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/QingyuZhao/BR-Net
공식 코드Representation Learning with Statistical Independence to Mitigate Bias
https://github.com/RQuispeC/multi-stream-crowd-counting-extended
공식 코드Multi-Stream Networks and Ground-Truth Generation for Crowd Counting
https://github.com/RameshaKaru/devi
공식 코드DEVI: Open-source Human-Robot Interface for Interactive Receptionist Systems
https://github.com/Raschka-research-group/coral-cnn
공식 코드Rank consistent ordinal regression for neural networks with application to age estimation
https://github.com/Raul2718/FLUXSynID
공식 코드https://github.com/RenMin1991/SIDE
공식 코드Artificial Immune System of Secure Face Recognition Against Adversarial Attacks
https://github.com/Rock-100/FaceKit
공식 코드Real-Time Rotation-Invariant Face Detection with Progressive Calibration Networks
https://github.com/Rohith-hacker/realtime-faceid
공식 코드https://github.com/RoyYuchenHe/BlockSparse
공식 코드https://github.com/Rudrabha/LipGAN
공식 코드https://github.com/Rudrabha/Wav2Lip
공식 코드A Lip Sync Expert Is All You Need for Speech to Lip Generation In The Wild
https://github.com/SCL-UT/face-manifold
공식 코드Face Manifold: Manifold Learning for Synthetic Face Generation
https://github.com/SCUT-AILab/DCP
공식 코드Discrimination-aware Network Pruning for Deep Model Compression
https://github.com/SHI-Labs/StyleNAT
공식 코드https://github.com/Seanseattle/MobileFaceSwap
공식 코드MobileFaceSwap: A Lightweight Framework for Video Face Swapping
https://github.com/Shawn-Shan/fawkes
공식 코드Fawkes: Protecting Privacy against Unauthorized Deep Learning Models
https://github.com/Shawn-Shan/trapdoor
공식 코드Gotta Catch 'Em All: Using Honeypots to Catch Adversarial Attacks on Neural Networks
https://github.com/ShawnXYang/Face-Robustness-Benchmark
공식 코드RobFR: Benchmarking Adversarial Robustness on Face Recognition
https://github.com/SheepBreedingLab-HZAU/LAD-RCNN
공식 코드LAD-RCNN:A Powerful Tool for Livestock Face Detection and Normalization
https://github.com/Star-Clouds/CenterFace
공식 코드CenterFace: Joint Face Detection and Alignment Using Face as Point
https://github.com/Stillrot/RGINP
공식 코드https://github.com/THUIR/ZhihuRec-Dataset
공식 코드A Large-Scale Rich Context Query and Recommendation Dataset in Online Knowledge-Sharing
https://github.com/TadasBaltrusaitis/OpenFace
공식 코드Convolutional Experts Constrained Local Model for Facial Landmark Detection
https://github.com/TaharChettaoui/FRoundation
공식 코드FRoundation: Are Foundation Models Ready for Face Recognition?
https://github.com/Tencent/TFace
공식 코드SDD-FIQA: Unsupervised Face Image Quality Assessment with Similarity Distribution Distance
https://github.com/Tencent/TFace/tree/Adv-Attack-Defense/security/tasks/Adv-Attack-Defense/Sibling-Attack
공식 코드Sibling-Attack: Rethinking Transferable Adversarial Attacks against Face Recognition
https://github.com/TencentYoutuResearch/OpenWorld-DeepFakeAttribution
공식 코드Contrastive Pseudo Learning for Open-World DeepFake Attribution
https://github.com/TimoBolkart/GlobalLocalFaceModels
공식 코드Review of Statistical Shape Spaces for 3D Data with Comparative Analysis for Human Faces
https://github.com/TimoBolkart/MultilinearWaveletModel
공식 코드Multilinear Wavelets: A Statistical Shape Space for Human Faces
https://github.com/TimoBolkart/TEMPEH
공식 코드Instant Multi-View Head Capture through Learnable Registration
https://github.com/TimoBolkart/voca
공식 코드https://github.com/ToniCreswell/attribute-cVAEGAN
공식 코드https://github.com/USTC-IMCL/Task-driven-Semantic-Coding-via-RL
공식 코드https://github.com/UTAustin-SwarmLab/SecureSpectra
공식 코드SecureSpectra: Safeguarding Digital Identity from Deep Fake Threats via Intelligent Signatures
https://github.com/Usman1021/DLE
공식 코드Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.