention-bt
공식 코드Preventing Author Profiling through Zero-Shot Multilingual Back-Translation
https://github.com/umgupta/2d-slice-set-networks-for-brain-age
공식 코드https://github.com/unibas-gravis/Occlusion-Robust-MoFA
공식 코드Robust Model-based Face Reconstruction through Weakly-Supervised Outlier Segmentation
https://github.com/unibas-gravis/parametric-face-image-generator
공식 코드Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems
https://github.com/usef-kh/fer
공식 코드Facial Emotion Recognition: State of the Art Performance on FER2013
https://github.com/usingcolor/latentswap
공식 코드LatentSwap: An Efficient Latent Code Mapping Framework for Face Swapping
https://github.com/usmancheema89/SejongFaceDatabase
공식 코드https://github.com/uynaes/rankingawareclip
공식 코드Ranking-aware adapter for text-driven image ordering with CLIP
https://github.com/v0lta/pytorch-wavelet-toolbox
공식 코드https://github.com/vcbsl-fairness/fineface
공식 코드FineFACE: Fair Facial Attribute Classification Leveraging Fine-grained Features
https://github.com/vcbsl/audio-visual-deepfake
공식 코드Contextual Cross-Modal Attention for Audio-Visual Deepfake Detection and Localization
https://github.com/vchoutas/expose
공식 코드Monocular Expressive Body Regression through Body-Driven Attention
https://github.com/vcllab/ifqa
공식 코드https://github.com/vdigpku/cmua-watermark
공식 코드CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes
https://github.com/verlab/empowering-sign-language
공식 코드https://github.com/viebboy/MultilinearCompressiveLearningFramework
공식 코드https://github.com/viebboy/SIPL
공식 코드https://github.com/vigor-jzhang/dysphonic-emb
공식 코드Robust Vocal Quality Feature Embeddings for Dysphonic Voice Detection
https://github.com/vineetkumarrakesh/thg
공식 코드https://github.com/vis-face/cdftn
공식 코드Cyclically Disentangled Feature Translation for Face Anti-spoofing
https://github.com/vis-opt-group/sci
공식 코드Toward Fast, Flexible, and Robust Low-Light Image Enhancement
https://github.com/vis-var/lgsc-for-fas
공식 코드https://github.com/vishal3477/Reverse_Engineering_GMs
공식 코드Reverse Engineering of Generative Models: Inferring Model Hyperparameters from Generated Images
https://github.com/visionjo/facerec-bias-bfw
공식 코드https://github.com/visteam-isr-uc/ylfw
공식 코드Young Labeled Faces in the Wild (YLFW): A Dataset for Children Faces Recognition
https://github.com/visym/keynet
공식 코드Key-Nets: Optical Transformation Convolutional Networks for Privacy Preserving Vision Sensors
https://github.com/vitoralbiero/afd_dataset_cleaned
공식 코드https://github.com/vitoralbiero/gender_balance_training_data
공식 코드How Does Gender Balance In Training Data Affect Face Recognition Accuracy?
https://github.com/vitoralbiero/img2pose
공식 코드img2pose: Face Alignment and Detection via 6DoF, Face Pose Estimation
https://github.com/vitoralbiero/vggface2_test_cleaned
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.