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/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 코드https://github.com/vladhondru25/exddv
공식 코드ExDDV: A New Dataset for Explainable Deepfake Detection in Video
https://github.com/vprlab/iexam
공식 코드iExam: A Novel Online Exam Monitoring and Analysis System Based on Face Detection and Recognition
https://github.com/vpsg-research/waveguard
공식 코드https://github.com/vrkh1996/IPU-Net
공식 코드https://github.com/vsingh-group/LCODEC-deep-unlearning
공식 코드Deep Unlearning via Randomized Conditionally Independent Hessians
https://github.com/vuvko/fitw2020
공식 코드Achieving Better Kinship Recognition Through Better Baseline
https://github.com/vvictoryuki/freedom
공식 코드FreeDoM: Training-Free Energy-Guided Conditional Diffusion Model
https://github.com/wagner-niklas/cage_expression_inference
공식 코드https://github.com/wang-zidu/3ddfa-v3
공식 코드3D Face Reconstruction with the Geometric Guidance of Facial Part Segmentation
https://github.com/wang-zidu/s2td-face
공식 코드S2TD-Face: Reconstruct a Detailed 3D Face with Controllable Texture from a Single Sketch
https://github.com/wangjk666/M2TR-Multi-modal-Multi-scale-Transformers-for-Deepfake-Detection
공식 코드M2TR: Multi-modal Multi-scale Transformers for Deepfake Detection
https://github.com/wangsuzhen/Audio2Head
공식 코드Audio2Head: Audio-driven One-shot Talking-head Generation with Natural Head Motion
https://github.com/wangty1/DCPT
공식 코드Deep Convolutional Pooling Transformer for Deepfake Detection
https://github.com/wangty1/LampMark
공식 코드LampMark: Proactive Deepfake Detection via Training-Free Landmark Perceptual Watermarks
https://github.com/wangxuan2401/3dfacerecon
공식 코드https://github.com/wangyanckxx/surveyfer
공식 코드A Survey on Facial Expression Recognition of Static and Dynamic Emotions
https://github.com/wangzhifengharrison/htnet
공식 코드https://github.com/wangzhuo2019/ssan
공식 코드Domain Generalization via Shuffled Style Assembly for Face Anti-Spoofing
https://github.com/wasim004/FAME
공식 코드FAME: A Lightweight Spatio-Temporal Network for Model Attribution of Face-Swap Deepfakes
https://github.com/wbteng9526/mtut_fr
공식 코드https://github.com/weiguangzhao/df_mvr
공식 코드From 2D Images to 3D Model:Weakly Supervised Multi-View Face Reconstruction with Deep Fusion
https://github.com/weihaox/TediGAN
공식 코드TediGAN: Text-Guided Diverse Face Image Generation and Manipulation
https://github.com/weimengting/magicportrait
공식 코드MagicPortrait: Temporally Consistent Face Reenactment with 3D Geometric Guidance
https://github.com/wentianzhang-ml/frt-pad
공식 코드FRT-PAD: Effective Presentation Attack Detection Driven by Face Related Task
https://github.com/wenzhengzeng/mpeblink
공식 코드Real-time Multi-person Eyeblink Detection in the Wild for Untrimmed Video
https://github.com/wholebody3d/wholebody3d
공식 코드https://github.com/wi-pi/fairness_face_obfuscation
공식 코드Fairness Properties of Face Recognition and Obfuscation Systems
https://github.com/wi-pi/limitations_of_face_generation
공식 코드https://github.com/wiebket/bt4vt
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.