https://github.com/Carlyx/DPE
공식 코드DPE: Disentanglement of Pose and Expression for General Video Portrait Editing
Papers with Code 아카이브
Face Generation
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Face generation is the task of generating (or interpolating) new faces from an existing dataset.
The state-of-the-art results for this task are located in the Image Generation parent.
<span style="color:grey; opacity: 0.6">( Image credit: Progressive Growing of GANs for Improved Quality, Stability, and Variation )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/ControlNet/MARLIN
공식 코드MARLIN: Masked Autoencoder for facial video Representation LearnINg
https://github.com/DetionDX/Attribute2Sketch2Face
공식 코드Face Synthesis from Visual Attributes via Sketch using Conditional VAEs and GANs
https://github.com/DetionDX/GP-GAN-Gender-Preserving-GAN-for-Synthesizing-Faces-from-Landmarks
공식 코드GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks
https://github.com/DetionDX/GP-GAN-GenderPreserving-GAN-for-Synthesizing-Faces-from-Landmarks
공식 코드GP-GAN: Gender Preserving GAN for Synthesizing Faces from Landmarks
https://github.com/Dianezzy/ParaLip
공식 코드https://github.com/ESanchezLozano/GANnotation
공식 코드Triple consistency loss for pairing distributions in GAN-based face synthesis
https://github.com/FaceOnLive/Face-Recognition-SDK-Android
공식 코드Cross-Age Contrastive Learning for Age-Invariant Face Recognition
https://github.com/FeiiYin/StyleHEAT
공식 코드StyleHEAT: One-Shot High-Resolution Editable Talking Face Generation via Pre-trained StyleGAN
https://github.com/Fictionarry/InsTaG
공식 코드InsTaG: Learning Personalized 3D Talking Head from Few-Second Video
https://github.com/FuxiVirtualHuman/Write-a-Speaker
공식 코드Write-a-speaker: Text-based Emotional and Rhythmic Talking-head Generation
https://github.com/Hangz-nju-cuhk/Talking-Face_PC-AVS
공식 코드Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation
https://github.com/Hzzone/MTLFace
공식 코드When Age-Invariant Face Recognition Meets Face Age Synthesis: A Multi-Task Learning Framework
https://github.com/InterDigitalInc/HRFAE
공식 코드https://github.com/JOY-MM/JoyGen
공식 코드JoyGen: Audio-Driven 3D Depth-Aware Talking-Face Video Editing
https://github.com/JStehouwer/FFD_CVPR2020
공식 코드https://github.com/Jia1018/GoHD
공식 코드https://github.com/JianZhang-chick/EMOdiffhead
공식 코드EMOdiffhead: Continuously Emotional Control in Talking Head Generation via Diffusion
https://github.com/JiejiangWu/FaceG2E
공식 코드https://github.com/KU-CVLAB/MoDiTalker
공식 코드MoDiTalker: Motion-Disentangled Diffusion Model for High-Fidelity Talking Head Generation
https://github.com/KUCognitiveInformaticsLab/PairwiseExpressionAnnotator
공식 코드HAPI: A Model for Learning Robot Facial Expressions from Human Preferences
https://github.com/LynnHo/EigenGAN-Tensorflow
공식 코드https://github.com/MRzzm/DINet
공식 코드DINet: Deformation Inpainting Network for Realistic Face Visually Dubbing on High Resolution Video
https://github.com/MSoumm/FaVFA
공식 코드Fairer Analysis and Demographically Balanced Face Generation for Fairer Face Verification
https://github.com/Meta-Portrait/MetaPortrait
공식 코드MetaPortrait: Identity-Preserving Talking Head Generation with Fast Personalized Adaptation
https://github.com/MunchkinChen/FADING
공식 코드https://github.com/NetEase-Media/ControlTalk
공식 코드Controllable Talking Face Generation by Implicit Facial Keypoints Editing
https://github.com/Nithin-GK/UniteandConquer
공식 코드Unite and Conquer: Plug & Play Multi-Modal Synthesis using Diffusion Models
https://github.com/Raul2718/FLUXSynID
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/SHI-Labs/StyleNAT
공식 코드https://github.com/TimoBolkart/voca
공식 코드https://github.com/Weizhi-Zhong/IP_LAP
공식 코드Identity-Preserving Talking Face Generation with Landmark and Appearance Priors
https://github.com/YangNaruto/FQ-GAN
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.