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/YudongGuo/AD-NeRF
공식 코드AD-NeRF: Audio Driven Neural Radiance Fields for Talking Head Synthesis
https://github.com/ZhaoJ9014/High_Performance_Face_Recognition
공식 코드https://github.com/ZiqiaoPeng/SyncTalk
공식 코드SyncTalk: The Devil is in the Synchronization for Talking Head Synthesis
https://github.com/abhinavdhall/deepfake
공식 코드Not made for each other- Audio-Visual Dissonance-based Deepfake Detection and Localization
https://github.com/afm215/FaVGen
공식 코드Fairer Analysis and Demographically Balanced Face Generation for Fairer Face Verification
https://github.com/albertpumarola/GANimation
공식 코드GANimation: Anatomically-aware Facial Animation from a Single Image
https://github.com/ali-vilab/dreamtalk
공식 코드DreamTalk: When Emotional Talking Head Generation Meets Diffusion Probabilistic Models
https://github.com/aliprf/ganalyzer
공식 코드GANalyzer: Analysis and Manipulation of GANs Latent Space for Controllable Face Synthesis
https://github.com/antonibigata/Laughing-Matters
공식 코드Laughing Matters: Introducing Laughing-Face Generation using Diffusion Models
https://github.com/apchenstu/Facial_Details_Synthesis
공식 코드https://github.com/architect-road/icongan
공식 코드Design What You Desire: Icon Generation from Orthogonal Application and Theme Labels
https://github.com/aria-zhangjl/e3-facenet
공식 코드https://github.com/aryan-garg/styledcgan
공식 코드https://github.com/azadlab/FExGAN
공식 코드https://github.com/azadlab/FExGAN-Meta
공식 코드https://github.com/barisgecer/TBGAN
공식 코드Synthesizing Coupled 3D Face Modalities by Trunk-Branch Generative Adversarial Networks
https://github.com/barisgecer/facegan
공식 코드https://github.com/bing-li-ai/AniGAN
공식 코드AniGAN: Style-Guided Generative Adversarial Networks for Unsupervised Anime Face Generation
https://github.com/carolineec/EverybodyDanceNow
공식 코드https://github.com/celebv-hq/celebv-hq
공식 코드https://github.com/choyingw/Voice2Mesh
공식 코드Voice2Mesh: Cross-Modal 3D Face Model Generation from Voices
https://github.com/cleardusk/MeGlass
공식 코드https://github.com/csinva/matching-with-gans
공식 코드Matched sample selection with GANs for mitigating attribute confounding
https://github.com/csyxwei/FFWM
공식 코드https://github.com/cvi-szu/degstalk
공식 코드DEGSTalk: Decomposed Per-Embedding Gaussian Fields for Hair-Preserving Talking Face Synthesis
https://github.com/daizigege/Identity_hider
공식 코드Seeing is not Believing: An Identity Hider for Human Vision Privacy Protection
https://github.com/dariant/id-booth
공식 코드ID-Booth: Identity-consistent Face Generation with Diffusion Models
https://github.com/deepalianeja/CharacterLipSync2D
공식 코드https://github.com/deepbrainai-research/koeba
공식 코드https://github.com/deepinsight/insightface
공식 코드ArcFace: Additive Angular Margin Loss for Deep Face Recognition
https://github.com/deeplsd/merkel-podcast-corpus
공식 코드https://github.com/ditzley/joint-gestures-and-face
공식 코드Joint Co-Speech Gesture and Expressive Talking Face Generation using Diffusion with Adapters
https://github.com/duxiaodan/fast_text2stylegan
공식 코드Text-Free Learning of a Natural Language Interface for Pretrained Face Generators
https://github.com/eeskimez/emotalkingface
공식 코드Speech Driven Talking Face Generation from a Single Image and an Emotion Condition
https://github.com/eladrich/pixel2style2pixel
공식 코드Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.