https://github.com/aliprf/ganalyzer
공식 코드GANalyzer: Analysis and Manipulation of GANs Latent Space for Controllable Face Synthesis
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/aliprf/kd-loss
공식 코드Facial Landmark Points Detection Using Knowledge Distillation-Based Neural Networks
https://github.com/alldbi/FLM
공식 코드https://github.com/alonzolfi/adversarialmask
공식 코드Adversarial Mask: Real-World Universal Adversarial Attack on Face Recognition Model
https://github.com/altndrr/clup
공식 코드Cluster-level pseudo-labelling for source-free cross-domain facial expression recognition
https://github.com/amazon-research/gendered-profession-translations
공식 코드Faithful Target Attribute Prediction in Neural Machine Translation
https://github.com/amilkh/cs230-fer
공식 코드https://github.com/aminesi/robustness-on-device
공식 코드Robustness Analysis of Deep Learning Frameworks on Mobile Platforms
https://github.com/amoonfana/fastface
공식 코드FastFace: Fast-converging Scheduler for Large-scale Face Recognition Training with One GPU
https://github.com/andrekassis/DiffBreak
공식 코드https://github.com/andrekassis/ai-watermark
공식 코드UnMarker: A Universal Attack on Defensive Image Watermarking
https://github.com/andresprados/spiga
공식 코드Shape Preserving Facial Landmarks with Graph Attention Networks
https://github.com/angelomenezes/Pytorch_Face_SR
공식 코드https://github.com/anger-man/unsupervised-depth-estimation
공식 코드Unsupervised Single-shot Depth Estimation using Perceptual Reconstruction
https://github.com/anguyen8/deepface-emd
공식 코드https://github.com/anguyen8/face-vit
공식 코드Fast and Interpretable Face Identification for Out-Of-Distribution Data Using Vision Transformers
https://github.com/anhttran/extreme_3d_faces
공식 코드https://github.com/anjith2006/bob.paper.deep_pix_bis_pad.icb2019
공식 코드Deep Pixel-wise Binary Supervision for Face Presentation Attack Detection
https://github.com/anjith2006/bob.paper.ijcb2021_vision_transformer_pad
공식 코드On the Effectiveness of Vision Transformers for Zero-shot Face Anti-Spoofing
https://github.com/anmol2059/svdd2024
공식 코드https://github.com/anonrep/iba-for-video-sequences
공식 코드Extending Information Bottleneck Attribution to Video Sequences
https://github.com/anonymous-author-sub/seeable
공식 코드SeeABLE: Soft Discrepancies and Bounded Contrastive Learning for Exposing Deepfakes
https://github.com/anonymoususera/ava
공식 코드AVA: Inconspicuous Attribute Variation-based Adversarial Attack bypassing DeepFake Detection
https://github.com/antonibigata/Laughing-Matters
공식 코드Laughing Matters: Introducing Laughing-Face Generation using Diffusion Models
https://github.com/anubhav1997/youneednodataset
공식 코드A Dataless FaceSwap Detection Approach Using Synthetic Images
https://github.com/anuragranj/coma
공식 코드https://github.com/aosokin/cnn_head_detection
공식 코드https://github.com/apchenstu/Facial_Details_Synthesis
공식 코드https://github.com/aqeelanwar/MaskTheFace
공식 코드https://github.com/arassadin/emotiw2017
공식 코드Group-level Emotion Recognition using Transfer Learning from Face Identification
https://github.com/aravinda27/extswap
공식 코드ExtSwap: Leveraging Extended Latent Mapper for Generating High Quality Face Swapping
https://github.com/architect-road/icongan
공식 코드Design What You Desire: Icon Generation from Orthogonal Application and Theme Labels
https://github.com/arctrinity9/datasets-reachocc-and-synchocc
공식 코드Self-Supervised Robustifying Guidance for Monocular 3D Face Reconstruction
https://github.com/aria-zhangjl/e3-facenet
공식 코드https://github.com/arin-upna/biased-heritage
공식 코드Biased Heritage: How Datasets Shape Models in Facial Expression Recognition
https://github.com/arkel23/animesion
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.