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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/microsoft/auto-formula
공식 코드https://github.com/microsoft/digiface1m
공식 코드DigiFace-1M: 1 Million Digital Face Images for Face Recognition
https://github.com/miliadis/FIRC
공식 코드Robust and Low-Rank Representation for Fast Face Identification with Occlusions
https://github.com/milliema/invreg
공식 코드https://github.com/ming053l/grace
공식 코드https://github.com/mingwuzheng/imface
공식 코드ImFace: A Nonlinear 3D Morphable Face Model with Implicit Neural Representations
https://github.com/minhnhatvt/label-distribution-learning-fer-tf
공식 코드Uncertainty-aware Label Distribution Learning for Facial Expression Recognition
https://github.com/minivision-ai/photo2cartoon
공식 코드Few-shot Knowledge Transfer for Fine-grained Cartoon Face Generation
https://github.com/mintelligence-group/visiophysioenet
공식 코드VisioPhysioENet: Multimodal Engagement Detection using Visual and Physiological Signals
https://github.com/miraflow/DistilDIRE
공식 코드DistilDIRE: A Small, Fast, Cheap and Lightweight Diffusion Synthesized Deepfake Detection
https://github.com/mireillefares/vaanimation
공식 코드Transformer Network for Semantically-Aware and Speech-Driven Upper-Face Generation
https://github.com/mit-ll/lorax_cil
공식 코드LoRAX: LoRA eXpandable Networks for Continual Synthetic Image Attribution
https://github.com/mittalgovind/GOTCHA-Deepfakes
공식 코드GOTCHA: Real-Time Video Deepfake Detection via Challenge-Response
https://github.com/miu200521358/NeuralVoicePuppetryMMD
공식 코드https://github.com/mk-minchul/adaface
공식 코드https://github.com/mk-minchul/caface
공식 코드Cluster and Aggregate: Face Recognition with Large Probe Set
https://github.com/mk-minchul/dcface
공식 코드DCFace: Synthetic Face Generation with Dual Condition Diffusion Model
https://github.com/mk-minchul/kprpe
공식 코드https://github.com/mkocaoglu/CausalGAN
공식 코드CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
https://github.com/mobiface/mobiface.github.io
공식 코드MobiFace: A Novel Dataset for Mobile Face Tracking in the Wild
https://github.com/modar7/the_fairness_stitch
공식 코드The Fairness Stitch: Unveiling the Potential of Model Stitching in Neural Network De-Biasing
https://github.com/modelscope/facechain
공식 코드TopoFR: A Closer Look at Topology Alignment on Face Recognition
https://github.com/mohammadrezanaderi4/sfi-swin
공식 코드https://github.com/monu-khicher-1/multi-stage-learning
공식 코드https://github.com/mosamdabhi/3dlfm
공식 코드https://github.com/mouse1998mouse/S-xception
공식 코드Improved Xception with Dual Attention Mechanism and Feature Fusion for Face Forgery Detection
https://github.com/mrkoujan/DeepFaceFlow
공식 코드https://github.com/mrkoujan/FER
공식 코드Real-time Facial Expression Recognition "In The Wild'' by Disentangling 3D Expression from Identity
https://github.com/msa-lmc/s2d
공식 코드https://github.com/msed-ebrahimi/aroface
공식 코드ARoFace: Alignment Robustness to Improve Low-Quality Face Recognition
https://github.com/mshaikh2/HDL_Forensics
공식 코드https://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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.