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Face Reenactment

Face Reenactment is an emerging conditional face synthesis task that aims at fulfilling two goals simultaneously: 1) transfer a source face shape to a target face; while 2) preserve the appearance and the identity of the target face.

Source: One-shot Face Reenactment

Papers

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Showing 26–50 of 68 papers

TitleStatusHype
StyleAvatar: Real-time Photo-realistic Portrait Avatar from a Single VideoCode2
Unsupervised Facial Performance Editing via Vector-Quantized StyleGAN Representations—0
High-fidelity Facial Avatar Reconstruction from Monocular Video with Generative Priors—0
Compressing Video Calls using Synthetic Talking Heads—0
Audio-Visual Face ReenactmentCode1
StyleMask: Disentangling the Style Space of StyleGAN2 for Neural Face ReenactmentCode1
3DFaceShop: Explicitly Controllable 3D-Aware Portrait GenerationCode2
One-Shot Face Reenactment on Megapixels—0
Thin-Plate Spline Motion Model for Image AnimationCode4
FSGANv2: Improved Subject Agnostic Face Swapping and Reenactment—0
Thinking the Fusion Strategy of Multi-reference Face Reenactment—0
Finding Directions in GAN's Latent Space for Neural Face ReenactmentCode1
Dual-Generator Face Reenactment—0
Initiative Defense against Facial ManipulationCode1
AI-generated characters for supporting personalized learning and well-beingCode1
AnimeCeleb: Large-Scale Animation CelebHeads Dataset for Head ReenactmentCode1
Fine-grained Identity Preserving Landmark Synthesis for Face Reenactment—0
Detection of GAN-synthesized street videos—0
UniFaceGAN: A Unified Framework for Temporally Consistent Facial Video Editing—0
Egocentric Videoconferencing—0
Pareidolia Face Reenactment—0
Everything's Talkin': Pareidolia Face ReenactmentCode1
LI-Net: Large-Pose Identity-Preserving Face Reenactment Network—0
Single Source One Shot Reenactment using Weighted motion From Paired Feature Points—0
KoDF: A Large-scale Korean DeepFake Detection Dataset—0
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