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/sstzal/DFRF
공식 코드Learning Dynamic Facial Radiance Fields for Few-Shot Talking Head Synthesis
https://github.com/sstzal/DiffTalk
공식 코드DiffTalk: Crafting Diffusion Models for Generalized Audio-Driven Portraits Animation
https://github.com/starhiking/ExpLLM-TMM
공식 코드ExpLLM: Towards Chain of Thought for Facial Expression Recognition
https://github.com/starhiking/HeatmapInHeatmap
공식 코드HIH: Towards More Accurate Face Alignment via Heatmap in Heatmap
https://github.com/stat-ml/face-evaluation
공식 코드https://github.com/stefhoer/PartialLFW
공식 코드https://github.com/stephenivy07/Landmarkgan
공식 코드https://github.com/stevensmith2000/btnet
공식 코드Learning Unified Representations for Multi-Resolution Face Recognition
https://github.com/stevexwu/face_brightness
공식 코드Face Recognition Accuracy Across Demographics: Shining a Light Into the Problem
https://github.com/storymy/take-off-eyeglasses
공식 코드Portrait Eyeglasses and Shadow Removal by Leveraging 3D Synthetic Data
https://github.com/sudban3089/ID-Preserving-Facial-Aging
공식 코드Identity-Preserving Aging of Face Images via Latent Diffusion Models
https://github.com/sungho-coolg/fscl
공식 코드Fair Contrastive Learning for Facial Attribute Classification
https://github.com/sunwei925/dn-piqa
공식 코드https://github.com/sunyiyou/safas
공식 코드Rethinking Domain Generalization for Face Anti-spoofing: Separability and Alignment
https://github.com/superhero-7/DreamID
공식 코드DreamID: High-Fidelity and Fast diffusion-based Face Swapping via Triplet ID Group Learning
https://github.com/susanqq/Talking_Face_Generation
공식 코드Talking Face Generation by Conditional Recurrent Adversarial Network
https://github.com/sutd-visual-computing-group/Re-thinking_MI
공식 코드Re-thinking Model Inversion Attacks Against Deep Neural Networks
https://github.com/suzhigangssz/avih
공식 코드Hiding Visual Information via Obfuscating Adversarial Perturbations
https://github.com/svddchallenge/ctrsvdd2024_baseline
공식 코드SVDD Challenge 2024: A Singing Voice Deepfake Detection Challenge Evaluation Plan
https://github.com/swjtu-3dvision/boundaryface
공식 코드BoundaryFace: A mining framework with noise label self-correction for Face Recognition
https://github.com/swjtuma/fer-yolo-mamba
공식 코드FER-YOLO-Mamba: Facial Expression Detection and Classification Based on Selective State Space
https://github.com/swordcheng/FCSR-GAN
공식 코드FCSR-GAN: Joint Face Completion and Super-resolution via Multi-task Learning
https://github.com/sxjdwang/talklip
공식 코드Seeing What You Said: Talking Face Generation Guided by a Lip Reading Expert
https://github.com/sxl142/tex-face
공식 코드Controllable 3D Face Generation with Conditional Style Code Diffusion
https://github.com/szlbiubiubiu/CemiFace
공식 코드CemiFace: Center-based Semi-hard Synthetic Face Generation for Face Recognition
https://github.com/szlbiubiubiu/Part_fViT
공식 코드https://github.com/szlbiubiubiu/lafs_cvpr2024
공식 코드LAFS: Landmark-based Facial Self-supervised Learning for Face Recognition
https://github.com/taco-group/hfmf
공식 코드HFMF: Hierarchical Fusion Meets Multi-Stream Models for Deepfake Detection
https://github.com/tae898/age-gender
공식 코드Generalizing MLPs With Dropouts, Batch Normalization, and Skip Connections
https://github.com/takihasan/arbex
공식 코드ARBEx: Attentive Feature Extraction with Reliability Balancing for Robust Facial Expression Learning
https://github.com/tal-ai/RECLE
공식 코드Representation Learning from Limited Educational Data with Crowdsourced Labels
https://github.com/taldatech/deep-latent-particles-pytorch
공식 코드Unsupervised Image Representation Learning with Deep Latent Particles
https://github.com/talented-q/ifer-master
공식 코드More comprehensive facial inversion for more effective expression recognition
https://github.com/talented-q/poster_v2
공식 코드POSTER++: A simpler and stronger facial expression recognition network
https://github.com/talkingnow/HM-Conformer
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.