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/msy1412/abaw4
공식 코드AU-Supervised Convolutional Vision Transformers for Synthetic Facial Expression Recognition
https://github.com/murinj/big-moe
공식 코드BIG-MoE: Bypass Isolated Gating MoE for Generalized Multimodal Face Anti-Spoofing
https://github.com/murphytju/difffas
공식 코드https://github.com/muse1998/IAL-for-Facial-Expression-Recognition
공식 코드Intensity-Aware Loss for Dynamic Facial Expression Recognition in the Wild
https://github.com/muse1998/mmnet
공식 코드MMNet: Muscle motion-guided network for micro-expression recognition
https://github.com/mvillarreal14/confoc
공식 코드ConFoc: Content-Focus Protection Against Trojan Attacks on Neural Networks
https://github.com/mysee1989/GraphJigsaw
공식 코드https://github.com/mzhang367/opqn
공식 코드Orthonormal Product Quantization Network for Scalable Face Image Retrieval
https://github.com/naserdamer/mordiff
공식 코드https://github.com/natanielruiz/deep-head-pose
공식 코드https://github.com/natanielruiz/dockerface
공식 코드Dockerface: an Easy to Install and Use Faster R-CNN Face Detector in a Docker Container
https://github.com/nathanlem1/igae-net
공식 코드https://github.com/nchinaev/MobileFace
공식 코드MobileFace: 3D Face Reconstruction with Efficient CNN Regression
https://github.com/nctu-eva-lab/antifakeprompt
공식 코드AntifakePrompt: Prompt-Tuned Vision-Language Models are Fake Image Detectors
https://github.com/ndb796/machineunlearning
공식 코드https://github.com/ndido98/frcsyn
공식 코드FRCSyn Challenge at WACV 2024:Face Recognition Challenge in the Era of Synthetic Data
https://github.com/ndrwmlnk/ai-blitz-xiii
공식 코드https://github.com/ndrwmlnk/deep-face-generation-and-editing-a-survey
공식 코드https://github.com/negarhdr/diversified-facial-expression-recognition
공식 코드Learning Diversified Feature Representations for Facial Expression Recognition in the Wild
https://github.com/nenadmarkus/pico
공식 코드Object Detection with Pixel Intensity Comparisons Organized in Decision Trees
https://github.com/nerminsamet/HPRNet
공식 코드HPRNet: Hierarchical Point Regression for Whole-Body Human Pose Estimation
https://github.com/netopedro/idistill
공식 코드Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detection
https://github.com/netopedro/ocfr-2022
공식 코드https://github.com/netopedro/orthomad
공식 코드OrthoMAD: Morphing Attack Detection Through Orthogonal Identity Disentanglement
https://github.com/neuralchen/SimSwap
공식 코드SimSwap: An Efficient Framework For High Fidelity Face Swapping
https://github.com/neuromorphic-paris/frog
공식 코드A Framework for Event-based Computer Vision on a Mobile Device
https://github.com/nhshin-mcl/mwr
공식 코드Moving Window Regression: A Novel Approach to Ordinal Regression
https://github.com/niccobiondi/cores-compatibility
공식 코드https://github.com/nice-x/dfgc-2022
공식 코드https://github.com/nicksunday/deepfake-music-detector
공식 코드https://github.com/nickyfot/emoclip
공식 코드EmoCLIP: A Vision-Language Method for Zero-Shot Video Facial Expression Recognition
https://github.com/nii-yamagishilab/AfriHuBERT
공식 코드AfriHuBERT: A self-supervised speech representation model for African languages
https://github.com/nii-yamagishilab/antideepfake
공식 코드https://github.com/nii-yamagishilab/ssl_vits_df
공식 코드Exploring Self-Supervised Vision Transformers for Deepfake Detection: A Comparative Analysis
https://github.com/nilslukas/gan-watermark
공식 코드PTW: Pivotal Tuning Watermarking for Pre-Trained Image Generators
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.