https://github.com/dasec/dataset-duplicates
공식 코드Considerations on the Evaluation of Biometric Quality Assessment Algorithms
Papers with Code 아카이브
3D Face Reconstruction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 9
- 보존된 평가 기록
- 94
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/dasec/hda-synchildfaces-agetransformation
공식 코드Child Face Recognition at Scale: Synthetic Data Generation and Performance Benchmark
https://github.com/dasec/ladimo
공식 코드LADIMO: Face Morph Generation through Biometric Template Inversion with Latent Diffusion
https://github.com/dasec/tetraloss
공식 코드TetraLoss: Improving the Robustness of Face Recognition against Morphing Attacks
https://github.com/dash-lab/fakeavceleb
공식 코드FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset
https://github.com/datamllab/Mitigating_Gender_Bias_In_Captioning_System
공식 코드https://github.com/datasciencegrimmer/syn-yawpitch
공식 코드https://github.com/david-svitov/margindistillation
공식 코드https://github.com/davidberend/FairnessMatters
공식 코드Fair and accurate age prediction using distribution aware data curation and augmentation
https://github.com/davidcombei/ai4t
공식 코드Unmasking real-world audio deepfakes: A data-centric approach
https://github.com/davide-coccomini/Combining-EfficientNet-and-Vision-Transformers-for-Video-Deepfake-Detection
공식 코드Combining EfficientNet and Vision Transformers for Video Deepfake Detection
https://github.com/davide-coccomini/Combining-EfficientNet-and-Vision-Transformersfor-Video-Deepfake-Detection
공식 코드Combining EfficientNet and Vision Transformers for Video Deepfake Detection
https://github.com/davide-coccomini/Cross-Forgery-Analysis-of-Vision-Transformers-and-CNNs-for-Deepfake-Image-Detection
공식 코드Cross-Forgery Analysis of Vision Transformers and CNNs for Deepfake Image Detection
https://github.com/davide-coccomini/adversarial-magnification-to-deceive-deepfake-detection-through-super-resolution
공식 코드Adversarial Magnification to Deceive Deepfake Detection through Super Resolution
https://github.com/davide-coccomini/deepfake-detection-without-deepfakes-generalization-via-synthetic-frequency-patterns-injection
공식 코드Deepfake Detection without Deepfakes: Generalization via Synthetic Frequency Patterns Injection
https://github.com/davide-coccomini/mintime-multi-identity-size-invariant-timesformer-for-video-deepfake-detection
공식 코드MINTIME: Multi-Identity Size-Invariant Video Deepfake Detection
https://github.com/davidsandberg/facenet
공식 코드https://github.com/ddw2aigroup2cqupt/mgrl
공식 코드Multi-Granularity Representation Learning for Sketch-based Dynamic Face Image Retrieval
https://github.com/ddw2aigroup2cqupt/slfir
공식 코드https://github.com/deepalianeja/CharacterLipSync2D
공식 코드https://github.com/deepbrainai-research/koeba
공식 코드https://github.com/deepcam-cn/facequality
공식 코드EQFace: A Simple Explicit Quality Network for Face Recognition
https://github.com/deepcam-cn/yolov5-face
공식 코드https://github.com/deepfakeil/dfil
공식 코드DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery Clues
https://github.com/deepinsight/insightface
공식 코드ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/deepinsight/insightface/tree/master/challenges/cvpr23-fas-wild
공식 코드Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results
https://github.com/deepinsight/insightface/tree/master/recognition/partial_fc
공식 코드Partial FC: Training 10 Million Identities on a Single Machine
https://github.com/deeplsd/merkel-podcast-corpus
공식 코드https://github.com/deezer/deepfake-detector
공식 코드https://github.com/deezer/sigir2019-2stagesampling
공식 코드Improving Collaborative Metric Learning with Efficient Negative Sampling
https://github.com/delaprada/handsynthesis
공식 코드Analyzing the Synthetic-to-Real Domain Gap in 3D Hand Pose Estimation
https://github.com/dflip3k/dflip-3k
공식 코드Linguistic Profiling of Deepfakes: An Open Database for Next-Generation Deepfake Detection
https://github.com/dgragnaniello/PQP
공식 코드Perceptual Quality-preserving Black-Box Attack against Deep Learning Image Classifiers
https://github.com/dineshsrivasthav/adaptive_meta_learning_with_multi_agent_framework
공식 코드https://github.com/dituu/Semi-Siamese-Training
공식 코드https://github.com/ditzley/joint-gestures-and-face
공식 코드Joint Co-Speech Gesture and Expressive Talking Face Generation using Diffusion with Adapters
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.